From 57c0440a735c0d990d5e260a8f3e99a62a5c74b1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 5 Jun 2019 01:12:22 -0700 Subject: [PATCH] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 251596458 --- tensorflow/go/op/wrappers.go | 57674 ++++++++++++++++----------------- 1 file changed, 28837 insertions(+), 28837 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index f5575d3ed2a..cf54f5c2207 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -38,61 +38,6 @@ func makeOutputList(op *tf.Operation, start int, output string) ([]tf.Output, in return list, start + size, nil } -// Generates fingerprint values. -// -// Generates fingerprint values of `data`. -// -// Fingerprint op considers the first dimension of `data` as the batch dimension, -// and `output[i]` contains the fingerprint value generated from contents in -// `data[i, ...]` for all `i`. -// -// Fingerprint op writes fingerprint values as byte arrays. For example, the -// default method `farmhash64` generates a 64-bit fingerprint value at a time. -// This 8-byte value is written out as an `uint8` array of size 8, in little-endian -// order. -// -// For example, suppose that `data` has data type `DT_INT32` and shape (2, 3, 4), -// and that the fingerprint method is `farmhash64`. In this case, the output shape -// is (2, 8), where 2 is the batch dimension size of `data`, and 8 is the size of -// each fingerprint value in bytes. `output[0, :]` is generated from 12 integers in -// `data[0, :, :]` and similarly `output[1, :]` is generated from other 12 integers -// in `data[1, :, :]`. -// -// Note that this op fingerprints the raw underlying buffer, and it does not -// fingerprint Tensor's metadata such as data type and/or shape. For example, the -// fingerprint values are invariant under reshapes and bitcasts as long as the -// batch dimension remain the same: -// -// ``` -// Fingerprint(data) == Fingerprint(Reshape(data, ...)) -// Fingerprint(data) == Fingerprint(Bitcast(data, ...)) -// ``` -// -// For string data, one should expect `Fingerprint(data) != -// Fingerprint(ReduceJoin(data))` in general. -// -// Arguments: -// data: Must have rank 1 or higher. -// method: Fingerprint method used by this op. Currently available method is -// `farmhash::fingerprint64`. -// -// Returns A two-dimensional `Tensor` of type `tf.uint8`. The first dimension equals to -// `data`'s first dimension, and the second dimension size depends on the -// fingerprint algorithm. -func Fingerprint(scope *Scope, data tf.Output, method tf.Output) (fingerprint tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Fingerprint", - Input: []tf.Input{ - data, method, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel. type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr) @@ -208,6 +153,65 @@ func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs t return op.Output(0), op.Output(1), op.Output(2) } +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// +// and `max` to 'outputs' tensor of same shape as `inputs`. +// +// `[min; max]` define the clamping range for the `inputs` data. +// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` +// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and +// then de-quantized and output as floats in `[min; max]` interval. +// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// +// Before quantization, `min` and `max` values are adjusted with the following +// logic. +// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values, +// the behavior can be unexpected: +// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`. +// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`. +// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `, +// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVars", + Input: []tf.Input{ + inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // FakeQuantWithMinMaxArgsAttr is an optional argument to FakeQuantWithMinMaxArgs. type FakeQuantWithMinMaxArgsAttr func(optionalAttr) @@ -280,93 +284,6 @@ func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQua return op.Output(0) } -// Adds sparse `updates` to an existing tensor according to `indices`. -// -// This operation creates a new tensor by adding sparse `updates` to the passed -// in `tensor`. -// This operation is very similar to `tf.scatter_nd_add`, except that the updates -// are added onto an existing tensor (as opposed to a variable). If the memory -// for the existing tensor cannot be re-used, a copy is made and updated. -// -// `indices` is an integer tensor containing indices into a new tensor of shape -// `shape`. The last dimension of `indices` can be at most the rank of `shape`: -// -// indices.shape[-1] <= shape.rank -// -// The last dimension of `indices` corresponds to indices into elements -// (if `indices.shape[-1] = shape.rank`) or slices -// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of -// `shape`. `updates` is a tensor with shape -// -// indices.shape[:-1] + shape[indices.shape[-1]:] -// -// The simplest form of tensor_scatter_add is to add individual elements to a -// tensor by index. For example, say we want to add 4 elements in a rank-1 -// tensor with 8 elements. -// -// In Python, this scatter add operation would look like this: -// -// ```python -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// tensor = tf.ones([8], dtype=tf.int32) -// updated = tf.tensor_scatter_add(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [1, 12, 1, 11, 10, 1, 1, 13] -// -// We can also, insert entire slices of a higher rank tensor all at once. For -// example, if we wanted to insert two slices in the first dimension of a -// rank-3 tensor with two matrices of new values. -// -// In Python, this scatter add operation would look like this: -// -// ```python -// indices = tf.constant([[0], [2]]) -// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]], -// [[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]]]) -// tensor = tf.ones([4, 4, 4]) -// updated = tf.tensor_scatter_add(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], -// [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] -// -// Note that on CPU, if an out of bound index is found, an error is returned. -// On GPU, if an out of bound index is found, the index is ignored. -// -// Arguments: -// tensor: Tensor to copy/update. -// indices: Index tensor. -// updates: Updates to scatter into output. -// -// Returns A new tensor copied from tensor and updates added according to the indices. -func TensorScatterAdd(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorScatterAdd", - Input: []tf.Input{ - tensor, indices, updates, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // LowerBoundAttr is an optional argument to LowerBound. type LowerBoundAttr func(optionalAttr) @@ -425,70 +342,46 @@ func LowerBound(scope *Scope, sorted_inputs tf.Output, values tf.Output, optiona return op.Output(0) } -// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm. -type QuantizedInstanceNormAttr func(optionalAttr) +// UpperBoundAttr is an optional argument to UpperBound. +type UpperBoundAttr func(optionalAttr) -// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value. -// -// value: If True, `given_y_min` and `given_y_min` -// and `given_y_max` are used as the output range. Otherwise, -// the implementation computes the output range. -// If not specified, defaults to false -func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr { +// UpperBoundOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func UpperBoundOutType(value tf.DataType) UpperBoundAttr { return func(m optionalAttr) { - m["output_range_given"] = value + m["out_type"] = value } } -// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value. +// Applies upper_bound(sorted_search_values, values) along each row. // -// value: Output in `y_min` if `output_range_given` is True. -// If not specified, defaults to 0 -func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr { - return func(m optionalAttr) { - m["given_y_min"] = value - } -} - -// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value. +// Each set of rows with the same index in (sorted_inputs, values) is treated +// independently. The resulting row is the equivalent of calling +// `np.searchsorted(sorted_inputs, values, side='right')`. // -// value: Output in `y_max` if `output_range_given` is True. -// If not specified, defaults to 0 -func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr { - return func(m optionalAttr) { - m["given_y_max"] = value - } -} - -// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value. +// The result is not a global index to the entire +// `Tensor`, but rather just the index in the last dimension. // -// value: A small float number to avoid dividing by 0. -// If not specified, defaults to 1e-05 -func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr { - return func(m optionalAttr) { - m["variance_epsilon"] = value - } -} - -// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value. +// A 2-D example: +// sorted_sequence = [[0, 3, 9, 9, 10], +// [1, 2, 3, 4, 5]] +// values = [[2, 4, 9], +// [0, 2, 6]] // -// value: Minimum value of `y_max - y_min` -// If not specified, defaults to 0.001 -func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr { - return func(m optionalAttr) { - m["min_separation"] = value - } -} - -// Quantized Instance normalization. +// result = UpperBound(sorted_sequence, values) +// +// result == [[1, 2, 4], +// [0, 2, 5]] // // Arguments: -// x: A 4D input Tensor. -// x_min: The value represented by the lowest quantized input. -// x_max: The value represented by the highest quantized input. +// sorted_inputs: 2-D Tensor where each row is ordered. +// values: 2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains +// the values that will be searched for in `sorted_search_values`. // -// Returns A 4D Tensor.The value represented by the lowest quantized output.The value represented by the highest quantized output. -func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) { +// Returns A `Tensor` with the same shape as `values`. It contains the last scalar index +// into the last dimension where values can be inserted without changing the +// ordered property. +func UpperBound(scope *Scope, sorted_inputs tf.Output, values tf.Output, optional ...UpperBoundAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -497,14 +390,14 @@ func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf. a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedInstanceNorm", + Type: "UpperBound", Input: []tf.Input{ - x, x_min, x_max, + sorted_inputs, values, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } // Concatenates quantized tensors along one dimension. @@ -644,6 +537,152 @@ func Dequantize(scope *Scope, input tf.Output, min_range tf.Output, max_range tf return op.Output(0) } +// QuantizeV2Attr is an optional argument to QuantizeV2. +type QuantizeV2Attr func(optionalAttr) + +// QuantizeV2Mode sets the optional mode attribute to value. +// If not specified, defaults to "MIN_COMBINED" +func QuantizeV2Mode(value string) QuantizeV2Attr { + return func(m optionalAttr) { + m["mode"] = value + } +} + +// QuantizeV2RoundMode sets the optional round_mode attribute to value. +// If not specified, defaults to "HALF_AWAY_FROM_ZERO" +func QuantizeV2RoundMode(value string) QuantizeV2Attr { + return func(m optionalAttr) { + m["round_mode"] = value + } +} + +// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'. +// +// [min_range, max_range] are scalar floats that specify the range for +// the 'input' data. The 'mode' attribute controls exactly which calculations are +// used to convert the float values to their quantized equivalents. The +// 'round_mode' attribute controls which rounding tie-breaking algorithm is used +// when rounding float values to their quantized equivalents. +// +// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: +// +// ``` +// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) +// if T == qint8: out[i] -= (range(T) + 1) / 2.0 +// ``` +// +// here `range(T) = numeric_limits::max() - numeric_limits::min()` +// +// *MIN_COMBINED Mode Example* +// +// Assume the input is type float and has a possible range of [0.0, 6.0] and the +// output type is quint8 ([0, 255]). The min_range and max_range values should be +// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each +// value of the input by 255/6 and cast to quint8. +// +// If the output type was qint8 ([-128, 127]), the operation will additionally +// subtract each value by 128 prior to casting, so that the range of values aligns +// with the range of qint8. +// +// If the mode is 'MIN_FIRST', then this approach is used: +// +// ``` +// num_discrete_values = 1 << (# of bits in T) +// range_adjust = num_discrete_values / (num_discrete_values - 1) +// range = (range_max - range_min) * range_adjust +// range_scale = num_discrete_values / range +// quantized = round(input * range_scale) - round(range_min * range_scale) + +// numeric_limits::min() +// quantized = max(quantized, numeric_limits::min()) +// quantized = min(quantized, numeric_limits::max()) +// ``` +// +// The biggest difference between this and MIN_COMBINED is that the minimum range +// is rounded first, before it's subtracted from the rounded value. With +// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing +// and dequantizing will introduce a larger and larger error. +// +// *SCALED mode Example* +// +// `SCALED` mode matches the quantization approach used in +// `QuantizeAndDequantize{V2|V3}`. +// +// If the mode is `SCALED`, we do not use the full range of the output type, +// choosing to elide the lowest possible value for symmetry (e.g., output range is +// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to +// 0. +// +// We first find the range of values in our tensor. The +// range we use is always centered on 0, so we find m such that +// +// ```c++ +// m = max(abs(input_min), abs(input_max)) +// ``` +// +// Our input tensor range is then `[-m, m]`. +// +// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`. +// If T is signed, this is +// +// ``` +// num_bits = sizeof(T) * 8 +// [min_fixed, max_fixed] = +// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1] +// ``` +// +// Otherwise, if T is unsigned, the fixed-point range is +// +// ``` +// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1] +// ``` +// +// From this we compute our scaling factor, s: +// +// ```c++ +// s = (max_fixed - min_fixed) / (2 * m) +// ``` +// +// Now we can quantize the elements of our tensor: +// +// ```c++ +// result = round(input * s) +// ``` +// +// One thing to watch out for is that the operator may choose to adjust the +// requested minimum and maximum values slightly during the quantization process, +// so you should always use the output ports as the range for further calculations. +// For example, if the requested minimum and maximum values are close to equal, +// they will be separated by a small epsilon value to prevent ill-formed quantized +// buffers from being created. Otherwise, you can end up with buffers where all the +// quantized values map to the same float value, which causes problems for +// operations that have to perform further calculations on them. +// +// Arguments: +// +// min_range: The minimum scalar value possibly produced for the input. +// max_range: The maximum scalar value possibly produced for the input. +// +// +// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output. +func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeV2", + Input: []tf.Input{ + input, min_range, max_range, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // QuantizeAndDequantizeV3Attr is an optional argument to QuantizeAndDequantizeV3. type QuantizeAndDequantizeV3Attr func(optionalAttr) @@ -839,6 +878,43 @@ func QuantizeAndDequantizeV2(scope *Scope, input tf.Output, input_min tf.Output, return op.Output(0) } +// Extract `patches` from `input` and put them in the "depth" output dimension. 3D extension of `extract_image_patches`. +// +// Arguments: +// input: 5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`. +// ksizes: The size of the sliding window for each dimension of `input`. +// strides: 1-D of length 5. How far the centers of two consecutive patches are in +// `input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`. +// padding: The type of padding algorithm to use. +// +// We specify the size-related attributes as: +// +// ```python +// ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1] +// strides = [1, stride_planes, strides_rows, strides_cols, 1] +// ``` +// +// Returns 5-D Tensor with shape `[batch, out_planes, out_rows, out_cols, +// ksize_planes * ksize_rows * ksize_cols * depth]` containing patches +// with size `ksize_planes x ksize_rows x ksize_cols x depth` vectorized +// in the "depth" dimension. Note `out_planes`, `out_rows` and `out_cols` +// are the dimensions of the output patches. +func ExtractVolumePatches(scope *Scope, input tf.Output, ksizes []int64, strides []int64, padding string) (patches tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "ExtractVolumePatches", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // DepthToSpaceAttr is an optional argument to DepthToSpace. type DepthToSpaceAttr func(optionalAttr) @@ -964,104 +1040,118 @@ func DepthToSpace(scope *Scope, input tf.Output, block_size int64, optional ...D return op.Output(0) } -// BatchToSpace for 4-D tensors of type T. +// SpaceToDepthAttr is an optional argument to SpaceToDepth. +type SpaceToDepthAttr func(optionalAttr) + +// SpaceToDepthDataFormat sets the optional data_format attribute to value. +// If not specified, defaults to "NHWC" +func SpaceToDepthDataFormat(value string) SpaceToDepthAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// SpaceToDepth for tensors of type T. // -// This is a legacy version of the more general BatchToSpaceND. +// Rearranges blocks of spatial data, into depth. More specifically, +// this op outputs a copy of the input tensor where values from the `height` +// and `width` dimensions are moved to the `depth` dimension. +// The attr `block_size` indicates the input block size. // -// Rearranges (permutes) data from batch into blocks of spatial data, followed by -// cropping. This is the reverse transformation of SpaceToBatch. More specifically, -// this op outputs a copy of the input tensor where values from the `batch` -// dimension are moved in spatial blocks to the `height` and `width` dimensions, -// followed by cropping along the `height` and `width` dimensions. +// * Non-overlapping blocks of size `block_size x block size` are rearranged +// into depth at each location. +// * The depth of the output tensor is `block_size * block_size * input_depth`. +// * The Y, X coordinates within each block of the input become the high order +// component of the output channel index. +// * The input tensor's height and width must be divisible by block_size. // -// Arguments: -// input: 4-D tensor with shape -// `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size, -// depth]`. Note that the batch size of the input tensor must be divisible by -// `block_size * block_size`. -// crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies -// how many elements to crop from the intermediate result across the spatial -// dimensions as follows: +// The `data_format` attr specifies the layout of the input and output tensors +// with the following options: +// "NHWC": `[ batch, height, width, channels ]` +// "NCHW": `[ batch, channels, height, width ]` +// "NCHW_VECT_C": +// `qint8 [ batch, channels / 4, height, width, 4 ]` // -// crops = [[crop_top, crop_bottom], [crop_left, crop_right]] +// It is useful to consider the operation as transforming a 6-D Tensor. +// e.g. for data_format = NHWC, +// Each element in the input tensor can be specified via 6 coordinates, +// ordered by decreasing memory layout significance as: +// n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates +// within the output image, bX, bY means coordinates +// within the input block, iC means input channels). +// The output would be a transpose to the following layout: +// n,oY,oX,bY,bX,iC // +// This operation is useful for resizing the activations between convolutions +// (but keeping all data), e.g. instead of pooling. It is also useful for training +// purely convolutional models. // -// Returns 4-D with shape `[batch, height, width, depth]`, where: -// -// height = height_pad - crop_top - crop_bottom -// width = width_pad - crop_left - crop_right -// -// The attr `block_size` must be greater than one. It indicates the block size. -// -// Some examples: -// -// (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2: +// For example, given an input of shape `[1, 2, 2, 1]`, data_format = "NHWC" and +// block_size = 2: // // ``` -// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// x = [[[[1], [2]], +// [[3], [4]]]] // ``` // -// The output tensor has shape `[1, 2, 2, 1]` and value: +// This operation will output a tensor of shape `[1, 1, 1, 4]`: // // ``` -// x = [[[[1], [2]], [[3], [4]]]] +// [[[[1, 2, 3, 4]]]] // ``` // -// (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2: +// Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`, +// the corresponding output will have a single element (i.e. width and height are +// both 1) and will have a depth of 4 channels (1 * block_size * block_size). +// The output element shape is `[1, 1, 4]`. // -// ``` -// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] -// ``` -// -// The output tensor has shape `[1, 2, 2, 3]` and value: +// For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g. // // ``` // x = [[[[1, 2, 3], [4, 5, 6]], // [[7, 8, 9], [10, 11, 12]]]] // ``` // -// (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2: +// This operation, for block_size of 2, will return the following tensor of shape +// `[1, 1, 1, 12]` // // ``` -// x = [[[[1], [3]], [[9], [11]]], -// [[[2], [4]], [[10], [12]]], -// [[[5], [7]], [[13], [15]]], -// [[[6], [8]], [[14], [16]]]] +// [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] // ``` // -// The output tensor has shape `[1, 4, 4, 1]` and value: +// Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2: // // ``` -// x = [[[[1], [2], [3], [4]], -// [[5], [6], [7], [8]], -// [[9], [10], [11], [12]], -// [[13], [14], [15], [16]]]] +// x = [[[[1], [2], [5], [6]], +// [[3], [4], [7], [8]], +// [[9], [10], [13], [14]], +// [[11], [12], [15], [16]]]] // ``` // -// (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2: +// the operator will return the following tensor of shape `[1 2 2 4]`: // // ``` -// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], -// [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] +// x = [[[[1, 2, 3, 4], +// [5, 6, 7, 8]], +// [[9, 10, 11, 12], +// [13, 14, 15, 16]]]] // ``` // -// The output tensor has shape `[2, 2, 4, 1]` and value: +// Arguments: // -// ``` -// x = [[[[1], [3]], [[5], [7]]], -// [[[2], [4]], [[10], [12]]], -// [[[5], [7]], [[13], [15]]], -// [[[6], [8]], [[14], [16]]]] -// ``` -func BatchToSpace(scope *Scope, input tf.Output, crops tf.Output, block_size int64) (output tf.Output) { +// block_size: The size of the spatial block. +func SpaceToDepth(scope *Scope, input tf.Output, block_size int64, optional ...SpaceToDepthAttr) (output tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"block_size": block_size} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "BatchToSpace", + Type: "SpaceToDepth", Input: []tf.Input{ - input, crops, + input, }, Attrs: attrs, } @@ -1319,6 +1409,143 @@ func SpaceToBatch(scope *Scope, input tf.Output, paddings tf.Output, block_size return op.Output(0) } +// SpaceToBatch for N-D tensors of type T. +// +// This operation divides "spatial" dimensions `[1, ..., M]` of the input into a +// grid of blocks of shape `block_shape`, and interleaves these blocks with the +// "batch" dimension (0) such that in the output, the spatial dimensions +// `[1, ..., M]` correspond to the position within the grid, and the batch +// dimension combines both the position within a spatial block and the original +// batch position. Prior to division into blocks, the spatial dimensions of the +// input are optionally zero padded according to `paddings`. See below for a +// precise description. +// +// Arguments: +// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, +// where spatial_shape has `M` dimensions. +// block_shape: 1-D with shape `[M]`, all values must be >= 1. +// paddings: 2-D with shape `[M, 2]`, all values must be >= 0. +// `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension +// `i + 1`, which corresponds to spatial dimension `i`. It is required that +// `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`. +// +// This operation is equivalent to the following steps: +// +// 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the +// input according to `paddings` to produce `padded` of shape `padded_shape`. +// +// 2. Reshape `padded` to `reshaped_padded` of shape: +// +// [batch] + +// [padded_shape[1] / block_shape[0], +// block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1], +// block_shape[M-1]] + +// remaining_shape +// +// 3. Permute dimensions of `reshaped_padded` to produce +// `permuted_reshaped_padded` of shape: +// +// block_shape + +// [batch] + +// [padded_shape[1] / block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1]] + +// remaining_shape +// +// 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch +// dimension, producing an output tensor of shape: +// +// [batch * prod(block_shape)] + +// [padded_shape[1] / block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1]] + +// remaining_shape +// +// Some examples: +// +// (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 1]` and value: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 3]` and value: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[4, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and +// paddings = `[[0, 0], [2, 0]]`: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]]], +// [[[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[8, 1, 3, 1]` and value: +// +// ``` +// x = [[[[0], [1], [3]]], [[[0], [9], [11]]], +// [[[0], [2], [4]]], [[[0], [10], [12]]], +// [[[0], [5], [7]]], [[[0], [13], [15]]], +// [[[0], [6], [8]]], [[[0], [14], [16]]]] +// ``` +// +// Among others, this operation is useful for reducing atrous convolution into +// regular convolution. +func SpaceToBatchND(scope *Scope, input tf.Output, block_shape tf.Output, paddings tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SpaceToBatchND", + Input: []tf.Input{ + input, block_shape, paddings, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ListDiffAttr is an optional argument to ListDiff. type ListDiffAttr func(optionalAttr) @@ -1378,6 +1605,61 @@ func ListDiff(scope *Scope, x tf.Output, y tf.Output, optional ...ListDiffAttr) return op.Output(0), op.Output(1) } +// Inserts a dimension of 1 into a tensor's shape. +// +// Given a tensor `input`, this operation inserts a dimension of 1 at the +// dimension index `axis` of `input`'s shape. The dimension index `axis` starts at +// zero; if you specify a negative number for `axis` it is counted backward from +// the end. +// +// This operation is useful if you want to add a batch dimension to a single +// element. For example, if you have a single image of shape `[height, width, +// channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, +// which will make the shape `[1, height, width, channels]`. +// +// Other examples: +// +// ``` +// # 't' is a tensor of shape [2] +// shape(expand_dims(t, 0)) ==> [1, 2] +// shape(expand_dims(t, 1)) ==> [2, 1] +// shape(expand_dims(t, -1)) ==> [2, 1] +// +// # 't2' is a tensor of shape [2, 3, 5] +// shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] +// shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] +// shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] +// ``` +// +// This operation requires that: +// +// `-1-input.dims() <= dim <= input.dims()` +// +// This operation is related to `squeeze()`, which removes dimensions of +// size 1. +// +// Arguments: +// +// axis: 0-D (scalar). Specifies the dimension index at which to +// expand the shape of `input`. Must be in the range +// `[-rank(input) - 1, rank(input)]`. +// +// Returns Contains the same data as `input`, but its shape has an additional +// dimension of size 1 added. +func ExpandDims(scope *Scope, input tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExpandDims", + Input: []tf.Input{ + input, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // A placeholder op that passes through `input` when its output is not fed. // // Arguments: @@ -1429,42 +1711,41 @@ func PlaceholderV2(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.O return op.Output(0) } -// PlaceholderAttr is an optional argument to Placeholder. -type PlaceholderAttr func(optionalAttr) - -// PlaceholderShape sets the optional shape attribute to value. +// Pads a tensor. // -// value: (Optional) The shape of the tensor. If the shape has 0 dimensions, the -// shape is unconstrained. -// If not specified, defaults to -func PlaceholderShape(value tf.Shape) PlaceholderAttr { - return func(m optionalAttr) { - m["shape"] = value - } -} - -// A placeholder op for a value that will be fed into the computation. +// This operation pads `input` according to the `paddings` and `constant_values` +// you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is +// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many padding values to add before the contents of `input` in that dimension, +// and `paddings[D, 1]` indicates how many padding values to add after the contents +// of `input` in that dimension. `constant_values` is a scalar tensor of the same +// type as `input` that indicates the value to use for padding `input`. // -// N.B. This operation will fail with an error if it is executed. It is -// intended as a way to represent a value that will always be fed, and to -// provide attrs that enable the fed value to be checked at runtime. +// The padded size of each dimension D of the output is: // -// Arguments: -// dtype: The type of elements in the tensor. +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` // -// Returns A placeholder tensor that must be replaced using the feed mechanism. -func Placeholder(scope *Scope, dtype tf.DataType, optional ...PlaceholderAttr) (output tf.Output) { +// For example: +// +// ``` +// # 't' is [[1, 1], [2, 2]] +// # 'paddings' is [[1, 1], [2, 2]] +// # 'constant_values' is 0 +// # rank of 't' is 2 +// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] +// [0, 0, 1, 1, 0, 0] +// [0, 0, 2, 2, 0, 0] +// [0, 0, 0, 0, 0, 0]] +// ``` +func PadV2(scope *Scope, input tf.Output, paddings tf.Output, constant_values tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "Placeholder", - - Attrs: attrs, + Type: "PadV2", + Input: []tf.Input{ + input, paddings, constant_values, + }, } op := scope.AddOperation(opspec) return op.Output(0) @@ -2005,162 +2286,23 @@ func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output return op.Output(0) } -// Returns the rank of a tensor. +// Ensures that the tensor's shape matches the expected shape. // -// This operation returns an integer representing the rank of `input`. -// -// For example: -// -// ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// # shape of tensor 't' is [2, 2, 3] -// rank(t) ==> 3 -// ``` -// -// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank -// of a tensor is the number of indices required to uniquely select each element -// of the tensor. Rank is also known as "order", "degree", or "ndims." -func Rank(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Rank", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ReverseSequenceAttr is an optional argument to ReverseSequence. -type ReverseSequenceAttr func(optionalAttr) - -// ReverseSequenceBatchDim sets the optional batch_dim attribute to value. -// -// value: The dimension along which reversal is performed. -// If not specified, defaults to 0 -func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr { - return func(m optionalAttr) { - m["batch_dim"] = value - } -} - -// Reverses variable length slices. -// -// This op first slices `input` along the dimension `batch_dim`, and for each -// slice `i`, reverses the first `seq_lengths[i]` elements along -// the dimension `seq_dim`. -// -// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`, -// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`. -// -// The output slice `i` along dimension `batch_dim` is then given by input -// slice `i`, with the first `seq_lengths[i]` slices along dimension -// `seq_dim` reversed. -// -// For example: -// -// ``` -// # Given this: -// batch_dim = 0 -// seq_dim = 1 -// input.dims = (4, 8, ...) -// seq_lengths = [7, 2, 3, 5] -// -// # then slices of input are reversed on seq_dim, but only up to seq_lengths: -// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] -// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] -// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] -// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] -// -// # while entries past seq_lens are copied through: -// output[0, 7:, :, ...] = input[0, 7:, :, ...] -// output[1, 2:, :, ...] = input[1, 2:, :, ...] -// output[2, 3:, :, ...] = input[2, 3:, :, ...] -// output[3, 2:, :, ...] = input[3, 2:, :, ...] -// ``` -// -// In contrast, if: -// -// ``` -// # Given this: -// batch_dim = 2 -// seq_dim = 0 -// input.dims = (8, ?, 4, ...) -// seq_lengths = [7, 2, 3, 5] -// -// # then slices of input are reversed on seq_dim, but only up to seq_lengths: -// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...] -// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...] -// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...] -// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...] -// -// # while entries past seq_lens are copied through: -// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...] -// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...] -// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...] -// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...] -// ``` +// Raises an error if the input tensor's shape does not match the specified shape. +// Returns the input tensor otherwise. // // Arguments: -// input: The input to reverse. -// seq_lengths: 1-D with length `input.dims(batch_dim)` and -// `max(seq_lengths) <= input.dims(seq_dim)` -// seq_dim: The dimension which is partially reversed. +// input: A tensor, whose shape is to be validated. +// shape: The expected (possibly partially specified) shape of the input tensor. // -// Returns The partially reversed input. It has the same shape as `input`. -func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) { +// Returns A tensor with the same shape and contents as the input tensor or value. +func EnsureShape(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"seq_dim": seq_dim} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"shape": shape} opspec := tf.OpSpec{ - Type: "ReverseSequence", - Input: []tf.Input{ - input, seq_lengths, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ShapeAttr is an optional argument to Shape. -type ShapeAttr func(optionalAttr) - -// ShapeOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func ShapeOutType(value tf.DataType) ShapeAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Returns the shape of a tensor. -// -// This operation returns a 1-D integer tensor representing the shape of `input`. -// -// For example: -// -// ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// shape(t) ==> [2, 2, 3] -// ``` -func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Shape", + Type: "EnsureShape", Input: []tf.Input{ input, }, @@ -2170,27 +2312,24 @@ func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Outp return op.Output(0) } -// UniqueWithCountsV2Attr is an optional argument to UniqueWithCountsV2. -type UniqueWithCountsV2Attr func(optionalAttr) +// UniqueWithCountsAttr is an optional argument to UniqueWithCounts. +type UniqueWithCountsAttr func(optionalAttr) -// UniqueWithCountsV2OutIdx sets the optional out_idx attribute to value. +// UniqueWithCountsOutIdx sets the optional out_idx attribute to value. // If not specified, defaults to DT_INT32 -func UniqueWithCountsV2OutIdx(value tf.DataType) UniqueWithCountsV2Attr { +func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr { return func(m optionalAttr) { m["out_idx"] = value } } -// Finds unique elements along an axis of a tensor. +// Finds unique elements in a 1-D tensor. // -// This operation either returns a tensor `y` containing unique elements -// along the `axis` of a tensor. The returned unique elements is sorted -// in the same order as they occur along `axis` in `x`. -// This operation also returns a tensor `idx` and a tensor `count` -// that are the same size as the number of the elements in `x` along the -// `axis` dimension. The `idx` contains the index in the unique output `y` -// and the `count` contains the count in the unique output `y`. -// In other words, for an `1-D` tensor `x` with `axis = None: +// This operation returns a tensor `y` containing all of the unique elements of `x` +// sorted in the same order that they occur in `x`. This operation also returns a +// tensor `idx` the same size as `x` that contains the index of each value of `x` +// in the unique output `y`. Finally, it returns a third tensor `count` that +// contains the count of each element of `y` in `x`. In other words: // // `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` // @@ -2204,41 +2343,11 @@ func UniqueWithCountsV2OutIdx(value tf.DataType) UniqueWithCountsV2Attr { // count ==> [2, 1, 3, 1, 2] // ``` // -// For an `2-D` tensor `x` with `axis = 0`: -// -// ``` -// # tensor 'x' is [[1, 0, 0], -// # [1, 0, 0], -// # [2, 0, 0]] -// y, idx, count = unique_with_counts(x, axis=0) -// y ==> [[1, 0, 0], -// [2, 0, 0]] -// idx ==> [0, 0, 1] -// count ==> [2, 1] -// ``` -// -// For an `2-D` tensor `x` with `axis = 1`: -// -// ``` -// # tensor 'x' is [[1, 0, 0], -// # [1, 0, 0], -// # [2, 0, 0]] -// y, idx, count = unique_with_counts(x, axis=1) -// y ==> [[1, 0], -// [1, 0], -// [2, 0]] -// idx ==> [0, 1, 1] -// count ==> [1, 2] -// ``` -// // Arguments: -// x: A `Tensor`. -// axis: A `Tensor` of type `int32` (default: None). The axis of the Tensor to -// find the unique elements. +// x: 1-D. // -// Returns A `Tensor`. Unique elements along the `axis` of `Tensor` x.A 1-D Tensor. Has the same type as x that contains the index of each -// value of x in the output y.A 1-D Tensor. The count of each value of x in the output y. -func UniqueWithCountsV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueWithCountsV2Attr) (y tf.Output, idx tf.Output, count tf.Output) { +// Returns 1-D.1-D.1-D. +func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) { if scope.Err() != nil { return } @@ -2247,9 +2356,9 @@ func UniqueWithCountsV2(scope *Scope, x tf.Output, axis tf.Output, optional ...U a(attrs) } opspec := tf.OpSpec{ - Type: "UniqueWithCountsV2", + Type: "UniqueWithCounts", Input: []tf.Input{ - x, axis, + x, }, Attrs: attrs, } @@ -2392,6 +2501,25 @@ func Unique(scope *Scope, x tf.Output, optional ...UniqueAttr) (y tf.Output, idx return op.Output(0), op.Output(1) } +// Shuffle dimensions of x according to a permutation and conjugate the result. +// +// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: +// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` +// `y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])` +func ConjugateTranspose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConjugateTranspose", + Input: []tf.Input{ + x, perm, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Shuffle dimensions of x according to a permutation. // // The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: @@ -2410,6 +2538,82 @@ func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { return op.Output(0) } +// Reshapes a tensor. +// +// Given `tensor`, this operation returns a tensor that has the same values +// as `tensor` with shape `shape`. +// +// If one component of `shape` is the special value -1, the size of that dimension +// is computed so that the total size remains constant. In particular, a `shape` +// of `[-1]` flattens into 1-D. At most one component of `shape` can be -1. +// +// If `shape` is 1-D or higher, then the operation returns a tensor with shape +// `shape` filled with the values of `tensor`. In this case, the number of elements +// implied by `shape` must be the same as the number of elements in `tensor`. +// +// For example: +// +// ``` +// # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] +// # tensor 't' has shape [9] +// reshape(t, [3, 3]) ==> [[1, 2, 3], +// [4, 5, 6], +// [7, 8, 9]] +// +// # tensor 't' is [[[1, 1], [2, 2]], +// # [[3, 3], [4, 4]]] +// # tensor 't' has shape [2, 2, 2] +// reshape(t, [2, 4]) ==> [[1, 1, 2, 2], +// [3, 3, 4, 4]] +// +// # tensor 't' is [[[1, 1, 1], +// # [2, 2, 2]], +// # [[3, 3, 3], +// # [4, 4, 4]], +// # [[5, 5, 5], +// # [6, 6, 6]]] +// # tensor 't' has shape [3, 2, 3] +// # pass '[-1]' to flatten 't' +// reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6] +// +// # -1 can also be used to infer the shape +// +// # -1 is inferred to be 9: +// reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], +// [4, 4, 4, 5, 5, 5, 6, 6, 6]] +// # -1 is inferred to be 2: +// reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], +// [4, 4, 4, 5, 5, 5, 6, 6, 6]] +// # -1 is inferred to be 3: +// reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1], +// [2, 2, 2], +// [3, 3, 3]], +// [[4, 4, 4], +// [5, 5, 5], +// [6, 6, 6]]] +// +// # tensor 't' is [7] +// # shape `[]` reshapes to a scalar +// reshape(t, []) ==> 7 +// ``` +// +// Arguments: +// +// shape: Defines the shape of the output tensor. +func Reshape(scope *Scope, tensor tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reshape", + Input: []tf.Input{ + tensor, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Stops gradient computation. // // When executed in a graph, this op outputs its input tensor as-is. @@ -2464,135 +2668,6 @@ func DebugGradientIdentity(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } -// OneHotAttr is an optional argument to OneHot. -type OneHotAttr func(optionalAttr) - -// OneHotAxis sets the optional axis attribute to value. -// -// value: The axis to fill (default: -1, a new inner-most axis). -// If not specified, defaults to -1 -func OneHotAxis(value int64) OneHotAttr { - return func(m optionalAttr) { - m["axis"] = value - } -} - -// Returns a one-hot tensor. -// -// The locations represented by indices in `indices` take value `on_value`, -// while all other locations take value `off_value`. -// -// If the input `indices` is rank `N`, the output will have rank `N+1`, -// The new axis is created at dimension `axis` (default: the new axis is -// appended at the end). -// -// If `indices` is a scalar the output shape will be a vector of length `depth`. -// -// If `indices` is a vector of length `features`, the output shape will be: -// ``` -// features x depth if axis == -1 -// depth x features if axis == 0 -// ``` -// -// If `indices` is a matrix (batch) with shape `[batch, features]`, -// the output shape will be: -// ``` -// batch x features x depth if axis == -1 -// batch x depth x features if axis == 1 -// depth x batch x features if axis == 0 -// ``` -// -// -// Examples -// ========= -// -// Suppose that -// ``` -// indices = [0, 2, -1, 1] -// depth = 3 -// on_value = 5.0 -// off_value = 0.0 -// axis = -1 -// ``` -// -// Then output is `[4 x 3]`: -// ``` -// output = -// [5.0 0.0 0.0] // one_hot(0) -// [0.0 0.0 5.0] // one_hot(2) -// [0.0 0.0 0.0] // one_hot(-1) -// [0.0 5.0 0.0] // one_hot(1) -// ``` -// -// Suppose that -// ``` -// indices = [0, 2, -1, 1] -// depth = 3 -// on_value = 0.0 -// off_value = 3.0 -// axis = 0 -// ``` -// -// Then output is `[3 x 4]`: -// ``` -// output = -// [0.0 3.0 3.0 3.0] -// [3.0 3.0 3.0 0.0] -// [3.0 3.0 3.0 3.0] -// [3.0 0.0 3.0 3.0] -// // ^ one_hot(0) -// // ^ one_hot(2) -// // ^ one_hot(-1) -// // ^ one_hot(1) -// ``` -// -// Suppose that -// ``` -// indices = [[0, 2], [1, -1]] -// depth = 3 -// on_value = 1.0 -// off_value = 0.0 -// axis = -1 -// ``` -// -// Then output is `[2 x 2 x 3]`: -// ``` -// output = -// [ -// [1.0, 0.0, 0.0] // one_hot(0) -// [0.0, 0.0, 1.0] // one_hot(2) -// ][ -// [0.0, 1.0, 0.0] // one_hot(1) -// [0.0, 0.0, 0.0] // one_hot(-1) -// ] -// ``` -// -// Arguments: -// indices: A tensor of indices. -// depth: A scalar defining the depth of the one hot dimension. -// on_value: A scalar defining the value to fill in output when `indices[j] = i`. -// off_value: A scalar defining the value to fill in output when `indices[j] != i`. -// -// Returns The one-hot tensor. -func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OneHot", - Input: []tf.Input{ - indices, depth, on_value, off_value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Returns a list of tensors with the same shapes and contents as the input // // tensors. @@ -2648,45 +2723,57 @@ func Snapshot(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } -// GatherAttr is an optional argument to Gather. -type GatherAttr func(optionalAttr) +// GatherV2Attr is an optional argument to GatherV2. +type GatherV2Attr func(optionalAttr) -// GatherValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func GatherValidateIndices(value bool) GatherAttr { +// GatherV2BatchDims sets the optional batch_dims attribute to value. +// If not specified, defaults to 0 +func GatherV2BatchDims(value int64) GatherV2Attr { return func(m optionalAttr) { - m["validate_indices"] = value + m["batch_dims"] = value } } -// Gather slices from `params` according to `indices`. +// Gather slices from `params` axis `axis` according to `indices`. // // `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). -// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: +// Produces an output tensor with shape `params.shape[:axis] + indices.shape + +// params.shape[axis + 1:]` where: // // ```python -// # Scalar indices -// output[:, ..., :] = params[indices, :, ... :] +// # Scalar indices (output is rank(params) - 1). +// output[a_0, ..., a_n, b_0, ..., b_n] = +// params[a_0, ..., a_n, indices, b_0, ..., b_n] // -// # Vector indices -// output[i, :, ..., :] = params[indices[i], :, ... :] +// # Vector indices (output is rank(params)). +// output[a_0, ..., a_n, i, b_0, ..., b_n] = +// params[a_0, ..., a_n, indices[i], b_0, ..., b_n] // -// # Higher rank indices -// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// # Higher rank indices (output is rank(params) + rank(indices) - 1). +// output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = +// params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] // ``` // -// If `indices` is a permutation and `len(indices) == params.shape[0]` then -// this operation will permute `params` accordingly. -// -// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in -// `indices` are always validated to be within range. If assigned to GPU, -// out-of-bound indices result in safe but unspecified behavior, which may include -// raising an error. -// //
// //
-func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) { +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, a 0 is stored in the +// corresponding output value. +// +// See also `tf.batch_gather` and `tf.gather_nd`. +// +// Arguments: +// params: The tensor from which to gather values. Must be at least rank +// `axis + 1`. +// indices: Index tensor. Must be in range `[0, params.shape[axis])`. +// axis: The axis in `params` to gather `indices` from. Defaults to the first +// dimension. Supports negative indexes. +// +// Returns Values from `params` gathered from indices given by `indices`, with +// shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`. +func GatherV2(scope *Scope, params tf.Output, indices tf.Output, axis tf.Output, optional ...GatherV2Attr) (output tf.Output) { if scope.Err() != nil { return } @@ -2695,9 +2782,9 @@ func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...Gathe a(attrs) } opspec := tf.OpSpec{ - Type: "Gather", + Type: "GatherV2", Input: []tf.Input{ - params, indices, + params, indices, axis, }, Attrs: attrs, } @@ -2748,102 +2835,18 @@ func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) { return op.Output(0) } -// EditDistanceAttr is an optional argument to EditDistance. -type EditDistanceAttr func(optionalAttr) - -// EditDistanceNormalize sets the optional normalize attribute to value. -// -// value: boolean (if true, edit distances are normalized by length of truth). -// -// The output is: -// If not specified, defaults to true -func EditDistanceNormalize(value bool) EditDistanceAttr { - return func(m optionalAttr) { - m["normalize"] = value - } -} - -// Computes the (possibly normalized) Levenshtein Edit Distance. -// -// The inputs are variable-length sequences provided by SparseTensors -// (hypothesis_indices, hypothesis_values, hypothesis_shape) -// and -// (truth_indices, truth_values, truth_shape). -// -// The inputs are: -// -// Arguments: -// hypothesis_indices: The indices of the hypothesis list SparseTensor. -// This is an N x R int64 matrix. -// hypothesis_values: The values of the hypothesis list SparseTensor. -// This is an N-length vector. -// hypothesis_shape: The shape of the hypothesis list SparseTensor. -// This is an R-length vector. -// truth_indices: The indices of the truth list SparseTensor. -// This is an M x R int64 matrix. -// truth_values: The values of the truth list SparseTensor. -// This is an M-length vector. -// truth_shape: truth indices, vector. -// -// Returns A dense float tensor with rank R - 1. -// -// For the example input: -// -// // hypothesis represents a 2x1 matrix with variable-length values: -// // (0,0) = ["a"] -// // (1,0) = ["b"] -// hypothesis_indices = [[0, 0, 0], -// [1, 0, 0]] -// hypothesis_values = ["a", "b"] -// hypothesis_shape = [2, 1, 1] -// -// // truth represents a 2x2 matrix with variable-length values: -// // (0,0) = [] -// // (0,1) = ["a"] -// // (1,0) = ["b", "c"] -// // (1,1) = ["a"] -// truth_indices = [[0, 1, 0], -// [1, 0, 0], -// [1, 0, 1], -// [1, 1, 0]] -// truth_values = ["a", "b", "c", "a"] -// truth_shape = [2, 2, 2] -// normalize = true -// -// The output will be: -// -// // output is a 2x2 matrix with edit distances normalized by truth lengths. -// output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis -// [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis -func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EditDistance", - Input: []tf.Input{ - hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Reverses specific dimensions of a tensor. // -// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions -// of `tensor`, this operation reverses each dimension i of `tensor` where -// `dims[i]` is `True`. +// NOTE `tf.reverse` has now changed behavior in preparation for 1.0. +// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. // -// `tensor` can have up to 8 dimensions. The number of dimensions -// of `tensor` must equal the number of elements in `dims`. In other words: +// Given a `tensor`, and a `int32` tensor `axis` representing the set of +// dimensions of `tensor` to reverse. This operation reverses each dimension +// `i` for which there exists `j` s.t. `axis[j] == i`. // -// `rank(tensor) = size(dims)` +// `tensor` can have up to 8 dimensions. The number of dimensions specified +// in `axis` may be 0 or more entries. If an index is specified more than +// once, a InvalidArgument error is raised. // // For example: // @@ -2856,7 +2859,7 @@ func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values // # [20, 21, 22, 23]]]] // # tensor 't' shape is [1, 2, 3, 4] // -// # 'dims' is [False, False, False, True] +// # 'dims' is [3] or 'dims' is [-1] // reverse(t, dims) ==> [[[[ 3, 2, 1, 0], // [ 7, 6, 5, 4], // [ 11, 10, 9, 8]], @@ -2864,7 +2867,7 @@ func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values // [19, 18, 17, 16], // [23, 22, 21, 20]]]] // -// # 'dims' is [False, True, False, False] +// # 'dims' is '[1]' (or 'dims' is '[-3]') // reverse(t, dims) ==> [[[[12, 13, 14, 15], // [16, 17, 18, 19], // [20, 21, 22, 23] @@ -2872,7 +2875,7 @@ func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values // [ 4, 5, 6, 7], // [ 8, 9, 10, 11]]]] // -// # 'dims' is [False, False, True, False] +// # 'dims' is '[2]' (or 'dims' is '[-2]') // reverse(t, dims) ==> [[[[8, 9, 10, 11], // [4, 5, 6, 7], // [0, 1, 2, 3]] @@ -2883,17 +2886,68 @@ func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values // // Arguments: // tensor: Up to 8-D. -// dims: 1-D. The dimensions to reverse. +// axis: 1-D. The indices of the dimensions to reverse. Must be in the range +// `[-rank(tensor), rank(tensor))`. // // Returns The same shape as `tensor`. -func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) { +func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Reverse", + Type: "ReverseV2", Input: []tf.Input{ - tensor, dims, + tensor, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the batched diagonal part of a batched tensor. +// +// This operation returns a tensor with the `diagonal` part +// of the batched `input`. The `diagonal` part is computed as follows: +// +// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a +// tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where: +// +// `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`. +// +// The input must be at least a matrix. +// +// For example: +// +// ``` +// # 'input' is [[[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]], +// [[5, 0, 0, 0] +// [0, 6, 0, 0] +// [0, 0, 7, 0] +// [0, 0, 0, 8]]] +// +// and input.shape = (2, 4, 4) +// +// tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]] +// +// which has shape (2, 4) +// ``` +// +// Arguments: +// input: Rank `k` tensor where `k >= 2`. +// +// Returns The extracted diagonal(s) having shape +// `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`. +func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiagPart", + Input: []tf.Input{ + input, }, } op := scope.AddOperation(opspec) @@ -2975,48 +3029,6 @@ func Diag(scope *Scope, diagonal tf.Output) (output tf.Output) { return op.Output(0) } -// Returns a tensor of ones with the same shape and type as x. -// -// Arguments: -// x: a tensor of type T. -// -// Returns a tensor of the same shape and type as x but filled with ones. -func OnesLike(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OnesLike", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Gives a guarantee to the TF runtime that the input tensor is a constant. -// -// The runtime is then free to make optimizations based on this. -// -// Only accepts value typed tensors as inputs and rejects resource variable handles -// as input. -// -// Returns the input tensor without modification. -func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "GuaranteeConst", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Returns immutable tensor from memory region. // // The current implementation memmaps the tensor from a file. @@ -3059,34 +3071,29 @@ func HostConst(scope *Scope, value tf.Tensor, dtype tf.DataType) (output tf.Outp return op.Output(0) } -// Computes offsets of concat inputs within its output. -// -// For example: -// -// ``` -// # 'x' is [2, 2, 7] -// # 'y' is [2, 3, 7] -// # 'z' is [2, 5, 7] -// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] -// ``` -// -// This is typically used by gradient computations for a concat operation. +// Splits a tensor into `num_split` tensors along one dimension. // // Arguments: -// concat_dim: The dimension along which to concatenate. -// shape: The `N` int32 vectors representing shape of tensors being concatenated. +// axis: 0-D. The dimension along which to split. Must be in the range +// `[-rank(value), rank(value))`. +// value: The tensor to split. +// num_split: The number of ways to split. Must evenly divide +// `value.shape[split_dim]`. // -// Returns The `N` int32 vectors representing the starting offset -// of input tensors within the concatenated output. -func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) { +// Returns They are identically shaped tensors, whose shape matches that of `value` +// except along `axis`, where their sizes are +// `values.shape[split_dim] / num_split`. +func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"num_split": num_split} opspec := tf.OpSpec{ - Type: "ConcatOffset", + Type: "Split", Input: []tf.Input{ - concat_dim, tf.OutputList(shape), + axis, value, }, + Attrs: attrs, } op := scope.AddOperation(opspec) if scope.Err() != nil { @@ -3094,11 +3101,36 @@ func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset } var idx int var err error - if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil { - scope.UpdateErr("ConcatOffset", err) + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("Split", err) return } - return offset + return output +} + +// Concatenates tensors along one dimension. +// +// Arguments: +// values: List of `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// axis: 0-D. The dimension along which to concatenate. Must be in the +// range [-rank(values), rank(values)). +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatV2", + Input: []tf.Input{ + tf.OutputList(values), axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } // Concatenates tensors along one dimension. @@ -3126,6 +3158,47 @@ func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.O return op.Output(0) } +// Broadcast an array for a compatible shape. +// +// Broadcasting is the process of making arrays to have compatible shapes +// for arithmetic operations. Two shapes are compatible if for each +// dimension pair they are either equal or one of them is one. When trying +// to broadcast a Tensor to a shape, it starts with the trailing dimensions, +// and works its way forward. +// +// For example, +// +// ```python +// >>> x = tf.constant([1, 2, 3]) +// >>> y = tf.broadcast_to(x, [3, 3]) +// >>> sess.run(y) +// array([[1, 2, 3], +// [1, 2, 3], +// [1, 2, 3]], dtype=int32) +// ``` +// +// In the above example, the input Tensor with the shape of `[1, 3]` +// is broadcasted to output Tensor with shape of `[3, 3]`. +// +// Arguments: +// input: A Tensor to broadcast. +// shape: An 1-D `int` Tensor. The shape of the desired output. +// +// Returns A Tensor. +func BroadcastTo(scope *Scope, input tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastTo", + Input: []tf.Input{ + input, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // EmptyAttr is an optional argument to Empty. type EmptyAttr func(optionalAttr) @@ -3167,9 +3240,9 @@ func Empty(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...EmptyAt return op.Output(0) } -// Adds v into specified rows of x. +// Subtracts `v` into specified rows of `x`. // -// Computes y = x; y[i, :] += v; return y. +// Computes y = x; y[i, :] -= v; return y. // // Arguments: // x: A `Tensor` of type T. @@ -3177,12 +3250,36 @@ func Empty(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...EmptyAt // v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. // // Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. -func InplaceAdd(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { +func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "InplaceAdd", + Type: "InplaceSub", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Updates specified rows with values in `v`. +// +// Computes `x[i, :] = v; return x`. +// +// Arguments: +// x: A tensor of type `T`. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceUpdate", Input: []tf.Input{ x, i, v, }, @@ -3254,179 +3351,132 @@ func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf return op.Output(0) } -// MfccAttr is an optional argument to Mfcc. -type MfccAttr func(optionalAttr) +// BatchAttr is an optional argument to Batch. +type BatchAttr func(optionalAttr) -// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. -// -// value: The highest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 4000 -func MfccUpperFrequencyLimit(value float32) MfccAttr { +// BatchMaxEnqueuedBatches sets the optional max_enqueued_batches attribute to value. +// If not specified, defaults to 10 +func BatchMaxEnqueuedBatches(value int64) BatchAttr { return func(m optionalAttr) { - m["upper_frequency_limit"] = value + m["max_enqueued_batches"] = value } } -// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. -// -// value: The lowest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 20 -func MfccLowerFrequencyLimit(value float32) MfccAttr { +// BatchAllowedBatchSizes sets the optional allowed_batch_sizes attribute to value. +// If not specified, defaults to <> +func BatchAllowedBatchSizes(value []int64) BatchAttr { return func(m optionalAttr) { - m["lower_frequency_limit"] = value + m["allowed_batch_sizes"] = value } } -// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. -// -// value: Resolution of the Mel bank used internally. -// If not specified, defaults to 40 -func MfccFilterbankChannelCount(value int64) MfccAttr { - return func(m optionalAttr) { - m["filterbank_channel_count"] = value - } -} - -// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. -// -// value: How many output channels to produce per time slice. -// If not specified, defaults to 13 -func MfccDctCoefficientCount(value int64) MfccAttr { - return func(m optionalAttr) { - m["dct_coefficient_count"] = value - } -} - -// Transforms a spectrogram into a form that's useful for speech recognition. -// -// Mel Frequency Cepstral Coefficients are a way of representing audio data that's -// been effective as an input feature for machine learning. They are created by -// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the -// higher frequencies that are less significant to the human ear. They have a long -// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum -// is a good resource to learn more. -// -// Arguments: -// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared -// set to true. -// sample_rate: How many samples per second the source audio used. -func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Mfcc", - Input: []tf.Input{ - spectrogram, sample_rate, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UnbatchAttr is an optional argument to Unbatch. -type UnbatchAttr func(optionalAttr) - -// UnbatchContainer sets the optional container attribute to value. +// BatchContainer sets the optional container attribute to value. // If not specified, defaults to "" -func UnbatchContainer(value string) UnbatchAttr { +func BatchContainer(value string) BatchAttr { return func(m optionalAttr) { m["container"] = value } } -// UnbatchSharedName sets the optional shared_name attribute to value. +// BatchSharedName sets the optional shared_name attribute to value. // If not specified, defaults to "" -func UnbatchSharedName(value string) UnbatchAttr { +func BatchSharedName(value string) BatchAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Reverses the operation of Batch for a single output Tensor. +// BatchBatchingQueue sets the optional batching_queue attribute to value. +// If not specified, defaults to "" +func BatchBatchingQueue(value string) BatchAttr { + return func(m optionalAttr) { + m["batching_queue"] = value + } +} + +// Batches all input tensors nondeterministically. // -// An instance of Unbatch either receives an empty batched_tensor, in which case it -// asynchronously waits until the values become available from a concurrently -// running instance of Unbatch with the same container and shared_name, or receives -// a non-empty batched_tensor in which case it finalizes all other concurrently -// running instances and outputs its own element from the batch. +// When many instances of this Op are being run concurrently with the same +// container/shared_name in the same device, some will output zero-shaped Tensors +// and others will output Tensors of size up to max_batch_size. // -// batched_tensor: The possibly transformed output of Batch. The size of the first -// dimension should remain unchanged by the transformations for the operation to -// work. -// batch_index: The matching batch_index obtained from Batch. -// id: The id scalar emitted by Batch. -// unbatched_tensor: The Tensor corresponding to this execution. -// timeout_micros: Maximum amount of time (in microseconds) to wait to receive the -// batched input tensor associated with a given invocation of the op. -// container: Container to control resource sharing. -// shared_name: Instances of Unbatch with the same container and shared_name are -// assumed to possibly belong to the same batch. If left empty, the op name will -// be used as the shared name. -func Unbatch(scope *Scope, batched_tensor tf.Output, batch_index tf.Output, id tf.Output, timeout_micros int64, optional ...UnbatchAttr) (unbatched_tensor tf.Output) { +// All Tensors in in_tensors are batched together (so, for example, labels and +// features should be batched with a single instance of this operation. +// +// Each invocation of batch emits an `id` scalar which will be used to identify +// this particular invocation when doing unbatch or its gradient. +// +// Each op which emits a non-empty batch will also emit a non-empty batch_index +// Tensor, which, is a [K, 3] matrix where each row contains the invocation's id, +// start, and length of elements of each set of Tensors present in batched_tensors. +// +// Batched tensors are concatenated along the first dimension, and all tensors in +// in_tensors must have the first dimension of the same size. +// +// in_tensors: The tensors to be batched. +// num_batch_threads: Number of scheduling threads for processing batches of work. +// Determines the number of batches processed in parallel. +// max_batch_size: Batch sizes will never be bigger than this. +// batch_timeout_micros: Maximum number of microseconds to wait before outputting +// an incomplete batch. +// allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does +// nothing. Otherwise, supplies a list of batch sizes, causing the op to pad +// batches up to one of those sizes. The entries must increase monotonically, and +// the final entry must equal max_batch_size. +// grad_timeout_micros: The timeout to use for the gradient. See Unbatch. +// batched_tensors: Either empty tensors or a batch of concatenated Tensors. +// batch_index: If out_tensors is non-empty, has information to invert it. +// container: Controls the scope of sharing of this batch. +// id: always contains a scalar with a unique ID for this invocation of Batch. +// shared_name: Concurrently running instances of batch in the same device with the +// same container and shared_name will batch their elements together. If left +// empty, the op name will be used as the shared name. +// T: the types of tensors to be batched. +func Batch(scope *Scope, in_tensors []tf.Output, num_batch_threads int64, max_batch_size int64, batch_timeout_micros int64, grad_timeout_micros int64, optional ...BatchAttr) (batched_tensors []tf.Output, batch_index tf.Output, id tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"timeout_micros": timeout_micros} + attrs := map[string]interface{}{"num_batch_threads": num_batch_threads, "max_batch_size": max_batch_size, "batch_timeout_micros": batch_timeout_micros, "grad_timeout_micros": grad_timeout_micros} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Unbatch", + Type: "Batch", Input: []tf.Input{ - batched_tensor, batch_index, id, + tf.OutputList(in_tensors), }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Extract `patches` from `images` and put them in the "depth" output dimension. -// -// Arguments: -// images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`. -// ksizes: The size of the sliding window for each dimension of `images`. -// strides: 1-D of length 4. How far the centers of two consecutive patches are in -// the images. Must be: `[1, stride_rows, stride_cols, 1]`. -// rates: 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the -// input stride, specifying how far two consecutive patch samples are in the -// input. Equivalent to extracting patches with -// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by -// subsampling them spatially by a factor of `rates`. This is equivalent to -// `rate` in dilated (a.k.a. Atrous) convolutions. -// padding: The type of padding algorithm to use. -// -// We specify the size-related attributes as: -// -// ```python -// ksizes = [1, ksize_rows, ksize_cols, 1] -// strides = [1, strides_rows, strides_cols, 1] -// rates = [1, rates_rows, rates_cols, 1] -// ``` -// -// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * -// ksize_cols * depth]` containing image patches with size -// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note -// `out_rows` and `out_cols` are the dimensions of the output patches. -func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding} + var idx int + var err error + if batched_tensors, idx, err = makeOutputList(op, idx, "batched_tensors"); err != nil { + scope.UpdateErr("Batch", err) + return + } + batch_index = op.Output(idx) + id = op.Output(idx) + return batched_tensors, batch_index, id +} + +// Returns a tensor of zeros with the same shape and type as x. +// +// Arguments: +// x: a tensor of type T. +// +// Returns a tensor of the same shape and type as x but filled with zeros. +func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } opspec := tf.OpSpec{ - Type: "ExtractImagePatches", + Type: "ZerosLike", Input: []tf.Input{ - images, + x, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -3471,50 +3521,6 @@ func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Ensures that the tensor's shape matches the expected shape. -// -// Raises an error if the input tensor's shape does not match the specified shape. -// Returns the input tensor otherwise. -// -// Arguments: -// input: A tensor, whose shape is to be validated. -// shape: The expected (possibly partially specified) shape of the input tensor. -// -// Returns A tensor with the same shape and contents as the input tensor or value. -func EnsureShape(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"shape": shape} - opspec := tf.OpSpec{ - Type: "EnsureShape", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Elementwise computes the bitwise XOR of `x` and `y`. -// -// The result will have those bits set, that are different in `x` and `y`. The -// computation is performed on the underlying representations of `x` and `y`. -func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BitwiseXor", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Elementwise computes the bitwise OR of `x` and `y`. // // The result will have those bits set, that are set in `x`, `y` or both. The @@ -3533,34 +3539,20 @@ func BitwiseOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Elementwise computes the bitwise AND of `x` and `y`. +// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount). // -// The result will have those bits set, that are set in both `x` and `y`. The -// computation is performed on the underlying representations of `x` and `y`. -func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// For each entry in `x`, calculates the number of `1` (on) bits in the binary +// representation of that entry. +// +// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into +// `int32` or `int64` and perform the bitcount on the result, than to feed in +// 8- or 16-bit inputs and then aggregate the resulting counts. +func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "BitwiseAnd", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010. -// -// Flip each bit of supported types. For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101. -// This operation is performed on each element of the tensor argument `x`. -func Invert(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Invert", + Type: "PopulationCount", Input: []tf.Input{ x, }, @@ -3604,51 +3596,6 @@ func BoostedTreesQuantileStreamResourceGetBucketBoundaries(scope *Scope, quantil return bucket_boundaries } -// BoostedTreesQuantileStreamResourceFlushAttr is an optional argument to BoostedTreesQuantileStreamResourceFlush. -type BoostedTreesQuantileStreamResourceFlushAttr func(optionalAttr) - -// BoostedTreesQuantileStreamResourceFlushGenerateQuantiles sets the optional generate_quantiles attribute to value. -// -// value: bool; If True, the output will be the num_quantiles for each stream where the ith -// entry is the ith quantile of the input with an approximation error of epsilon. -// Duplicate values may be present. -// If False, the output will be the points in the histogram that we got which roughly -// translates to 1/epsilon boundaries and without any duplicates. -// Default to False. -// If not specified, defaults to false -func BoostedTreesQuantileStreamResourceFlushGenerateQuantiles(value bool) BoostedTreesQuantileStreamResourceFlushAttr { - return func(m optionalAttr) { - m["generate_quantiles"] = value - } -} - -// Flush the summaries for a quantile stream resource. -// -// An op that flushes the summaries for a quantile stream resource. -// -// Arguments: -// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. -// num_buckets: int; approximate number of buckets unless using generate_quantiles. -// -// Returns the created operation. -func BoostedTreesQuantileStreamResourceFlush(scope *Scope, quantile_stream_resource_handle tf.Output, num_buckets tf.Output, optional ...BoostedTreesQuantileStreamResourceFlushAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BoostedTreesQuantileStreamResourceFlush", - Input: []tf.Input{ - quantile_stream_resource_handle, num_buckets, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - // Deserialize bucket boundaries and ready flag into current QuantileAccumulator. // // An op that deserializes bucket boundaries and are boundaries ready flag into current QuantileAccumulator. @@ -3695,6 +3642,80 @@ func BoostedTreesQuantileStreamResourceAddSummaries(scope *Scope, quantile_strea return scope.AddOperation(opspec) } +// Makes the summary of quantiles for the batch. +// +// An op that takes a list of tensors (one tensor per feature) and outputs the +// quantile summaries for each tensor. +// +// Arguments: +// float_values: float; List of Rank 1 Tensors each containing values for a single feature. +// example_weights: float; Rank 1 Tensor with weights per instance. +// epsilon: float; The required maximum approximation error. +// +// Returns float; List of Rank 2 Tensors each containing the quantile summary +// (value, weight, min_rank, max_rank) of a single feature. +func BoostedTreesMakeQuantileSummaries(scope *Scope, float_values []tf.Output, example_weights tf.Output, epsilon tf.Output) (summaries []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesMakeQuantileSummaries", + Input: []tf.Input{ + tf.OutputList(float_values), example_weights, epsilon, + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if summaries, idx, err = makeOutputList(op, idx, "summaries"); err != nil { + scope.UpdateErr("BoostedTreesMakeQuantileSummaries", err) + return + } + return summaries +} + +// BoostedTreesCreateQuantileStreamResourceAttr is an optional argument to BoostedTreesCreateQuantileStreamResource. +type BoostedTreesCreateQuantileStreamResourceAttr func(optionalAttr) + +// BoostedTreesCreateQuantileStreamResourceMaxElements sets the optional max_elements attribute to value. +// +// value: int; The maximum number of data points that can be fed to the stream. +// If not specified, defaults to 1099511627776 +func BoostedTreesCreateQuantileStreamResourceMaxElements(value int64) BoostedTreesCreateQuantileStreamResourceAttr { + return func(m optionalAttr) { + m["max_elements"] = value + } +} + +// Create the Resource for Quantile Streams. +// +// Arguments: +// quantile_stream_resource_handle: resource; Handle to quantile stream resource. +// epsilon: float; The required approximation error of the stream resource. +// num_streams: int; The number of streams managed by the resource that shares the same epsilon. +// +// Returns the created operation. +func BoostedTreesCreateQuantileStreamResource(scope *Scope, quantile_stream_resource_handle tf.Output, epsilon tf.Output, num_streams tf.Output, optional ...BoostedTreesCreateQuantileStreamResourceAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCreateQuantileStreamResource", + Input: []tf.Input{ + quantile_stream_resource_handle, epsilon, num_streams, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // Checks whether a quantile stream has been initialized. // // An Op that checks if quantile stream resource is initialized. @@ -3754,6 +3775,37 @@ func BoostedTreesQuantileStreamResourceHandleOp(scope *Scope, optional ...Booste return op.Output(0) } +// Return a slice from 'input'. +// +// The output tensor is a tensor with dimensions described by 'size' +// whose values are extracted from 'input' starting at the offsets in +// 'begin'. +// +// *Requirements*: +// 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) +// +// Arguments: +// +// begin: begin[i] specifies the offset into the 'i'th dimension of +// 'input' to slice from. +// size: size[i] specifies the number of elements of the 'i'th dimension +// of 'input' to slice. If size[i] is -1, all remaining elements in dimension +// i are included in the slice (i.e. this is equivalent to setting +// size[i] = input.dim_size(i) - begin[i]). +func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Slice", + Input: []tf.Input{ + input, begin, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Updates the tree ensemble by either adding a layer to the last tree being grown // // or by starting a new tree. @@ -3794,24 +3846,39 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe return scope.AddOperation(opspec) } -// Serializes the tree ensemble to a proto. +// Runs multiple additive regression ensemble predictors on input instances and +// +// computes the update to cached logits. It is designed to be used during training. +// It traverses the trees starting from cached tree id and cached node id and +// calculates the updates to be pushed to the cache. // // Arguments: -// tree_ensemble_handle: Handle to the tree ensemble. // -// Returns Stamp token of the tree ensemble resource.Serialized proto of the ensemble. -func BoostedTreesSerializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, tree_ensemble_serialized tf.Output) { +// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting +// tree of prediction. +// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting +// node of prediction. +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for partial logits +// shape. +// +// Returns Rank 2 Tensor containing logits update (with respect to cached +// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. +func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} opspec := tf.OpSpec{ - Type: "BoostedTreesSerializeEnsemble", + Type: "BoostedTreesTrainingPredict", Input: []tf.Input{ - tree_ensemble_handle, + tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return op.Output(0), op.Output(1), op.Output(2) } // Debugging/model interpretability outputs for each example. @@ -3844,25 +3911,42 @@ func BoostedTreesExampleDebugOutputs(scope *Scope, tree_ensemble_handle tf.Outpu return op.Output(0) } -// Updates specified rows with values in `v`. +// PlaceholderAttr is an optional argument to Placeholder. +type PlaceholderAttr func(optionalAttr) + +// PlaceholderShape sets the optional shape attribute to value. // -// Computes `x[i, :] = v; return x`. +// value: (Optional) The shape of the tensor. If the shape has 0 dimensions, the +// shape is unconstrained. +// If not specified, defaults to +func PlaceholderShape(value tf.Shape) PlaceholderAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// A placeholder op for a value that will be fed into the computation. +// +// N.B. This operation will fail with an error if it is executed. It is +// intended as a way to represent a value that will always be fed, and to +// provide attrs that enable the fed value to be checked at runtime. // // Arguments: -// x: A tensor of type `T`. -// i: A vector. Indices into the left-most dimension of `x`. -// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// dtype: The type of elements in the tensor. // -// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. -func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { +// Returns A placeholder tensor that must be replaced using the feed mechanism. +func Placeholder(scope *Scope, dtype tf.DataType, optional ...PlaceholderAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "InplaceUpdate", - Input: []tf.Input{ - x, i, v, - }, + Type: "Placeholder", + + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -3897,48 +3981,84 @@ func BoostedTreesPredict(scope *Scope, tree_ensemble_handle tf.Output, bucketize return op.Output(0) } -// Deserializes a serialized tree ensemble config and replaces current tree +// Aggregates the summary of accumulated stats for the batch. // -// ensemble. +// The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket. +// +// Arguments: +// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. +// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. +// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. +// feature: int32; Rank 2 feature Tensors (shape=[batch_size, feature_dimension]). +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature. +// +// Returns output Rank 4 Tensor (shape=[splits, feature_dimension, buckets, logits_dimension + hessian_dimension]) +// containing accumulated stats for each node, feature dimension and bucket. +func BoostedTreesAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "BoostedTreesAggregateStats", + Input: []tf.Input{ + node_ids, gradients, hessians, feature, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Makes the summary of accumulated stats for the batch. +// +// The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example. +// +// Arguments: +// node_ids: int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer. +// gradients: float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients. +// hessians: float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians. +// bucketized_features_list: int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column). +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature. +// +// Returns output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket. The first index of 4th dimension refers to gradients, and the second to hessians. +func BoostedTreesMakeStatsSummary(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, bucketized_features_list []tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "BoostedTreesMakeStatsSummary", + Input: []tf.Input{ + node_ids, gradients, hessians, tf.OutputList(bucketized_features_list), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Retrieves the tree ensemble resource stamp token, number of trees and growing statistics. // // Arguments: // tree_ensemble_handle: Handle to the tree ensemble. -// stamp_token: Token to use as the new value of the resource stamp. -// tree_ensemble_serialized: Serialized proto of the ensemble. // -// Returns the created operation. -func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { +// Returns Stamp token of the tree ensemble resource.The number of trees in the tree ensemble resource.The number of trees that were finished successfully.The number of layers we attempted to build (but not necessarily succeeded).Rank size 2 tensor that contains start and end ids of the nodes in the latest +// layer. +func BoostedTreesGetEnsembleStates(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, num_trees tf.Output, num_finalized_trees tf.Output, num_attempted_layers tf.Output, last_layer_nodes_range tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "BoostedTreesDeserializeEnsemble", + Type: "BoostedTreesGetEnsembleStates", Input: []tf.Input{ - tree_ensemble_handle, stamp_token, tree_ensemble_serialized, + tree_ensemble_handle, }, } - return scope.AddOperation(opspec) -} - -// Creates a tree ensemble model and returns a handle to it. -// -// Arguments: -// tree_ensemble_handle: Handle to the tree ensemble resource to be created. -// stamp_token: Token to use as the initial value of the resource stamp. -// tree_ensemble_serialized: Serialized proto of the tree ensemble. -// -// Returns the created operation. -func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BoostedTreesCreateEnsemble", - Input: []tf.Input{ - tree_ensemble_handle, stamp_token, tree_ensemble_serialized, - }, - } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } // Calculates gains for each feature and returns the best possible split information for the feature. @@ -4003,154 +4123,6 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out return node_ids_list, gains_list, thresholds_list, left_node_contribs_list, right_node_contribs_list } -// Checks whether a tree ensemble has been initialized. -// -// Arguments: -// tree_ensemble_handle: Handle to the tree ensemble resouce. -// -// Returns output boolean on whether it is initialized or not. -func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsBoostedTreesEnsembleInitialized", - Input: []tf.Input{ - tree_ensemble_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// BoostedTreesEnsembleResourceHandleOpAttr is an optional argument to BoostedTreesEnsembleResourceHandleOp. -type BoostedTreesEnsembleResourceHandleOpAttr func(optionalAttr) - -// BoostedTreesEnsembleResourceHandleOpContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func BoostedTreesEnsembleResourceHandleOpContainer(value string) BoostedTreesEnsembleResourceHandleOpAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// BoostedTreesEnsembleResourceHandleOpSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func BoostedTreesEnsembleResourceHandleOpSharedName(value string) BoostedTreesEnsembleResourceHandleOpAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Creates a handle to a BoostedTreesEnsembleResource -func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTreesEnsembleResourceHandleOpAttr) (resource tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BoostedTreesEnsembleResourceHandleOp", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Output the logits for the given input data -// -// Arguments: -// tree_handle: Handle to the tree resource. -// dense_features: Rank 2 dense features tensor. -// logits_dimension: Scalar, dimension of the logits. -// -// Returns The logits predictions from the tree for each instance in the batch. -func TensorForestTreePredict(scope *Scope, tree_handle tf.Output, dense_features tf.Output, logits_dimension int64) (logits tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"logits_dimension": logits_dimension} - opspec := tf.OpSpec{ - Type: "TensorForestTreePredict", - Input: []tf.Input{ - tree_handle, dense_features, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Get the number of nodes in a tree -// -// Arguments: -// tree_handle: Handle to the tree resource. -// -// Returns The size of the tree. -func TensorForestTreeSize(scope *Scope, tree_handle tf.Output) (tree_size tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorForestTreeSize", - Input: []tf.Input{ - tree_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PreventGradientAttr is an optional argument to PreventGradient. -type PreventGradientAttr func(optionalAttr) - -// PreventGradientMessage sets the optional message attribute to value. -// -// value: Will be printed in the error when anyone tries to differentiate -// this operation. -// If not specified, defaults to "" -func PreventGradientMessage(value string) PreventGradientAttr { - return func(m optionalAttr) { - m["message"] = value - } -} - -// An identity op that triggers an error if a gradient is requested. -// -// When executed in a graph, this op outputs its input tensor as-is. -// -// When building ops to compute gradients, the TensorFlow gradient system -// will return an error when trying to lookup the gradient of this op, -// because no gradient must ever be registered for this function. This -// op exists to prevent subtle bugs from silently returning unimplemented -// gradients in some corner cases. -// -// Arguments: -// input: any tensor. -// -// Returns the same input tensor. -func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "PreventGradient", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Deserializes a proto into the tree handle // // Arguments: @@ -4171,24 +4143,24 @@ func TensorForestTreeDeserialize(scope *Scope, tree_handle tf.Output, tree_confi return scope.AddOperation(opspec) } -// Creates a tree resource and returns a handle to it. +// Serializes the tree handle to a proto // // Arguments: -// tree_handle: Handle to the tree resource to be created. -// tree_config: Serialized proto string of the boosted_trees.Tree. +// tree_handle: Handle to the tree resource to be serialized. // -// Returns the created operation. -func TensorForestCreateTreeVariable(scope *Scope, tree_handle tf.Output, tree_config tf.Output) (o *tf.Operation) { +// Returns Serialied proto string of the tree resource. +func TensorForestTreeSerialize(scope *Scope, tree_handle tf.Output) (tree_config tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "TensorForestCreateTreeVariable", + Type: "TensorForestTreeSerialize", Input: []tf.Input{ - tree_handle, tree_config, + tree_handle, }, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // Checks whether a tree has been initialized. @@ -4211,41 +4183,63 @@ func TensorForestTreeIsInitializedOp(scope *Scope, tree_handle tf.Output) (is_in return op.Output(0) } -// TensorForestTreeResourceHandleOpAttr is an optional argument to TensorForestTreeResourceHandleOp. -type TensorForestTreeResourceHandleOpAttr func(optionalAttr) +// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. +type ComputeAccidentalHitsAttr func(optionalAttr) -// TensorForestTreeResourceHandleOpContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func TensorForestTreeResourceHandleOpContainer(value string) TensorForestTreeResourceHandleOpAttr { +// ComputeAccidentalHitsSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { return func(m optionalAttr) { - m["container"] = value + m["seed"] = value } } -// TensorForestTreeResourceHandleOpSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func TensorForestTreeResourceHandleOpSharedName(value string) TensorForestTreeResourceHandleOpAttr { +// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["seed2"] = value } } -// Creates a handle to a TensorForestTreeResource -func TensorForestTreeResourceHandleOp(scope *Scope, optional ...TensorForestTreeResourceHandleOpAttr) (resource tf.Output) { +// Computes the ids of the positions in sampled_candidates that match true_labels. +// +// When doing log-odds NCE, the result of this op should be passed through a +// SparseToDense op, then added to the logits of the sampled candidates. This has +// the effect of 'removing' the sampled labels that match the true labels by +// making the classifier sure that they are sampled labels. +// +// Arguments: +// true_classes: The true_classes output of UnpackSparseLabels. +// sampled_candidates: The sampled_candidates output of CandidateSampler. +// num_true: Number of true labels per context. +// +// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label +// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element +// is -FLOAT_MAX. +func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"num_true": num_true} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TensorForestTreeResourceHandleOp", - + Type: "ComputeAccidentalHits", + Input: []tf.Input{ + true_classes, sampled_candidates, + }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } // FixedUnigramCandidateSamplerAttr is an optional argument to FixedUnigramCandidateSampler. @@ -4407,6 +4401,88 @@ func FixedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true return op.Output(0), op.Output(1), op.Output(2) } +// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm. +type QuantizedInstanceNormAttr func(optionalAttr) + +// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value. +// +// value: If True, `given_y_min` and `given_y_min` +// and `given_y_max` are used as the output range. Otherwise, +// the implementation computes the output range. +// If not specified, defaults to false +func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["output_range_given"] = value + } +} + +// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value. +// +// value: Output in `y_min` if `output_range_given` is True. +// If not specified, defaults to 0 +func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["given_y_min"] = value + } +} + +// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value. +// +// value: Output in `y_max` if `output_range_given` is True. +// If not specified, defaults to 0 +func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["given_y_max"] = value + } +} + +// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value. +// +// value: A small float number to avoid dividing by 0. +// If not specified, defaults to 1e-05 +func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["variance_epsilon"] = value + } +} + +// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value. +// +// value: Minimum value of `y_max - y_min` +// If not specified, defaults to 0.001 +func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["min_separation"] = value + } +} + +// Quantized Instance normalization. +// +// Arguments: +// x: A 4D input Tensor. +// x_min: The value represented by the lowest quantized input. +// x_max: The value represented by the highest quantized input. +// +// Returns A 4D Tensor.The value represented by the lowest quantized output.The value represented by the highest quantized output. +func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedInstanceNorm", + Input: []tf.Input{ + x, x_min, x_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler. type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr) @@ -4480,192 +4556,206 @@ func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, n return op.Output(0), op.Output(1), op.Output(2) } -// LoadAndRemapMatrixAttr is an optional argument to LoadAndRemapMatrix. -type LoadAndRemapMatrixAttr func(optionalAttr) +// LearnedUnigramCandidateSamplerAttr is an optional argument to LearnedUnigramCandidateSampler. +type LearnedUnigramCandidateSamplerAttr func(optionalAttr) -// LoadAndRemapMatrixMaxRowsInMemory sets the optional max_rows_in_memory attribute to value. +// LearnedUnigramCandidateSamplerSeed sets the optional seed attribute to value. // -// value: The maximum number of rows to load from the checkpoint at -// once. If less than or equal to 0, the entire matrix will be loaded into -// memory. Setting this arg trades increased disk reads for lower memory usage. -// If not specified, defaults to -1 -func LoadAndRemapMatrixMaxRowsInMemory(value int64) LoadAndRemapMatrixAttr { +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func LearnedUnigramCandidateSamplerSeed(value int64) LearnedUnigramCandidateSamplerAttr { return func(m optionalAttr) { - m["max_rows_in_memory"] = value + m["seed"] = value } } -// Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint +// LearnedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value. // -// at `ckpt_path` and potentially reorders its rows and columns using the -// specified remappings. +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func LearnedUnigramCandidateSamplerSeed2(value int64) LearnedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. // -// Most users should use one of the wrapper initializers (such as -// `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this -// function directly. +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. // -// The remappings are 1-D tensors with the following properties: +// For each batch, this op picks a single set of sampled candidate labels. // -// * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output -// matrix will be initialized from the row corresponding to index -// `row_remapping[i]` in the old `Tensor` from the checkpoint. -// * `col_remapping` must have either 0 entries (indicating that no column -// reordering is needed) or `num_cols` entries. If specified, column `j` of the -// output matrix will be initialized from the column corresponding to index -// `col_remapping[j]` in the old `Tensor` from the checkpoint. -// * A value of -1 in either of the remappings signifies a "missing" entry. In that -// case, values from the `initializing_values` tensor will be used to fill that -// missing row or column. If `row_remapping` has `r` missing entries and -// `col_remapping` has `c` missing entries, then the following condition must be -// true: -// -// `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)` -// -// The remapping tensors can be generated using the GenerateVocabRemapping op. -// -// As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1], -// initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing -// the value from row i, column j of the old tensor in the checkpoint, the output -// matrix will look like the following: -// -// [[w(1, 0), w(1, 2), 0.5], -// [w(0, 0), w(0, 2), -0.5], -// [0.25, -0.25, 42]] +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. // // Arguments: -// ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from -// which the old matrix `Tensor` will be loaded. -// old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint. -// row_remapping: An int `Tensor` of row remappings (generally created by -// `generate_vocab_remapping`). Even if no row remapping is needed, this must -// still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted -// index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`). -// col_remapping: An int `Tensor` of column remappings (generally created by -// `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping -// is to be done (e.g. column ordering is the same). -// initializing_values: A float `Tensor` containing values to fill in for cells -// in the output matrix that are not loaded from the checkpoint. Length must be -// exactly the same as the number of missing / new cells. -// num_rows: Number of rows (length of the 1st dimension) in the output matrix. -// num_cols: Number of columns (length of the 2nd dimension) in the output matrix. +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). // -// Returns Output matrix containing existing values loaded from the -// checkpoint, and with any missing values filled in from initializing_values. -func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Output, row_remapping tf.Output, col_remapping tf.Output, initializing_values tf.Output, num_rows int64, num_cols int64, optional ...LoadAndRemapMatrixAttr) (output_matrix tf.Output) { +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func LearnedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LearnedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_rows": num_rows, "num_cols": num_cols} + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "LoadAndRemapMatrix", + Type: "LearnedUnigramCandidateSampler", Input: []tf.Input{ - ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values, + true_classes, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } -// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. -type GenerateVocabRemappingAttr func(optionalAttr) +// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler. +type LogUniformCandidateSamplerAttr func(optionalAttr) -// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. +// LogUniformCandidateSamplerSeed sets the optional seed attribute to value. // -// value: Number of entries in the old vocab file to consider. If -1, -// use the entire old vocabulary. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr { return func(m optionalAttr) { - m["old_vocab_size"] = value + m["seed"] = value } } -// Given a path to new and old vocabulary files, returns a remapping Tensor of +// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. // -// length `num_new_vocab`, where `remapping[i]` contains the row number in the old -// vocabulary that corresponds to row `i` in the new vocabulary (starting at line -// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` -// in the new vocabulary is not in the old vocabulary. The old vocabulary is -// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the -// default value of -1. +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a log-uniform distribution. // -// `num_vocab_offset` enables -// use in the partitioned variable case, and should generally be set through -// examining partitioning info. The format of the files should be a text file, -// with each line containing a single entity within the vocabulary. +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. // -// For example, with `new_vocab_file` a text file containing each of the following -// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], -// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be -// `[0, -1, 2]`. +// For each batch, this op picks a single set of sampled candidate labels. // -// The op also returns a count of how many entries in the new vocabulary -// were present in the old vocabulary, which is used to calculate the number of -// values to initialize in a weight matrix remapping -// -// This functionality can be used to remap both row vocabularies (typically, -// features) and column vocabularies (typically, classes) from TensorFlow -// checkpoints. Note that the partitioning logic relies on contiguous vocabularies -// corresponding to div-partitioned variables. Moreover, the underlying remapping -// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should -// use the corresponding index_table_from_file() as the FeatureColumn framework -// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. // // Arguments: -// new_vocab_file: Path to the new vocab file. -// old_vocab_file: Path to the old vocab file. -// new_vocab_offset: How many entries into the new vocab file to start reading. -// num_new_vocab: Number of entries in the new vocab file to remap. +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). // -// Returns A Tensor of length num_new_vocab where the element at index i -// is equal to the old ID that maps to the new ID i. This element is -1 for any -// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. -func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "GenerateVocabRemapping", + Type: "LogUniformCandidateSampler", Input: []tf.Input{ - new_vocab_file, old_vocab_file, + true_classes, }, Attrs: attrs, } op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Selects the k nearest centers for each point. +// +// Rows of points are assumed to be input points. Rows of centers are assumed to be +// the list of candidate centers. For each point, the k centers that have least L2 +// distance to it are computed. +// +// Arguments: +// points: Matrix of shape (n, d). Rows are assumed to be input points. +// centers: Matrix of shape (m, d). Rows are assumed to be centers. +// k: Number of nearest centers to return for each point. If k is larger than m, then +// only m centers are returned. +// +// Returns Matrix of shape (n, min(m, k)). Each row contains the indices of the centers +// closest to the corresponding point, ordered by increasing distance.Matrix of shape (n, min(m, k)). Each row contains the squared L2 distance to the +// corresponding center in nearest_center_indices. +func NearestNeighbors(scope *Scope, points tf.Output, centers tf.Output, k tf.Output) (nearest_center_indices tf.Output, nearest_center_distances tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NearestNeighbors", + Input: []tf.Input{ + points, centers, k, + }, + } + op := scope.AddOperation(opspec) return op.Output(0), op.Output(1) } -// Returns the index of a data point that should be added to the seed set. +// Selects num_to_sample rows of input using the KMeans++ criterion. // -// Entries in distances are assumed to be squared distances of candidate points to -// the already sampled centers in the seed set. The op constructs one Markov chain -// of the k-MC^2 algorithm and returns the index of one candidate point to be added -// as an additional cluster center. +// Rows of points are assumed to be input points. One row is selected at random. +// Subsequent rows are sampled with probability proportional to the squared L2 +// distance from the nearest row selected thus far till num_to_sample rows have +// been sampled. // // Arguments: -// distances: Vector with squared distances to the closest previously sampled cluster center -// for each candidate point. +// points: Matrix of shape (n, d). Rows are assumed to be input points. +// num_to_sample: Scalar. The number of rows to sample. This value must not be larger than n. // seed: Scalar. Seed for initializing the random number generator. +// num_retries_per_sample: Scalar. For each row that is sampled, this parameter +// specifies the number of additional points to draw from the current +// distribution before selecting the best. If a negative value is specified, a +// heuristic is used to sample O(log(num_to_sample)) additional points. // -// Returns Scalar with the index of the sampled point. -func KMC2ChainInitialization(scope *Scope, distances tf.Output, seed tf.Output) (index tf.Output) { +// Returns Matrix of shape (num_to_sample, d). The sampled rows. +func KmeansPlusPlusInitialization(scope *Scope, points tf.Output, num_to_sample tf.Output, seed tf.Output, num_retries_per_sample tf.Output) (samples tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "KMC2ChainInitialization", + Type: "KmeansPlusPlusInitialization", Input: []tf.Input{ - distances, seed, + points, num_to_sample, seed, num_retries_per_sample, }, } op := scope.AddOperation(opspec) @@ -4689,54 +4779,6 @@ func CollectiveBcastSend(scope *Scope, input tf.Output, group_size int64, group_ return op.Output(0) } -// Mutually accumulates multiple tensors of identical type and shape. -func CollectiveGather(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} - opspec := tf.OpSpec{ - Type: "CollectiveGather", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CollectiveReduceAttr is an optional argument to CollectiveReduce. -type CollectiveReduceAttr func(optionalAttr) - -// CollectiveReduceWaitFor sets the optional wait_for attribute to value. -// If not specified, defaults to <> -func CollectiveReduceWaitFor(value []int64) CollectiveReduceAttr { - return func(m optionalAttr) { - m["wait_for"] = value - } -} - -// Mutually reduces multiple tensors of identical type and shape. -func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64, optional ...CollectiveReduceAttr) (data tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CollectiveReduce", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // AbortAttr is an optional argument to Abort. type AbortAttr func(optionalAttr) @@ -4782,6 +4824,21 @@ func Abort(scope *Scope, optional ...AbortAttr) (o *tf.Operation) { return scope.AddOperation(opspec) } +// Does nothing. Serves as a control trigger for scheduling. +// +// Only useful as a placeholder for control edges. +// +// Returns the created operation. +func ControlTrigger(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ControlTrigger", + } + return scope.AddOperation(opspec) +} + // Forwards the input to the output. // // This operator represents the loop termination condition used by the @@ -4805,6 +4862,48 @@ func LoopCond(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } +// Makes its input available to the next iteration. +// +// Arguments: +// data: The tensor to be made available to the next iteration. +// +// Returns The same tensor as `data`. +func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NextIteration", + Input: []tf.Input{ + data, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Exits the current frame to its parent frame. +// +// Exit makes its input `data` available to the parent frame. +// +// Arguments: +// data: The tensor to be made available to the parent frame. +// +// Returns The same tensor as `data`. +func Exit(scope *Scope, data tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Exit", + Input: []tf.Input{ + data, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // EnterAttr is an optional argument to Enter. type EnterAttr func(optionalAttr) @@ -4860,39 +4959,30 @@ func Enter(scope *Scope, data tf.Output, frame_name string, optional ...EnterAtt return op.Output(0) } -// Runs multiple additive regression ensemble predictors on input instances and +// Forwards the value of an available tensor from `inputs` to `output`. // -// computes the update to cached logits. It is designed to be used during training. -// It traverses the trees starting from cached tree id and cached node id and -// calculates the updates to be pushed to the cache. +// `Merge` waits for at least one of the tensors in `inputs` to become available. +// It is usually combined with `Switch` to implement branching. +// +// `Merge` forwards the first tensor to become available to `output`, and sets +// `value_index` to its index in `inputs`. // // Arguments: +// inputs: The input tensors, exactly one of which will become available. // -// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting -// tree of prediction. -// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting -// node of prediction. -// bucketized_features: A list of rank 1 Tensors containing bucket id for each -// feature. -// logits_dimension: scalar, dimension of the logits, to be used for partial logits -// shape. -// -// Returns Rank 2 Tensor containing logits update (with respect to cached -// values stored) for each example.Rank 1 Tensor containing new tree ids for each example.Rank 1 Tensor containing new node ids in the new tree_ids. -func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { +// Returns Will be set to the available input tensor.The index of the chosen input tensor in `inputs`. +func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"logits_dimension": logits_dimension} opspec := tf.OpSpec{ - Type: "BoostedTreesTrainingPredict", + Type: "Merge", Input: []tf.Input{ - tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), + tf.OutputList(inputs), }, - Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0), op.Output(1) } // Forwards `data` to the output port determined by `pred`. @@ -4921,6 +5011,78 @@ func Switch(scope *Scope, data tf.Output, pred tf.Output) (output_false tf.Outpu return op.Output(0), op.Output(1) } +// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder. +type CTCBeamSearchDecoderAttr func(optionalAttr) + +// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value. +// +// value: If true, merge repeated classes in output. +// If not specified, defaults to true +func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr { + return func(m optionalAttr) { + m["merge_repeated"] = value + } +} + +// Performs beam search decoding on the logits given in input. +// +// A note about the attribute merge_repeated: For the beam search decoder, +// this means that if consecutive entries in a beam are the same, only +// the first of these is emitted. That is, when the top path is "A B B B B", +// "A B" is returned if merge_repeated = True but "A B B B B" is +// returned if merge_repeated = False. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// sequence_length: A vector containing sequence lengths, size `(batch)`. +// beam_width: A scalar >= 0 (beam search beam width). +// top_paths: A scalar >= 0, <= beam_width (controls output size). +// +// Returns A list (length: top_paths) of indices matrices. Matrix j, +// size `(total_decoded_outputs[j] x 2)`, has indices of a +// `SparseTensor`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j, +// size `(length total_decoded_outputs[j])`, has the values of a +// `SparseTensor`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j, +// size `(2)`, stores the shape of the decoded `SparseTensor[j]`. +// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The +// sequence log-probabilities. +func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCBeamSearchDecoder", + Input: []tf.Input{ + inputs, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + log_probability = op.Output(idx) + return decoded_indices, decoded_values, decoded_shape, log_probability +} + // CTCGreedyDecoderAttr is an optional argument to CTCGreedyDecoder. type CTCGreedyDecoderAttr func(optionalAttr) @@ -4974,61 +5136,61 @@ func CTCGreedyDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, return op.Output(0), op.Output(1), op.Output(2), op.Output(3) } -// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical. -type CudnnRNNParamsToCanonicalAttr func(optionalAttr) +// CudnnRNNCanonicalToParamsAttr is an optional argument to CudnnRNNCanonicalToParams. +type CudnnRNNCanonicalToParamsAttr func(optionalAttr) -// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value. +// CudnnRNNCanonicalToParamsRnnMode sets the optional rnn_mode attribute to value. // If not specified, defaults to "lstm" -func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsRnnMode(value string) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["rnn_mode"] = value } } -// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value. +// CudnnRNNCanonicalToParamsInputMode sets the optional input_mode attribute to value. // If not specified, defaults to "linear_input" -func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsInputMode(value string) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["input_mode"] = value } } -// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value. +// CudnnRNNCanonicalToParamsDirection sets the optional direction attribute to value. // If not specified, defaults to "unidirectional" -func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsDirection(value string) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["direction"] = value } } -// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value. +// CudnnRNNCanonicalToParamsDropout sets the optional dropout attribute to value. // If not specified, defaults to 0 -func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsDropout(value float32) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["dropout"] = value } } -// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value. +// CudnnRNNCanonicalToParamsSeed sets the optional seed attribute to value. // If not specified, defaults to 0 -func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsSeed(value int64) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["seed"] = value } } -// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value. +// CudnnRNNCanonicalToParamsSeed2 sets the optional seed2 attribute to value. // If not specified, defaults to 0 -func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr { +func CudnnRNNCanonicalToParamsSeed2(value int64) CudnnRNNCanonicalToParamsAttr { return func(m optionalAttr) { m["seed2"] = value } } -// Retrieves CudnnRNN params in canonical form. +// Converts CudnnRNN params from canonical form to usable form. // -// Retrieves a set of weights from the opaque params buffer that can be saved and -// restored in a way compatible with future runs. +// Writes a set of weights into the opaque params buffer so they can be used in +// upcoming training or inferences. // // Note that the params buffer may not be compatible across different GPUs. So any // save and restoration should be converted to and from the canonical weights and @@ -5037,15 +5199,15 @@ func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr { // num_layers: Specifies the number of layers in the RNN model. // num_units: Specifies the size of the hidden state. // input_size: Specifies the size of the input state. -// num_params: number of parameter sets for all layers. -// Each layer may contain multiple parameter sets, with each set consisting of -// a weight matrix and a bias vector. // weights: the canonical form of weights that can be used for saving // and restoration. They are more likely to be compatible across different // generations. // biases: the canonical form of biases that can be used for saving // and restoration. They are more likely to be compatible across different // generations. +// num_params: number of parameter sets for all layers. +// Each layer may contain multiple parameter sets, with each set consisting of +// a weight matrix and a bias vector. // rnn_mode: Indicates the type of the RNN model. // input_mode: Indicate whether there is a linear projection between the input and // The actual computation before the first layer. 'skip_input' is only allowed @@ -5056,36 +5218,23 @@ func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr { // dropout: dropout probability. When set to 0., dropout is disabled. // seed: the 1st part of a seed to initialize dropout. // seed2: the 2nd part of a seed to initialize dropout. -func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) { +func CudnnRNNCanonicalToParams(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsAttr) (params tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_params": num_params} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "CudnnRNNParamsToCanonical", + Type: "CudnnRNNCanonicalToParams", Input: []tf.Input{ - num_layers, num_units, input_size, params, + num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases), }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil { - scope.UpdateErr("CudnnRNNParamsToCanonical", err) - return - } - if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil { - scope.UpdateErr("CudnnRNNParamsToCanonical", err) - return - } - return weights, biases + return op.Output(0) } // CudnnRNNBackpropAttr is an optional argument to CudnnRNNBackprop. @@ -5530,47 +5679,49 @@ func CudnnRNNParamsSize(scope *Scope, num_layers tf.Output, num_units tf.Output, return op.Output(0) } -// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize. -type OrderedMapIncompleteSizeAttr func(optionalAttr) +// OrderedMapClearAttr is an optional argument to OrderedMapClear. +type OrderedMapClearAttr func(optionalAttr) -// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value. +// OrderedMapClearCapacity sets the optional capacity attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr { +func OrderedMapClearCapacity(value int64) OrderedMapClearAttr { return func(m optionalAttr) { m["capacity"] = value } } -// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// OrderedMapClearMemoryLimit sets the optional memory_limit attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr { +func OrderedMapClearMemoryLimit(value int64) OrderedMapClearAttr { return func(m optionalAttr) { m["memory_limit"] = value } } -// OrderedMapIncompleteSizeContainer sets the optional container attribute to value. +// OrderedMapClearContainer sets the optional container attribute to value. // If not specified, defaults to "" -func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr { +func OrderedMapClearContainer(value string) OrderedMapClearAttr { return func(m optionalAttr) { m["container"] = value } } -// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// OrderedMapClearSharedName sets the optional shared_name attribute to value. // If not specified, defaults to "" -func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr { +func OrderedMapClearSharedName(value string) OrderedMapClearAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Op returns the number of incomplete elements in the underlying container. -func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) { +// Op removes all elements in the underlying container. +// +// Returns the created operation. +func OrderedMapClear(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapClearAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -5579,12 +5730,11 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or a(attrs) } opspec := tf.OpSpec{ - Type: "OrderedMapIncompleteSize", + Type: "OrderedMapClear", Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } // OrderedMapUnstageNoKeyAttr is an optional argument to OrderedMapUnstageNoKey. @@ -5659,6 +5809,77 @@ func OrderedMapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataTyp return key, values } +// OrderedMapUnstageAttr is an optional argument to OrderedMapUnstage. +type OrderedMapUnstageAttr func(optionalAttr) + +// OrderedMapUnstageCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageCapacity(value int64) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapUnstageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageMemoryLimit(value int64) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapUnstageContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageContainer(value string) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapUnstageSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageSharedName(value string) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns the values associated with the key +// +// from the underlying container. If the underlying container +// does not contain this key, the op will block until it does. +func OrderedMapUnstage(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapUnstage", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapUnstage", err) + return + } + return values +} + // OrderedMapPeekAttr is an optional argument to OrderedMapPeek. type OrderedMapPeekAttr func(optionalAttr) @@ -5731,54 +5952,56 @@ func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf. return values } -// MapStageAttr is an optional argument to MapStage. -type MapStageAttr func(optionalAttr) +// OrderedMapStageAttr is an optional argument to OrderedMapStage. +type OrderedMapStageAttr func(optionalAttr) -// MapStageCapacity sets the optional capacity attribute to value. +// OrderedMapStageCapacity sets the optional capacity attribute to value. // // value: Maximum number of elements in the Staging Area. If > 0, inserts // on the container will block when the capacity is reached. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapStageCapacity(value int64) MapStageAttr { +func OrderedMapStageCapacity(value int64) OrderedMapStageAttr { return func(m optionalAttr) { m["capacity"] = value } } -// MapStageMemoryLimit sets the optional memory_limit attribute to value. +// OrderedMapStageMemoryLimit sets the optional memory_limit attribute to value. // If not specified, defaults to 0 // // REQUIRES: value >= 0 -func MapStageMemoryLimit(value int64) MapStageAttr { +func OrderedMapStageMemoryLimit(value int64) OrderedMapStageAttr { return func(m optionalAttr) { m["memory_limit"] = value } } -// MapStageContainer sets the optional container attribute to value. +// OrderedMapStageContainer sets the optional container attribute to value. // // value: If non-empty, this queue is placed in the given container. Otherwise, // a default container is used. // If not specified, defaults to "" -func MapStageContainer(value string) MapStageAttr { +func OrderedMapStageContainer(value string) OrderedMapStageAttr { return func(m optionalAttr) { m["container"] = value } } -// MapStageSharedName sets the optional shared_name attribute to value. +// OrderedMapStageSharedName sets the optional shared_name attribute to value. // // value: It is necessary to match this name to the matching Unstage Op. // If not specified, defaults to "" -func MapStageSharedName(value string) MapStageAttr { +func OrderedMapStageSharedName(value string) OrderedMapStageAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Stage (key, values) in the underlying container which behaves like a hashtable. +// Stage (key, values) in the underlying container which behaves like a ordered +// +// associative container. Elements are ordered by key. // // Arguments: // key: int64 @@ -5788,7 +6011,7 @@ func MapStageSharedName(value string) MapStageAttr { // // // Returns the created operation. -func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...MapStageAttr) (o *tf.Operation) { +func OrderedMapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...OrderedMapStageAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -5797,7 +6020,7 @@ func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output a(attrs) } opspec := tf.OpSpec{ - Type: "MapStage", + Type: "OrderedMapStage", Input: []tf.Input{ key, indices, tf.OutputList(values), }, @@ -5806,6 +6029,77 @@ func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output return scope.AddOperation(opspec) } +// MapPeekAttr is an optional argument to MapPeek. +type MapPeekAttr func(optionalAttr) + +// MapPeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapPeekCapacity(value int64) MapPeekAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapPeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapPeekMemoryLimit(value int64) MapPeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapPeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapPeekContainer(value string) MapPeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapPeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapPeekSharedName(value string) MapPeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified key. If the +// +// underlying container does not contain this key +// this op will block until it does. +func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapPeek", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapPeek", err) + return + } + return values +} + // StageClearAttr is an optional argument to StageClear. type StageClearAttr func(optionalAttr) @@ -5864,6 +6158,63 @@ func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) return scope.AddOperation(opspec) } +// StageSizeAttr is an optional argument to StageSize. +type StageSizeAttr func(optionalAttr) + +// StageSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeCapacity(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeMemoryLimit(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageSizeContainer(value string) StageSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageSizeSharedName(value string) StageSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StageSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // UnstageAttr is an optional argument to Unstage. type UnstageAttr func(optionalAttr) @@ -5933,27 +6284,101 @@ func Unstage(scope *Scope, dtypes []tf.DataType, optional ...UnstageAttr) (value return values } -// Get the value of the tensor specified by its handle. +// StageAttr is an optional argument to Stage. +type StageAttr func(optionalAttr) + +// StageCapacity sets the optional capacity attribute to value. +// +// value: Maximum number of elements in the Staging Area. If > 0, inserts +// on the container will block when the capacity is reached. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageCapacity(value int64) StageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageMemoryLimit sets the optional memory_limit attribute to value. +// +// value: The maximum number of bytes allowed for Tensors in the Staging Area. +// If > 0, inserts will block until sufficient space is available. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageMemoryLimit(value int64) StageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageContainer sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. Otherwise, +// a default container is used. +// If not specified, defaults to "" +func StageContainer(value string) StageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSharedName sets the optional shared_name attribute to value. +// +// value: It is necessary to match this name to the matching Unstage Op. +// If not specified, defaults to "" +func StageSharedName(value string) StageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Stage values similar to a lightweight Enqueue. +// +// The basic functionality of this Op is similar to a queue with many +// fewer capabilities and options. This Op is optimized for performance. // // Arguments: -// handle: The handle for a tensor stored in the session state. -// dtype: The type of the output value. +// values: a list of tensors +// dtypes A list of data types that inserted values should adhere to. // -// Returns The tensor for the given handle. -func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) { +// Returns the created operation. +func Stage(scope *Scope, values []tf.Output, optional ...StageAttr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "GetSessionTensor", + Type: "Stage", Input: []tf.Input{ - handle, + tf.OutputList(values), }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) +} + +// Delete the tensor specified by its handle in the session. +// +// Arguments: +// handle: The handle for a tensor stored in the session state. +// +// Returns the created operation. +func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeleteSessionTensor", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) } // Store the input tensor in the state of the current session. @@ -5977,22 +6402,100 @@ func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) { return op.Output(0) } -// Deprecated. Use TensorArrayCloseV3 +// ReverseSequenceAttr is an optional argument to ReverseSequence. +type ReverseSequenceAttr func(optionalAttr) + +// ReverseSequenceBatchDim sets the optional batch_dim attribute to value. // -// DEPRECATED at GraphDef version 26: Use TensorArrayCloseV3 +// value: The dimension along which reversal is performed. +// If not specified, defaults to 0 +func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr { + return func(m optionalAttr) { + m["batch_dim"] = value + } +} + +// Reverses variable length slices. // -// Returns the created operation. -func TensorArrayCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { +// This op first slices `input` along the dimension `batch_dim`, and for each +// slice `i`, reverses the first `seq_lengths[i]` elements along +// the dimension `seq_dim`. +// +// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`, +// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`. +// +// The output slice `i` along dimension `batch_dim` is then given by input +// slice `i`, with the first `seq_lengths[i]` slices along dimension +// `seq_dim` reversed. +// +// For example: +// +// ``` +// # Given this: +// batch_dim = 0 +// seq_dim = 1 +// input.dims = (4, 8, ...) +// seq_lengths = [7, 2, 3, 5] +// +// # then slices of input are reversed on seq_dim, but only up to seq_lengths: +// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] +// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] +// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] +// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] +// +// # while entries past seq_lens are copied through: +// output[0, 7:, :, ...] = input[0, 7:, :, ...] +// output[1, 2:, :, ...] = input[1, 2:, :, ...] +// output[2, 3:, :, ...] = input[2, 3:, :, ...] +// output[3, 2:, :, ...] = input[3, 2:, :, ...] +// ``` +// +// In contrast, if: +// +// ``` +// # Given this: +// batch_dim = 2 +// seq_dim = 0 +// input.dims = (8, ?, 4, ...) +// seq_lengths = [7, 2, 3, 5] +// +// # then slices of input are reversed on seq_dim, but only up to seq_lengths: +// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...] +// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...] +// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...] +// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...] +// +// # while entries past seq_lens are copied through: +// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...] +// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...] +// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...] +// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...] +// ``` +// +// Arguments: +// input: The input to reverse. +// seq_lengths: 1-D with length `input.dims(batch_dim)` and +// `max(seq_lengths) <= input.dims(seq_dim)` +// seq_dim: The dimension which is partially reversed. +// +// Returns The partially reversed input. It has the same shape as `input`. +func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "TensorArrayCloseV2", - Input: []tf.Input{ - handle, - }, + attrs := map[string]interface{}{"seq_dim": seq_dim} + for _, a := range optional { + a(attrs) } - return scope.AddOperation(opspec) + opspec := tf.OpSpec{ + Type: "ReverseSequence", + Input: []tf.Input{ + input, seq_lengths, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) } // Deprecated. Use TensorArraySizeV3 @@ -6012,33 +6515,120 @@ func TensorArraySizeV2(scope *Scope, handle tf.Output, flow_in tf.Output) (size return op.Output(0) } -// Receives a tensor value broadcast from another device. -func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { +// TensorArrayConcatV2Attr is an optional argument to TensorArrayConcatV2. +type TensorArrayConcatV2Attr func(optionalAttr) + +// TensorArrayConcatV2ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. +// If not specified, defaults to +func TensorArrayConcatV2ElementShapeExcept0(value tf.Shape) TensorArrayConcatV2Attr { + return func(m optionalAttr) { + m["element_shape_except0"] = value + } +} + +// Deprecated. Use TensorArrayConcatV3 +func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV2Attr) (value tf.Output, lengths tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "CollectiveBcastRecv", - + Type: "TensorArrayConcatV2", + Input: []tf.Input{ + handle, flow_in, + }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Deprecated. Use TensorArraySplitV3 +// RecordInputAttr is an optional argument to RecordInput. +type RecordInputAttr func(optionalAttr) + +// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value. // -// DEPRECATED at GraphDef version 26: Use TensorArraySplitV3 -func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) { +// value: Random seeds used to produce randomized records. +// If not specified, defaults to 301 +func RecordInputFileRandomSeed(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_random_seed"] = value + } +} + +// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value. +// +// value: Shifts the list of files after the list is randomly +// shuffled. +// If not specified, defaults to 0 +func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr { + return func(m optionalAttr) { + m["file_shuffle_shift_ratio"] = value + } +} + +// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value. +// +// value: The randomization shuffling buffer. +// If not specified, defaults to 10000 +func RecordInputFileBufferSize(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_buffer_size"] = value + } +} + +// RecordInputFileParallelism sets the optional file_parallelism attribute to value. +// +// value: How many sstables are opened and concurrently iterated over. +// If not specified, defaults to 16 +func RecordInputFileParallelism(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_parallelism"] = value + } +} + +// RecordInputBatchSize sets the optional batch_size attribute to value. +// +// value: The batch size. +// If not specified, defaults to 32 +func RecordInputBatchSize(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["batch_size"] = value + } +} + +// RecordInputCompressionType sets the optional compression_type attribute to value. +// +// value: The type of compression for the file. Currently ZLIB and +// GZIP are supported. Defaults to none. +// If not specified, defaults to "" +func RecordInputCompressionType(value string) RecordInputAttr { + return func(m optionalAttr) { + m["compression_type"] = value + } +} + +// Emits randomized records. +// +// Arguments: +// file_pattern: Glob pattern for the data files. +// +// Returns A tensor of shape [batch_size]. +func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"file_pattern": file_pattern} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorArraySplitV2", - Input: []tf.Input{ - handle, value, lengths, flow_in, - }, + Type: "RecordInput", + + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -6077,6 +6667,61 @@ func TensorArrayGatherV2(scope *Scope, handle tf.Output, indices tf.Output, flow return op.Output(0) } +// Deprecated. Use TensorArrayReadV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayReadV3 +func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "TensorArrayReadV2", + Input: []tf.Input{ + handle, index, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayGradV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3 +func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayWriteV2", + Input: []tf.Input{ + handle, index, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayGradV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayGradV3 +func TensorArrayGradV2(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"source": source} + opspec := tf.OpSpec{ + Type: "TensorArrayGradV2", + Input: []tf.Input{ + handle, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // TensorArrayV2Attr is an optional argument to TensorArrayV2. type TensorArrayV2Attr func(optionalAttr) @@ -6134,6 +6779,28 @@ func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ... return op.Output(0) } +// Delete the TensorArray from its resource container. +// +// This enables the user to close and release the resource in the middle +// of a step/run. +// +// Arguments: +// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// +// Returns the created operation. +func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayCloseV3", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + // Split the data from the input value into TensorArray elements. // // Assuming that `lengths` takes on values @@ -6176,170 +6843,70 @@ func TensorArraySplitV3(scope *Scope, handle tf.Output, value tf.Output, lengths return op.Output(0) } -// QuantizeV2Attr is an optional argument to QuantizeV2. -type QuantizeV2Attr func(optionalAttr) - -// QuantizeV2Mode sets the optional mode attribute to value. -// If not specified, defaults to "MIN_COMBINED" -func QuantizeV2Mode(value string) QuantizeV2Attr { - return func(m optionalAttr) { - m["mode"] = value - } -} - -// QuantizeV2RoundMode sets the optional round_mode attribute to value. -// If not specified, defaults to "HALF_AWAY_FROM_ZERO" -func QuantizeV2RoundMode(value string) QuantizeV2Attr { - return func(m optionalAttr) { - m["round_mode"] = value - } -} - -// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'. +// Scatter the data from the input value into specific TensorArray elements. // -// [min_range, max_range] are scalar floats that specify the range for -// the 'input' data. The 'mode' attribute controls exactly which calculations are -// used to convert the float values to their quantized equivalents. The -// 'round_mode' attribute controls which rounding tie-breaking algorithm is used -// when rounding float values to their quantized equivalents. -// -// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: -// -// ``` -// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) -// if T == qint8: out[i] -= (range(T) + 1) / 2.0 -// ``` -// -// here `range(T) = numeric_limits::max() - numeric_limits::min()` -// -// *MIN_COMBINED Mode Example* -// -// Assume the input is type float and has a possible range of [0.0, 6.0] and the -// output type is quint8 ([0, 255]). The min_range and max_range values should be -// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each -// value of the input by 255/6 and cast to quint8. -// -// If the output type was qint8 ([-128, 127]), the operation will additionally -// subtract each value by 128 prior to casting, so that the range of values aligns -// with the range of qint8. -// -// If the mode is 'MIN_FIRST', then this approach is used: -// -// ``` -// num_discrete_values = 1 << (# of bits in T) -// range_adjust = num_discrete_values / (num_discrete_values - 1) -// range = (range_max - range_min) * range_adjust -// range_scale = num_discrete_values / range -// quantized = round(input * range_scale) - round(range_min * range_scale) + -// numeric_limits::min() -// quantized = max(quantized, numeric_limits::min()) -// quantized = min(quantized, numeric_limits::max()) -// ``` -// -// The biggest difference between this and MIN_COMBINED is that the minimum range -// is rounded first, before it's subtracted from the rounded value. With -// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing -// and dequantizing will introduce a larger and larger error. -// -// *SCALED mode Example* -// -// `SCALED` mode matches the quantization approach used in -// `QuantizeAndDequantize{V2|V3}`. -// -// If the mode is `SCALED`, we do not use the full range of the output type, -// choosing to elide the lowest possible value for symmetry (e.g., output range is -// -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to -// 0. -// -// We first find the range of values in our tensor. The -// range we use is always centered on 0, so we find m such that -// -// ```c++ -// m = max(abs(input_min), abs(input_max)) -// ``` -// -// Our input tensor range is then `[-m, m]`. -// -// Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`. -// If T is signed, this is -// -// ``` -// num_bits = sizeof(T) * 8 -// [min_fixed, max_fixed] = -// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1] -// ``` -// -// Otherwise, if T is unsigned, the fixed-point range is -// -// ``` -// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1] -// ``` -// -// From this we compute our scaling factor, s: -// -// ```c++ -// s = (max_fixed - min_fixed) / (2 * m) -// ``` -// -// Now we can quantize the elements of our tensor: -// -// ```c++ -// result = round(input * s) -// ``` -// -// One thing to watch out for is that the operator may choose to adjust the -// requested minimum and maximum values slightly during the quantization process, -// so you should always use the output ports as the range for further calculations. -// For example, if the requested minimum and maximum values are close to equal, -// they will be separated by a small epsilon value to prevent ill-formed quantized -// buffers from being created. Otherwise, you can end up with buffers where all the -// quantized values map to the same float value, which causes problems for -// operations that have to perform further calculations on them. -// -// Arguments: -// -// min_range: The minimum scalar value possibly produced for the input. -// max_range: The maximum scalar value possibly produced for the input. -// -// -// Returns The quantized data produced from the float input.The actual minimum scalar value used for the output.The actual maximum scalar value used for the output. -func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"T": T} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizeV2", - Input: []tf.Input{ - input, min_range, max_range, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Read an element from the TensorArray into output `value`. +// `indices` must be a vector, its length must match the first dim of `value`. // // Arguments: // handle: The handle to a TensorArray. +// indices: The locations at which to write the tensor elements. +// value: The concatenated tensor to write to the TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. // +// Returns A float scalar that enforces proper chaining of operations. +func TensorArrayScatterV3(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayScatterV3", + Input: []tf.Input{ + handle, indices, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. +type TensorArrayGatherV3Attr func(optionalAttr) + +// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. +// +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Gather specific elements from the TensorArray into output `value`. +// +// All elements selected by `indices` must have the same shape. +// +// Arguments: +// handle: The handle to a TensorArray. +// indices: The locations in the TensorArray from which to read tensor elements. // flow_in: A float scalar that enforces proper chaining of operations. // dtype: The type of the elem that is returned. // -// Returns The tensor that is read from the TensorArray. -func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { +// Returns All of the elements in the TensorArray, concatenated along a new +// axis (the new dimension 0). +func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorArrayReadV3", + Type: "TensorArrayGatherV3", Input: []tf.Input{ - handle, index, flow_in, + handle, indices, flow_in, }, Attrs: attrs, } @@ -6401,6 +6968,26 @@ func TensorArrayGradWithShape(scope *Scope, handle tf.Output, flow_in tf.Output, return op.Output(0), op.Output(1) } +// Returns a tensor of ones with the same shape and type as x. +// +// Arguments: +// x: a tensor of type T. +// +// Returns a tensor of the same shape and type as x but filled with ones. +func OnesLike(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OnesLike", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a TensorArray for storing the gradients of values in the given handle. // // If the given TensorArray gradient already exists, returns a reference to it. @@ -6461,134 +7048,159 @@ func TensorArrayGradV3(scope *Scope, handle tf.Output, flow_in tf.Output, source return op.Output(0), op.Output(1) } -// Delete the stack from its resource container. +// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. +type GenerateVocabRemappingAttr func(optionalAttr) + +// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. +// +// value: Number of entries in the old vocab file to consider. If -1, +// use the entire old vocabulary. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { + return func(m optionalAttr) { + m["old_vocab_size"] = value + } +} + +// Given a path to new and old vocabulary files, returns a remapping Tensor of +// +// length `num_new_vocab`, where `remapping[i]` contains the row number in the old +// vocabulary that corresponds to row `i` in the new vocabulary (starting at line +// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` +// in the new vocabulary is not in the old vocabulary. The old vocabulary is +// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the +// default value of -1. +// +// `num_vocab_offset` enables +// use in the partitioned variable case, and should generally be set through +// examining partitioning info. The format of the files should be a text file, +// with each line containing a single entity within the vocabulary. +// +// For example, with `new_vocab_file` a text file containing each of the following +// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], +// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be +// `[0, -1, 2]`. +// +// The op also returns a count of how many entries in the new vocabulary +// were present in the old vocabulary, which is used to calculate the number of +// values to initialize in a weight matrix remapping +// +// This functionality can be used to remap both row vocabularies (typically, +// features) and column vocabularies (typically, classes) from TensorFlow +// checkpoints. Note that the partitioning logic relies on contiguous vocabularies +// corresponding to div-partitioned variables. Moreover, the underlying remapping +// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should +// use the corresponding index_table_from_file() as the FeatureColumn framework +// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). // // Arguments: -// handle: The handle to a stack. +// new_vocab_file: Path to the new vocab file. +// old_vocab_file: Path to the old vocab file. +// new_vocab_offset: How many entries into the new vocab file to start reading. +// num_new_vocab: Number of entries in the new vocab file to remap. // -// Returns the created operation. -func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { +// Returns A Tensor of length num_new_vocab where the element at index i +// is equal to the old ID that maps to the new ID i. This element is -1 for any +// new ID that is not found in the old vocabulary.Number of new vocab entries found in old vocab. +func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "StackCloseV2", - Input: []tf.Input{ - handle, - }, - } - return scope.AddOperation(opspec) -} - -// BatchAttr is an optional argument to Batch. -type BatchAttr func(optionalAttr) - -// BatchMaxEnqueuedBatches sets the optional max_enqueued_batches attribute to value. -// If not specified, defaults to 10 -func BatchMaxEnqueuedBatches(value int64) BatchAttr { - return func(m optionalAttr) { - m["max_enqueued_batches"] = value - } -} - -// BatchAllowedBatchSizes sets the optional allowed_batch_sizes attribute to value. -// If not specified, defaults to <> -func BatchAllowedBatchSizes(value []int64) BatchAttr { - return func(m optionalAttr) { - m["allowed_batch_sizes"] = value - } -} - -// BatchContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func BatchContainer(value string) BatchAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// BatchSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func BatchSharedName(value string) BatchAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// BatchBatchingQueue sets the optional batching_queue attribute to value. -// If not specified, defaults to "" -func BatchBatchingQueue(value string) BatchAttr { - return func(m optionalAttr) { - m["batching_queue"] = value - } -} - -// Batches all input tensors nondeterministically. -// -// When many instances of this Op are being run concurrently with the same -// container/shared_name in the same device, some will output zero-shaped Tensors -// and others will output Tensors of size up to max_batch_size. -// -// All Tensors in in_tensors are batched together (so, for example, labels and -// features should be batched with a single instance of this operation. -// -// Each invocation of batch emits an `id` scalar which will be used to identify -// this particular invocation when doing unbatch or its gradient. -// -// Each op which emits a non-empty batch will also emit a non-empty batch_index -// Tensor, which, is a [K, 3] matrix where each row contains the invocation's id, -// start, and length of elements of each set of Tensors present in batched_tensors. -// -// Batched tensors are concatenated along the first dimension, and all tensors in -// in_tensors must have the first dimension of the same size. -// -// in_tensors: The tensors to be batched. -// num_batch_threads: Number of scheduling threads for processing batches of work. -// Determines the number of batches processed in parallel. -// max_batch_size: Batch sizes will never be bigger than this. -// batch_timeout_micros: Maximum number of microseconds to wait before outputting -// an incomplete batch. -// allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does -// nothing. Otherwise, supplies a list of batch sizes, causing the op to pad -// batches up to one of those sizes. The entries must increase monotonically, and -// the final entry must equal max_batch_size. -// grad_timeout_micros: The timeout to use for the gradient. See Unbatch. -// batched_tensors: Either empty tensors or a batch of concatenated Tensors. -// batch_index: If out_tensors is non-empty, has information to invert it. -// container: Controls the scope of sharing of this batch. -// id: always contains a scalar with a unique ID for this invocation of Batch. -// shared_name: Concurrently running instances of batch in the same device with the -// same container and shared_name will batch their elements together. If left -// empty, the op name will be used as the shared name. -// T: the types of tensors to be batched. -func Batch(scope *Scope, in_tensors []tf.Output, num_batch_threads int64, max_batch_size int64, batch_timeout_micros int64, grad_timeout_micros int64, optional ...BatchAttr) (batched_tensors []tf.Output, batch_index tf.Output, id tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_batch_threads": num_batch_threads, "max_batch_size": max_batch_size, "batch_timeout_micros": batch_timeout_micros, "grad_timeout_micros": grad_timeout_micros} + attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Batch", + Type: "GenerateVocabRemapping", Input: []tf.Input{ - tf.OutputList(in_tensors), + new_vocab_file, old_vocab_file, }, Attrs: attrs, } op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StackPushV2Attr is an optional argument to StackPushV2. +type StackPushV2Attr func(optionalAttr) + +// StackPushV2SwapMemory sets the optional swap_memory attribute to value. +// +// value: Swap `elem` to CPU. Default to false. +// If not specified, defaults to false +func StackPushV2SwapMemory(value bool) StackPushV2Attr { + return func(m optionalAttr) { + m["swap_memory"] = value + } +} + +// Push an element onto the stack. +// +// Arguments: +// handle: The handle to a stack. +// elem: The tensor to be pushed onto the stack. +// +// Returns The same tensor as the input 'elem'. +func StackPushV2(scope *Scope, handle tf.Output, elem tf.Output, optional ...StackPushV2Attr) (output tf.Output) { if scope.Err() != nil { return } - var idx int - var err error - if batched_tensors, idx, err = makeOutputList(op, idx, "batched_tensors"); err != nil { - scope.UpdateErr("Batch", err) + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StackPushV2", + Input: []tf.Input{ + handle, elem, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StackV2Attr is an optional argument to StackV2. +type StackV2Attr func(optionalAttr) + +// StackV2StackName sets the optional stack_name attribute to value. +// +// value: Overrides the name used for the temporary stack resource. Default +// value is the name of the 'Stack' op (which is guaranteed unique). +// If not specified, defaults to "" +func StackV2StackName(value string) StackV2Attr { + return func(m optionalAttr) { + m["stack_name"] = value + } +} + +// A stack that produces elements in first-in last-out order. +// +// Arguments: +// max_size: The maximum size of the stack if non-negative. If negative, the stack +// size is unlimited. +// elem_type: The type of the elements on the stack. +// +// Returns The handle to the stack. +func StackV2(scope *Scope, max_size tf.Output, elem_type tf.DataType, optional ...StackV2Attr) (handle tf.Output) { + if scope.Err() != nil { return } - batch_index = op.Output(idx) - id = op.Output(idx) - return batched_tensors, batch_index, id + attrs := map[string]interface{}{"elem_type": elem_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StackV2", + Input: []tf.Input{ + max_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) } // Computes the number of elements in the given queue. @@ -6611,59 +7223,6 @@ func QueueSizeV2(scope *Scope, handle tf.Output) (size tf.Output) { return op.Output(0) } -// BoostedTreesCalculateBestFeatureSplitAttr is an optional argument to BoostedTreesCalculateBestFeatureSplit. -type BoostedTreesCalculateBestFeatureSplitAttr func(optionalAttr) - -// BoostedTreesCalculateBestFeatureSplitSplitType sets the optional split_type attribute to value. -// -// value: A string indicating if this Op should perform inequality split or equality split. -// If not specified, defaults to "inequality" -func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCalculateBestFeatureSplitAttr { - return func(m optionalAttr) { - m["split_type"] = value - } -} - -// Calculates gains for each feature and returns the best possible split information for the feature. -// -// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. -// -// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. -// -// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). -// -// The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature. -// -// Arguments: -// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). -// stats_summary: A Rank 4 tensor (#shape=[max_splits, feature_dims, bucket, stats_dims]) for accumulated stats summary (gradient/hessian) per node, per dimension, per buckets for each feature. -// The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used. -// l1: l1 regularization factor on leaf weights, per instance based. -// l2: l2 regularization factor on leaf weights, per instance based. -// tree_complexity: adjustment to the gain, per leaf based. -// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting. -// logits_dimension: The dimension of logit, i.e., number of classes. -// -// Returns A Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes.A Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes.A Rank 1 tensors indicating the best feature dimension for each feature to split for certain nodes if the feature is multi-dimension. See above for details like shapes and sizes.A Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes.A Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes.A Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node.A Rank 1 tensors indicating the which direction to go if data is missing. See above for details like shapes and sizes. -func BoostedTreesCalculateBestFeatureSplit(scope *Scope, node_id_range tf.Output, stats_summary tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, logits_dimension int64, optional ...BoostedTreesCalculateBestFeatureSplitAttr) (node_ids tf.Output, gains tf.Output, feature_dimensions tf.Output, thresholds tf.Output, left_node_contribs tf.Output, right_node_contribs tf.Output, split_with_default_directions tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"logits_dimension": logits_dimension} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BoostedTreesCalculateBestFeatureSplit", - Input: []tf.Input{ - node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) -} - // Returns true if queue is closed. // // This operation returns true if the queue is closed and false if the queue @@ -6729,23 +7288,6 @@ func QueueCloseV2(scope *Scope, handle tf.Output, optional ...QueueCloseV2Attr) return scope.AddOperation(opspec) } -// Return the reduction indices for computing gradients of s0 op s1 with broadcast. -// -// This is typically used by gradient computations for a broadcasting operation. -func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BroadcastGradientArgs", - Input: []tf.Input{ - s0, s1, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - // QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2. type QueueDequeueUpToV2Attr func(optionalAttr) @@ -6880,166 +7422,227 @@ func QueueDequeueManyV2(scope *Scope, handle tf.Output, n tf.Output, component_t return components } -// Subtracts sparse `updates` from an existing tensor according to `indices`. +// QueueDequeueV2Attr is an optional argument to QueueDequeueV2. +type QueueDequeueV2Attr func(optionalAttr) + +// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value. // -// This operation creates a new tensor by subtracting sparse `updates` from the -// passed in `tensor`. -// This operation is very similar to `tf.scatter_nd_sub`, except that the updates -// are subtracted from an existing tensor (as opposed to a variable). If the memory -// for the existing tensor cannot be re-used, a copy is made and updated. +// value: If the queue is empty, this operation will block for up to +// timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Dequeues a tuple of one or more tensors from the given queue. // -// `indices` is an integer tensor containing indices into a new tensor of shape -// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// This operation has k outputs, where k is the number of components +// in the tuples stored in the given queue, and output i is the ith +// component of the dequeued tuple. // -// indices.shape[-1] <= shape.rank -// -// The last dimension of `indices` corresponds to indices into elements -// (if `indices.shape[-1] = shape.rank`) or slices -// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of -// `shape`. `updates` is a tensor with shape -// -// indices.shape[:-1] + shape[indices.shape[-1]:] -// -// The simplest form of tensor_scatter_sub is to subtract individual elements -// from a tensor by index. For example, say we want to insert 4 scattered elements -// in a rank-1 tensor with 8 elements. -// -// In Python, this scatter subtract operation would look like this: -// -// ```python -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// tensor = tf.ones([8], dtype=tf.int32) -// updated = tf.tensor_scatter_sub(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [1, -10, 1, -9, -8, 1, 1, -11] -// -// We can also, insert entire slices of a higher rank tensor all at once. For -// example, if we wanted to insert two slices in the first dimension of a -// rank-3 tensor with two matrices of new values. -// -// In Python, this scatter add operation would look like this: -// -// ```python -// indices = tf.constant([[0], [2]]) -// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]], -// [[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]]]) -// tensor = tf.ones([4, 4, 4]) -// updated = tf.tensor_scatter_sub(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], -// [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] -// -// Note that on CPU, if an out of bound index is found, an error is returned. -// On GPU, if an out of bound index is found, the index is ignored. +// N.B. If the queue is empty, this operation will block until an element +// has been dequeued (or 'timeout_ms' elapses, if specified). // // Arguments: -// tensor: Tensor to copy/update. -// indices: Index tensor. -// updates: Updates to scatter into output. +// handle: The handle to a queue. +// component_types: The type of each component in a tuple. // -// Returns A new tensor copied from tensor and updates subtracted according to the indices. -func TensorScatterSub(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { +// Returns One or more tensors that were dequeued as a tuple. +func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorScatterSub", + Type: "QueueDequeueV2", Input: []tf.Input{ - tensor, indices, updates, + handle, }, + Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("QueueDequeueV2", err) + return + } + return components } -// PriorityQueueV2Attr is an optional argument to PriorityQueueV2. -type PriorityQueueV2Attr func(optionalAttr) +// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2. +type QueueEnqueueManyV2Attr func(optionalAttr) -// PriorityQueueV2ComponentTypes sets the optional component_types attribute to value. +// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value. // -// value: The type of each component in a value. +// value: If the queue is too full, this operation will block for up +// to timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Enqueues zero or more tuples of one or more tensors in the given queue. +// +// This operation slices each component tensor along the 0th dimension to +// make multiple queue elements. All of the tuple components must have the +// same size in the 0th dimension. +// +// The components input has k elements, which correspond to the components of +// tuples stored in the given queue. +// +// N.B. If the queue is full, this operation will block until the given +// elements have been enqueued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// components: One or more tensors from which the enqueued tensors should +// be taken. +// +// Returns the created operation. +func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueEnqueueManyV2", + Input: []tf.Input{ + handle, tf.OutputList(components), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QueueEnqueueV2Attr is an optional argument to QueueEnqueueV2. +type QueueEnqueueV2Attr func(optionalAttr) + +// QueueEnqueueV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue is full, this operation will block for up to +// timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueEnqueueV2TimeoutMs(value int64) QueueEnqueueV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Enqueues a tuple of one or more tensors in the given queue. +// +// The components input has k elements, which correspond to the components of +// tuples stored in the given queue. +// +// N.B. If the queue is full, this operation will block until the given +// element has been enqueued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// components: One or more tensors from which the enqueued tensors should be taken. +// +// Returns the created operation. +func QueueEnqueueV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueEnqueueV2", + Input: []tf.Input{ + handle, tf.OutputList(components), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// FIFOQueueV2Attr is an optional argument to FIFOQueueV2. +type FIFOQueueV2Attr func(optionalAttr) + +// FIFOQueueV2Shapes sets the optional shapes attribute to value. +// +// value: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. If the length of +// this attr is 0, the shapes of queue elements are not constrained, and +// only one element may be dequeued at a time. // If not specified, defaults to <> // // REQUIRES: len(value) >= 0 -func PriorityQueueV2ComponentTypes(value []tf.DataType) PriorityQueueV2Attr { +func FIFOQueueV2Shapes(value []tf.Shape) FIFOQueueV2Attr { return func(m optionalAttr) { - m["component_types"] = value + m["shapes"] = value } } -// PriorityQueueV2Capacity sets the optional capacity attribute to value. +// FIFOQueueV2Capacity sets the optional capacity attribute to value. // // value: The upper bound on the number of elements in this queue. // Negative numbers mean no limit. // If not specified, defaults to -1 -func PriorityQueueV2Capacity(value int64) PriorityQueueV2Attr { +func FIFOQueueV2Capacity(value int64) FIFOQueueV2Attr { return func(m optionalAttr) { m["capacity"] = value } } -// PriorityQueueV2Container sets the optional container attribute to value. +// FIFOQueueV2Container sets the optional container attribute to value. // // value: If non-empty, this queue is placed in the given container. // Otherwise, a default container is used. // If not specified, defaults to "" -func PriorityQueueV2Container(value string) PriorityQueueV2Attr { +func FIFOQueueV2Container(value string) FIFOQueueV2Attr { return func(m optionalAttr) { m["container"] = value } } -// PriorityQueueV2SharedName sets the optional shared_name attribute to value. +// FIFOQueueV2SharedName sets the optional shared_name attribute to value. // // value: If non-empty, this queue will be shared under the given name // across multiple sessions. // If not specified, defaults to "" -func PriorityQueueV2SharedName(value string) PriorityQueueV2Attr { +func FIFOQueueV2SharedName(value string) FIFOQueueV2Attr { return func(m optionalAttr) { m["shared_name"] = value } } -// A queue that produces elements sorted by the first component value. -// -// Note that the PriorityQueue requires the first component of any element -// to be a scalar int64, in addition to the other elements declared by -// component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue -// and DequeueMany) on a PriorityQueue will all require (resp. output) one extra -// entry in their input (resp. output) lists. +// A queue that produces elements in first-in first-out order. // // Arguments: -// shapes: The shape of each component in a value. The length of this attr must -// be either 0 or the same as the length of component_types. If the length of -// this attr is 0, the shapes of queue elements are not constrained, and -// only one element may be dequeued at a time. +// component_types: The type of each component in a value. // // Returns The handle to the queue. -func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV2Attr) (handle tf.Output) { +func FIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...FIFOQueueV2Attr) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"shapes": shapes} + attrs := map[string]interface{}{"component_types": component_types} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "PriorityQueueV2", + Type: "FIFOQueueV2", Attrs: attrs, } @@ -7231,298 +7834,59 @@ func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) return op.Output(0) } -// UniqueWithCountsAttr is an optional argument to UniqueWithCounts. -type UniqueWithCountsAttr func(optionalAttr) - -// UniqueWithCountsOutIdx sets the optional out_idx attribute to value. -// If not specified, defaults to DT_INT32 -func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr { - return func(m optionalAttr) { - m["out_idx"] = value - } -} - -// Finds unique elements in a 1-D tensor. +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. // -// This operation returns a tensor `y` containing all of the unique elements of `x` -// sorted in the same order that they occur in `x`. This operation also returns a -// tensor `idx` the same size as `x` that contains the index of each value of `x` -// in the unique output `y`. Finally, it returns a third tensor `count` that -// contains the count of each element of `y` in `x`. In other words: -// -// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` -// -// For example: -// -// ``` -// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] -// y, idx, count = unique_with_counts(x) -// y ==> [1, 2, 4, 7, 8] -// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] -// count ==> [2, 1, 3, 1, 2] -// ``` -// -// Arguments: -// x: 1-D. -// -// Returns 1-D.1-D.1-D. -func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UniqueWithCounts", - Input: []tf.Input{ - x, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Interleave the values from the `data` tensors into a single tensor. -// -// Builds a merged tensor such that +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, // // ```python -// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] +// +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) // ``` // -// For example, if each `indices[m]` is scalar or vector, we have -// -// ```python -// # Scalar indices: -// merged[indices[m], ...] = data[m][...] -// -// # Vector indices: -// merged[indices[m][i], ...] = data[m][i, ...] -// ``` -// -// Each `data[i].shape` must start with the corresponding `indices[i].shape`, -// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we -// must have `data[i].shape = indices[i].shape + constant`. In terms of this -// `constant`, the output shape is -// -// merged.shape = [max(indices)] + constant -// -// Values are merged in order, so if an index appears in both `indices[m][i]` and -// `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the -// merged result. If you do not need this guarantee, ParallelDynamicStitch might -// perform better on some devices. +// `data.shape` must start with `partitions.shape`. // // For example: // // ```python -// indices[0] = 6 -// indices[1] = [4, 1] -// indices[2] = [[5, 2], [0, 3]] -// data[0] = [61, 62] -// data[1] = [[41, 42], [11, 12]] -// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] -// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], -// [51, 52], [61, 62]] +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] // ``` // -// This method can be used to merge partitions created by `dynamic_partition` -// as illustrated on the following example: -// -// ```python -// # Apply function (increments x_i) on elements for which a certain condition -// # apply (x_i != -1 in this example). -// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4]) -// condition_mask=tf.not_equal(x,tf.constant(-1.)) -// partitioned_data = tf.dynamic_partition( -// x, tf.cast(condition_mask, tf.int32) , 2) -// partitioned_data[1] = partitioned_data[1] + 1.0 -// condition_indices = tf.dynamic_partition( -// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2) -// x = tf.dynamic_stitch(condition_indices, partitioned_data) -// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain -// # unchanged. -// ``` +// See `dynamic_stitch` for an example on how to merge partitions back. // //
-// +// //
-func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "DynamicStitch", - Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(data), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reverses specific dimensions of a tensor. -// -// NOTE `tf.reverse` has now changed behavior in preparation for 1.0. -// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. -// -// Given a `tensor`, and a `int32` tensor `axis` representing the set of -// dimensions of `tensor` to reverse. This operation reverses each dimension -// `i` for which there exists `j` s.t. `axis[j] == i`. -// -// `tensor` can have up to 8 dimensions. The number of dimensions specified -// in `axis` may be 0 or more entries. If an index is specified more than -// once, a InvalidArgument error is raised. -// -// For example: -// -// ``` -// # tensor 't' is [[[[ 0, 1, 2, 3], -// # [ 4, 5, 6, 7], -// # [ 8, 9, 10, 11]], -// # [[12, 13, 14, 15], -// # [16, 17, 18, 19], -// # [20, 21, 22, 23]]]] -// # tensor 't' shape is [1, 2, 3, 4] -// -// # 'dims' is [3] or 'dims' is [-1] -// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], -// [ 7, 6, 5, 4], -// [ 11, 10, 9, 8]], -// [[15, 14, 13, 12], -// [19, 18, 17, 16], -// [23, 22, 21, 20]]]] -// -// # 'dims' is '[1]' (or 'dims' is '[-3]') -// reverse(t, dims) ==> [[[[12, 13, 14, 15], -// [16, 17, 18, 19], -// [20, 21, 22, 23] -// [[ 0, 1, 2, 3], -// [ 4, 5, 6, 7], -// [ 8, 9, 10, 11]]]] -// -// # 'dims' is '[2]' (or 'dims' is '[-2]') -// reverse(t, dims) ==> [[[[8, 9, 10, 11], -// [4, 5, 6, 7], -// [0, 1, 2, 3]] -// [[20, 21, 22, 23], -// [16, 17, 18, 19], -// [12, 13, 14, 15]]]] -// ``` // // Arguments: -// tensor: Up to 8-D. -// axis: 1-D. The indices of the dimensions to reverse. Must be in the range -// `[-rank(tensor), rank(tensor))`. // -// Returns The same shape as `tensor`. -func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) { +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"num_partitions": num_partitions} opspec := tf.OpSpec{ - Type: "ReverseV2", + Type: "DynamicPartition", Input: []tf.Input{ - tensor, axis, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MultiDeviceIteratorFromStringHandleAttr is an optional argument to MultiDeviceIteratorFromStringHandle. -type MultiDeviceIteratorFromStringHandleAttr func(optionalAttr) - -// MultiDeviceIteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. -// -// value: The type list for the return values. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func MultiDeviceIteratorFromStringHandleOutputTypes(value []tf.DataType) MultiDeviceIteratorFromStringHandleAttr { - return func(m optionalAttr) { - m["output_types"] = value - } -} - -// MultiDeviceIteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. -// -// value: The list of shapes being produced. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func MultiDeviceIteratorFromStringHandleOutputShapes(value []tf.Shape) MultiDeviceIteratorFromStringHandleAttr { - return func(m optionalAttr) { - m["output_shapes"] = value - } -} - -// Generates a MultiDeviceIterator resource from its provided string handle. -// -// Arguments: -// string_handle: String representing the resource. -// -// Returns A MultiDeviceIterator resource. -func MultiDeviceIteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...MultiDeviceIteratorFromStringHandleAttr) (multi_device_iterator tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MultiDeviceIteratorFromStringHandle", - Input: []tf.Input{ - string_handle, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Produces a string handle for the given MultiDeviceIterator. -// -// Arguments: -// multi_device_iterator: A MultiDeviceIterator resource. -// -// Returns A string representing the resource. -func MultiDeviceIteratorToStringHandle(scope *Scope, multi_device_iterator tf.Output) (string_handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MultiDeviceIteratorToStringHandle", - Input: []tf.Input{ - multi_device_iterator, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Gets next element for the provided shard number. -// -// Arguments: -// multi_device_iterator: A MultiDeviceIterator resource. -// shard_num: Integer representing which shard to fetch data for. -// incarnation_id: Which incarnation of the MultiDeviceIterator is running. -// output_types: The type list for the return values. -// output_shapes: The list of shapes being produced. -// -// Returns Result of the get_next on the dataset. -func MultiDeviceIteratorGetNextFromShard(scope *Scope, multi_device_iterator tf.Output, shard_num tf.Output, incarnation_id tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "MultiDeviceIteratorGetNextFromShard", - Input: []tf.Input{ - multi_device_iterator, shard_num, incarnation_id, + data, partitions, }, Attrs: attrs, } @@ -7532,11 +7896,11 @@ func MultiDeviceIteratorGetNextFromShard(scope *Scope, multi_device_iterator tf. } var idx int var err error - if components, idx, err = makeOutputList(op, idx, "components"); err != nil { - scope.UpdateErr("MultiDeviceIteratorGetNextFromShard", err) + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) return } - return components + return outputs } // Initializes the multi device iterator with the given dataset. @@ -7562,51 +7926,61 @@ func MultiDeviceIteratorInit(scope *Scope, dataset tf.Output, multi_device_itera return op.Output(0) } -// Creates a MultiDeviceIterator resource. +// Generates fingerprint values. +// +// Generates fingerprint values of `data`. +// +// Fingerprint op considers the first dimension of `data` as the batch dimension, +// and `output[i]` contains the fingerprint value generated from contents in +// `data[i, ...]` for all `i`. +// +// Fingerprint op writes fingerprint values as byte arrays. For example, the +// default method `farmhash64` generates a 64-bit fingerprint value at a time. +// This 8-byte value is written out as an `uint8` array of size 8, in little-endian +// order. +// +// For example, suppose that `data` has data type `DT_INT32` and shape (2, 3, 4), +// and that the fingerprint method is `farmhash64`. In this case, the output shape +// is (2, 8), where 2 is the batch dimension size of `data`, and 8 is the size of +// each fingerprint value in bytes. `output[0, :]` is generated from 12 integers in +// `data[0, :, :]` and similarly `output[1, :]` is generated from other 12 integers +// in `data[1, :, :]`. +// +// Note that this op fingerprints the raw underlying buffer, and it does not +// fingerprint Tensor's metadata such as data type and/or shape. For example, the +// fingerprint values are invariant under reshapes and bitcasts as long as the +// batch dimension remain the same: +// +// ``` +// Fingerprint(data) == Fingerprint(Reshape(data, ...)) +// Fingerprint(data) == Fingerprint(Bitcast(data, ...)) +// ``` +// +// For string data, one should expect `Fingerprint(data) != +// Fingerprint(ReduceJoin(data))` in general. // // Arguments: -// devices: A list of devices the iterator works across. -// shared_name: If non-empty, this resource will be shared under the given name -// across multiple sessions. -// container: If non-empty, this resource is placed in the given container. -// Otherwise, a default container is used. -// output_types: The type list for the return values. -// output_shapes: The list of shapes being produced. +// data: Must have rank 1 or higher. +// method: Fingerprint method used by this op. Currently available method is +// `farmhash::fingerprint64`. // -// Returns Handle to the resource created. -func MultiDeviceIterator(scope *Scope, devices []string, shared_name string, container string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns A two-dimensional `Tensor` of type `tf.uint8`. The first dimension equals to +// `data`'s first dimension, and the second dimension size depends on the +// fingerprint algorithm. +func Fingerprint(scope *Scope, data tf.Output, method tf.Output) (fingerprint tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"devices": devices, "shared_name": shared_name, "container": container, "output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "MultiDeviceIterator", - - Attrs: attrs, + Type: "Fingerprint", + Input: []tf.Input{ + data, method, + }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Delete the tensor specified by its handle in the session. -// -// Arguments: -// handle: The handle for a tensor stored in the session state. -// -// Returns the created operation. -func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "DeleteSessionTensor", - Input: []tf.Input{ - handle, - }, - } - return scope.AddOperation(opspec) -} - // ModelDatasetAttr is an optional argument to ModelDataset. type ModelDatasetAttr func(optionalAttr) @@ -7645,47 +8019,6 @@ func ModelDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataT return op.Output(0) } -// Broadcast an array for a compatible shape. -// -// Broadcasting is the process of making arrays to have compatible shapes -// for arithmetic operations. Two shapes are compatible if for each -// dimension pair they are either equal or one of them is one. When trying -// to broadcast a Tensor to a shape, it starts with the trailing dimensions, -// and works its way forward. -// -// For example, -// -// ```python -// >>> x = tf.constant([1, 2, 3]) -// >>> y = tf.broadcast_to(x, [3, 3]) -// >>> sess.run(y) -// array([[1, 2, 3], -// [1, 2, 3], -// [1, 2, 3]], dtype=int32) -// ``` -// -// In the above example, the input Tensor with the shape of `[1, 3]` -// is broadcasted to output Tensor with shape of `[3, 3]`. -// -// Arguments: -// input: A Tensor to broadcast. -// shape: An 1-D `int` Tensor. The shape of the desired output. -// -// Returns A Tensor. -func BroadcastTo(scope *Scope, input tf.Output, shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BroadcastTo", - Input: []tf.Input{ - input, shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Gets the next output from the given iterator as an Optional variant. func IteratorGetNextAsOptional(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (optional tf.Output) { if scope.Err() != nil { @@ -7703,107 +8036,6 @@ func IteratorGetNextAsOptional(scope *Scope, iterator tf.Output, output_types [] return op.Output(0) } -// Creates an Optional variant with no value. -func OptionalNone(scope *Scope) (optional tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OptionalNone", - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Constructs an Optional variant from a tuple of tensors. -func OptionalFromValue(scope *Scope, components []tf.Output) (optional tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OptionalFromValue", - Input: []tf.Input{ - tf.OutputList(components), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FIFOQueueV2Attr is an optional argument to FIFOQueueV2. -type FIFOQueueV2Attr func(optionalAttr) - -// FIFOQueueV2Shapes sets the optional shapes attribute to value. -// -// value: The shape of each component in a value. The length of this attr must -// be either 0 or the same as the length of component_types. If the length of -// this attr is 0, the shapes of queue elements are not constrained, and -// only one element may be dequeued at a time. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func FIFOQueueV2Shapes(value []tf.Shape) FIFOQueueV2Attr { - return func(m optionalAttr) { - m["shapes"] = value - } -} - -// FIFOQueueV2Capacity sets the optional capacity attribute to value. -// -// value: The upper bound on the number of elements in this queue. -// Negative numbers mean no limit. -// If not specified, defaults to -1 -func FIFOQueueV2Capacity(value int64) FIFOQueueV2Attr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// FIFOQueueV2Container sets the optional container attribute to value. -// -// value: If non-empty, this queue is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func FIFOQueueV2Container(value string) FIFOQueueV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// FIFOQueueV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this queue will be shared under the given name -// across multiple sessions. -// If not specified, defaults to "" -func FIFOQueueV2SharedName(value string) FIFOQueueV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// A queue that produces elements in first-in first-out order. -// -// Arguments: -// component_types: The type of each component in a value. -// -// Returns The handle to the queue. -func FIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...FIFOQueueV2Attr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"component_types": component_types} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FIFOQueueV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // OptimizeDatasetAttr is an optional argument to OptimizeDataset. type OptimizeDatasetAttr func(optionalAttr) @@ -7886,96 +8118,19 @@ func DeserializeIterator(scope *Scope, resource_handle tf.Output, serialized tf. return scope.AddOperation(opspec) } -// Subtracts `v` into specified rows of `x`. -// -// Computes y = x; y[i, :] -= v; return y. -// -// Arguments: -// x: A `Tensor` of type T. -// i: A vector. Indices into the left-most dimension of `x`. -// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. -// -// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. -func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "InplaceSub", - Input: []tf.Input{ - x, i, v, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// IteratorFromStringHandleAttr is an optional argument to IteratorFromStringHandle. -type IteratorFromStringHandleAttr func(optionalAttr) - -// IteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. -// -// value: If specified, defines the type of each tuple component in an -// element produced by the resulting iterator. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func IteratorFromStringHandleOutputTypes(value []tf.DataType) IteratorFromStringHandleAttr { - return func(m optionalAttr) { - m["output_types"] = value - } -} - -// IteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. -// -// value: If specified, defines the shape of each tuple component in an -// element produced by the resulting iterator. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func IteratorFromStringHandleOutputShapes(value []tf.Shape) IteratorFromStringHandleAttr { - return func(m optionalAttr) { - m["output_shapes"] = value - } -} - -// Converts the given string representing a handle to an iterator to a resource. -// -// Arguments: -// string_handle: A string representation of the given handle. -// -// Returns A handle to an iterator resource. -func IteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...IteratorFromStringHandleAttr) (resource_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "IteratorFromStringHandle", - Input: []tf.Input{ - string_handle, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts the given `resource_handle` representing an iterator to a string. +// Converts the given `resource_handle` representing an iterator to a variant tensor. // // Arguments: // resource_handle: A handle to an iterator resource. // -// Returns A string representation of the given handle. -func IteratorToStringHandle(scope *Scope, resource_handle tf.Output) (string_handle tf.Output) { +// Returns A variant tensor storing the state of the iterator contained in the +// resource. +func SerializeIterator(scope *Scope, resource_handle tf.Output) (serialized tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "IteratorToStringHandle", + Type: "SerializeIterator", Input: []tf.Input{ resource_handle, }, @@ -8015,6 +8170,23 @@ func IteratorGetNextSync(scope *Scope, iterator tf.Output, output_types []tf.Dat return components } +// Mutually accumulates multiple tensors of identical type and shape. +func CollectiveGather(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + opspec := tf.OpSpec{ + Type: "CollectiveGather", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Gets the next output from the given iterator . func IteratorGetNext(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { if scope.Err() != nil { @@ -8041,24 +8213,23 @@ func IteratorGetNext(scope *Scope, iterator tf.Output, output_types []tf.DataTyp return components } -// A container for an iterator resource. +// Makes a new iterator from the given `dataset` and stores it in `iterator`. // -// Returns A handle to the iterator that can be passed to a "MakeIterator" or -// "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents -// resource sharing by name, and does not keep a reference to the resource -// container.A variant deleter that should be passed into the op that deletes the iterator. -func AnonymousIteratorV2(scope *Scope, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output, deleter tf.Output) { +// This operation may be executed multiple times. Each execution will reset the +// iterator in `iterator` to the first element of `dataset`. +// +// Returns the created operation. +func MakeIterator(scope *Scope, dataset tf.Output, iterator tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "AnonymousIteratorV2", - - Attrs: attrs, + Type: "MakeIterator", + Input: []tf.Input{ + dataset, iterator, + }, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return scope.AddOperation(opspec) } // A container for an iterator resource. @@ -8081,6 +8252,1151 @@ func AnonymousIterator(scope *Scope, output_types []tf.DataType, output_shapes [ return op.Output(0) } +// A container for an iterator resource. +// +// Returns A handle to the iterator that can be passed to a "MakeIterator" +// or "IteratorGetNext" op. +func Iterator(scope *Scope, shared_name string, container string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shared_name": shared_name, "container": container, "output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "Iterator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the records from one or more TFRecord files. +// +// Arguments: +// filenames: A scalar or vector containing the name(s) of the file(s) to be +// read. +// compression_type: A scalar containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// buffer_size: A scalar representing the number of bytes to buffer. A value of +// 0 means no buffering will be performed. +func TFRecordDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TFRecordDataset", + Input: []tf.Input{ + filenames, compression_type, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the records from one or more binary files. +// +// Arguments: +// filenames: A scalar or a vector containing the name(s) of the file(s) to be +// read. +// header_bytes: A scalar representing the number of bytes to skip at the +// beginning of a file. +// record_bytes: A scalar representing the number of bytes in each record. +// footer_bytes: A scalar representing the number of bytes to skip at the end +// of a file. +// buffer_size: A scalar representing the number of bytes to buffer. Must be > 0. +func FixedLengthRecordDataset(scope *Scope, filenames tf.Output, header_bytes tf.Output, record_bytes tf.Output, footer_bytes tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FixedLengthRecordDataset", + Input: []tf.Input{ + filenames, header_bytes, record_bytes, footer_bytes, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates an Optional variant with no value. +func OptionalNone(scope *Scope) (optional tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalNone", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ShuffleDatasetAttr is an optional argument to ShuffleDataset. +type ShuffleDatasetAttr func(optionalAttr) + +// ShuffleDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value. +// +// value: If true, each iterator over this dataset will be given +// a different pseudorandomly generated seed, based on a sequence seeded by the +// `seed` and `seed2` inputs. If false, each iterator will be given the same +// seed, and repeated iteration over this dataset will yield the exact same +// sequence of results. +// If not specified, defaults to true +func ShuffleDatasetReshuffleEachIteration(value bool) ShuffleDatasetAttr { + return func(m optionalAttr) { + m["reshuffle_each_iteration"] = value + } +} + +// Creates a dataset that shuffles elements from `input_dataset` pseudorandomly. +// +// Arguments: +// +// buffer_size: The number of output elements to buffer in an iterator over +// this dataset. Compare with the `min_after_dequeue` attr when creating a +// `RandomShuffleQueue`. +// seed: A scalar seed for the random number generator. If either `seed` or +// `seed2` is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// +// +func ShuffleDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShuffleDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShuffleDataset", + Input: []tf.Input{ + input_dataset, buffer_size, seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset with a range of values. Corresponds to python's xrange. +// +// Arguments: +// start: corresponds to start in python's xrange(). +// stop: corresponds to stop in python's xrange(). +// step: corresponds to step in python's xrange(). +// +// +func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "RangeDataset", + Input: []tf.Input{ + start, stop, step, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that batches and pads `batch_size` elements from the input. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// padded_shapes: A list of int64 tensors representing the desired padded shapes +// of the corresponding output components. These shapes may be partially +// specified, using `-1` to indicate that a particular dimension should be +// padded to the maximum size of all batch elements. +// padding_values: A list of scalars containing the padding value to use for +// each of the outputs. +// +func PaddedBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "PaddedBatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesQuantileStreamResourceFlushAttr is an optional argument to BoostedTreesQuantileStreamResourceFlush. +type BoostedTreesQuantileStreamResourceFlushAttr func(optionalAttr) + +// BoostedTreesQuantileStreamResourceFlushGenerateQuantiles sets the optional generate_quantiles attribute to value. +// +// value: bool; If True, the output will be the num_quantiles for each stream where the ith +// entry is the ith quantile of the input with an approximation error of epsilon. +// Duplicate values may be present. +// If False, the output will be the points in the histogram that we got which roughly +// translates to 1/epsilon boundaries and without any duplicates. +// Default to False. +// If not specified, defaults to false +func BoostedTreesQuantileStreamResourceFlushGenerateQuantiles(value bool) BoostedTreesQuantileStreamResourceFlushAttr { + return func(m optionalAttr) { + m["generate_quantiles"] = value + } +} + +// Flush the summaries for a quantile stream resource. +// +// An op that flushes the summaries for a quantile stream resource. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// num_buckets: int; approximate number of buckets unless using generate_quantiles. +// +// Returns the created operation. +func BoostedTreesQuantileStreamResourceFlush(scope *Scope, quantile_stream_resource_handle tf.Output, num_buckets tf.Output, optional ...BoostedTreesQuantileStreamResourceFlushAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceFlush", + Input: []tf.Input{ + quantile_stream_resource_handle, num_buckets, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// BatchDatasetV2Attr is an optional argument to BatchDatasetV2. +type BatchDatasetV2Attr func(optionalAttr) + +// BatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. +// If not specified, defaults to false +func BatchDatasetV2ParallelCopy(value bool) BatchDatasetV2Attr { + return func(m optionalAttr) { + m["parallel_copy"] = value + } +} + +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a batch. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +// +func BatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...BatchDatasetV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapIncompleteSizeAttr is an optional argument to MapIncompleteSize. +type MapIncompleteSizeAttr func(optionalAttr) + +// MapIncompleteSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapIncompleteSizeCapacity(value int64) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapIncompleteSizeMemoryLimit(value int64) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapIncompleteSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapIncompleteSizeContainer(value string) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapIncompleteSizeSharedName(value string) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of incomplete elements in the underlying container. +func MapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...MapIncompleteSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapIncompleteSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// +// +func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "BatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesCalculateBestFeatureSplitAttr is an optional argument to BoostedTreesCalculateBestFeatureSplit. +type BoostedTreesCalculateBestFeatureSplitAttr func(optionalAttr) + +// BoostedTreesCalculateBestFeatureSplitSplitType sets the optional split_type attribute to value. +// +// value: A string indicating if this Op should perform inequality split or equality split. +// If not specified, defaults to "inequality" +func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCalculateBestFeatureSplitAttr { + return func(m optionalAttr) { + m["split_type"] = value + } +} + +// Calculates gains for each feature and returns the best possible split information for the feature. +// +// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. +// +// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. +// +// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). +// +// The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature. +// +// Arguments: +// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). +// stats_summary: A Rank 4 tensor (#shape=[max_splits, feature_dims, bucket, stats_dims]) for accumulated stats summary (gradient/hessian) per node, per dimension, per buckets for each feature. +// The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// tree_complexity: adjustment to the gain, per leaf based. +// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting. +// logits_dimension: The dimension of logit, i.e., number of classes. +// +// Returns A Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes.A Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes.A Rank 1 tensors indicating the best feature dimension for each feature to split for certain nodes if the feature is multi-dimension. See above for details like shapes and sizes.A Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes.A Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes.A Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node.A Rank 1 tensors indicating the which direction to go if data is missing. See above for details like shapes and sizes. +func BoostedTreesCalculateBestFeatureSplit(scope *Scope, node_id_range tf.Output, stats_summary tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, logits_dimension int64, optional ...BoostedTreesCalculateBestFeatureSplitAttr) (node_ids tf.Output, gains tf.Output, feature_dimensions tf.Output, thresholds tf.Output, left_node_contribs tf.Output, right_node_contribs tf.Output, split_with_default_directions tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCalculateBestFeatureSplit", + Input: []tf.Input{ + node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// PrefetchDatasetAttr is an optional argument to PrefetchDataset. +type PrefetchDatasetAttr func(optionalAttr) + +// PrefetchDatasetSlackPeriod sets the optional slack_period attribute to value. +// If not specified, defaults to 0 +func PrefetchDatasetSlackPeriod(value int64) PrefetchDatasetAttr { + return func(m optionalAttr) { + m["slack_period"] = value + } +} + +// Creates a dataset that asynchronously prefetches elements from `input_dataset`. +// +// Arguments: +// +// buffer_size: The maximum number of elements to buffer in an iterator over +// this dataset. +// +// +func PrefetchDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...PrefetchDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PrefetchDataset", + Input: []tf.Input{ + input_dataset, buffer_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Quantized Batch normalization. +// +// This op is deprecated and will be removed in the future. Prefer +// `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// t_min: The value represented by the lowest quantized input. +// t_max: The value represented by the highest quantized input. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// m_min: The value represented by the lowest quantized mean. +// m_max: The value represented by the highest quantized mean. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// v_min: The value represented by the lowest quantized variance. +// v_max: The value represented by the highest quantized variance. +// beta: A 1D beta Tensor with size matching the last dimension of t. +// An offset to be added to the normalized tensor. +// beta_min: The value represented by the lowest quantized offset. +// beta_max: The value represented by the highest quantized offset. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this tensor will be multiplied +// with the normalized tensor. +// gamma_min: The value represented by the lowest quantized gamma. +// gamma_max: The value represented by the highest quantized gamma. +// +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "QuantizedBatchNormWithGlobalNormalization", + Input: []tf.Input{ + t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizedReluAttr is an optional argument to QuantizedRelu. +type QuantizedReluAttr func(optionalAttr) + +// QuantizedReluOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear: `max(features, 0)` +// +// Arguments: +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. +func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedRelu", + Input: []tf.Input{ + features, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Fills empty rows in the input 2-D `SparseTensor` with a default value. +// +// The input `SparseTensor` is represented via the tuple of inputs +// (`indices`, `values`, `dense_shape`). The output `SparseTensor` has the +// same `dense_shape` but with indices `output_indices` and values +// `output_values`. +// +// This op inserts a single entry for every row that doesn't have any values. +// The index is created as `[row, 0, ..., 0]` and the inserted value +// is `default_value`. +// +// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values: +// +// [0, 1]: a +// [0, 3]: b +// [2, 0]: c +// [3, 1]: d +// +// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values: +// +// [0, 1]: a +// [0, 3]: b +// [1, 0]: default_value +// [2, 0]: c +// [3, 1]: d +// [4, 0]: default_value +// +// The output `SparseTensor` will be in row-major order and will have the +// same shape as the input. +// +// This op also returns an indicator vector shaped `[dense_shape[0]]` such that +// +// empty_row_indicator[i] = True iff row i was an empty row. +// +// And a reverse index map vector shaped `[indices.shape[0]]` that is used during +// backpropagation, +// +// reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :] +// +// Arguments: +// indices: 2-D. the indices of the sparse tensor. +// values: 1-D. the values of the sparse tensor. +// dense_shape: 1-D. the shape of the sparse tensor. +// default_value: 0-D. default value to insert into location `[row, 0, ..., 0]` +// for rows missing from the input sparse tensor. +// output indices: 2-D. the indices of the filled sparse tensor. +// +// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the +// input sparse tensor.1-D. a map from the input indices to the output indices. +func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseFillEmptyRows", + Input: []tf.Input{ + indices, values, dense_shape, default_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// StringLengthAttr is an optional argument to StringLength. +type StringLengthAttr func(optionalAttr) + +// StringLengthUnit sets the optional unit attribute to value. +// +// value: The unit that is counted to compute string length. One of: `"BYTE"` (for +// the number of bytes in each string) or `"UTF8_CHAR"` (for the number of UTF-8 +// encoded Unicode code points in each string). Results are undefined +// if `unit=UTF8_CHAR` and the `input` strings do not contain structurally +// valid UTF-8. +// If not specified, defaults to "BYTE" +func StringLengthUnit(value string) StringLengthAttr { + return func(m optionalAttr) { + m["unit"] = value + } +} + +// String lengths of `input`. +// +// Computes the length of each string given in the input tensor. +// +// Arguments: +// input: The string for which to compute the length. +// +// Returns Integer tensor that has the same shape as `input`. The output contains the +// element-wise string lengths of `input`. +func StringLength(scope *Scope, input tf.Output, optional ...StringLengthAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringLength", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Delete the stack from its resource container. +// +// Arguments: +// handle: The handle to a stack. +// +// Returns the created operation. +func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StackCloseV2", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool. +type FractionalAvgPoolAttr func(optionalAttr) + +// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value. +// +// value: When set to True, generates the pooling sequence in a +// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin +// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for +// difference between pseudorandom and random. +// If not specified, defaults to false +func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["pseudo_random"] = value + } +} + +// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [41/3, 26/3] for fractional avg pooling. +// If not specified, defaults to false +func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value. +// +// value: When set to True, a fixed pooling region will be used when +// iterating over a FractionalAvgPool node in the computation graph. Mainly used +// in unit test to make FractionalAvgPool deterministic. +// If not specified, defaults to false +func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["deterministic"] = value + } +} + +// FractionalAvgPoolSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Performs fractional average pooling on the input. +// +// Fractional average pooling is similar to Fractional max pooling in the pooling +// region generation step. The only difference is that after pooling regions are +// generated, a mean operation is performed instead of a max operation in each +// pooling region. +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// pooling_ratio: Pooling ratio for each dimension of `value`, currently only +// supports row and col dimension and should be >= 1.0. For example, a valid +// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements +// must be 1.0 because we don't allow pooling on batch and channels +// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions +// respectively. +// +// Returns output tensor after fractional avg pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient. +func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalAvgPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad. +type FractionalMaxPoolGradAttr func(optionalAttr) + +// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [20, 16] for fractional max pooling. +// If not specified, defaults to false +func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// Computes gradient of the FractionalMaxPool function. +// +// Arguments: +// orig_input: Original input for `fractional_max_pool` +// orig_output: Original output for `fractional_max_pool` +// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients +// w.r.t. the output of `fractional_max_pool`. +// row_pooling_sequence: row pooling sequence, form pooling region with +// col_pooling_sequence. +// col_pooling_sequence: column pooling sequence, form pooling region with +// row_pooling sequence. +// +// Returns 4-D. Gradients w.r.t. the input of `fractional_max_pool`. +func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalMaxPoolGrad", + Input: []tf.Input{ + orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool. +type FractionalMaxPoolAttr func(optionalAttr) + +// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value. +// +// value: When set to True, generates the pooling sequence in a +// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin +// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for +// difference between pseudorandom and random. +// If not specified, defaults to false +func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["pseudo_random"] = value + } +} + +// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [20, 16] for fractional max pooling. +// If not specified, defaults to false +func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value. +// +// value: When set to True, a fixed pooling region will be used when +// iterating over a FractionalMaxPool node in the computation graph. Mainly used +// in unit test to make FractionalMaxPool deterministic. +// If not specified, defaults to false +func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["deterministic"] = value + } +} + +// FractionalMaxPoolSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// FractionalMaxPoolSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Performs fractional max pooling on the input. +// +// Fractional max pooling is slightly different than regular max pooling. In +// regular max pooling, you downsize an input set by taking the maximum value of +// smaller N x N subsections of the set (often 2x2), and try to reduce the set by +// a factor of N, where N is an integer. Fractional max pooling, as you might +// expect from the word "fractional", means that the overall reduction ratio N +// does not have to be an integer. +// +// The sizes of the pooling regions are generated randomly but are fairly uniform. +// For example, let's look at the height dimension, and the constraints on the +// list of rows that will be pool boundaries. +// +// First we define the following: +// +// 1. input_row_length : the number of rows from the input set +// 2. output_row_length : which will be smaller than the input +// 3. alpha = input_row_length / output_row_length : our reduction ratio +// 4. K = floor(alpha) +// 5. row_pooling_sequence : this is the result list of pool boundary rows +// +// Then, row_pooling_sequence should satisfy: +// +// 1. a[0] = 0 : the first value of the sequence is 0 +// 2. a[end] = input_row_length : the last value of the sequence is the size +// 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size +// 4. length(row_pooling_sequence) = output_row_length+1 +// +// For more details on fractional max pooling, see this paper: +// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// pooling_ratio: Pooling ratio for each dimension of `value`, currently only +// supports row and col dimension and should be >= 1.0. For example, a valid +// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements +// must be 1.0 because we don't allow pooling on batch and channels +// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions +// respectively. +// +// Returns output tensor after fractional max pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient. +func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalMaxPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape. +type ExtractJpegShapeAttr func(optionalAttr) + +// ExtractJpegShapeOutputType sets the optional output_type attribute to value. +// +// value: (Optional) The output type of the operation (int32 or int64). +// Defaults to int32. +// If not specified, defaults to DT_INT32 +func ExtractJpegShapeOutputType(value tf.DataType) ExtractJpegShapeAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Extract the shape information of a JPEG-encoded image. +// +// This op only parses the image header, so it is much faster than DecodeJpeg. +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// +// Returns 1-D. The image shape with format [height, width, channels]. +func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegShapeAttr) (image_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExtractJpegShape", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NthElementAttr is an optional argument to NthElement. +type NthElementAttr func(optionalAttr) + +// NthElementReverse sets the optional reverse attribute to value. +// +// value: When set to True, find the nth-largest value in the vector and vice +// versa. +// If not specified, defaults to false +func NthElementReverse(value bool) NthElementAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Finds values of the `n`-th order statistic for the last dimension. +// +// If the input is a vector (rank-1), finds the entries which is the nth-smallest +// value in the vector and outputs their values as scalar tensor. +// +// For matrices (resp. higher rank input), computes the entries which is the +// nth-smallest value in each row (resp. vector along the last dimension). Thus, +// +// values.shape = input.shape[:-1] +// +// Arguments: +// input: 1-D or higher with last dimension at least `n+1`. +// n: 0-D. Position of sorted vector to select along the last dimension (along +// each row for matrices). Valid range of n is `[0, input.shape[:-1])` +// +// Returns The `n`-th order statistic along each last dimensional slice. +func NthElement(scope *Scope, input tf.Output, n tf.Output, optional ...NthElementAttr) (values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NthElement", + Input: []tf.Input{ + input, n, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Says whether the targets are in the top `K` predictions. +// +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed Precision at `k` as a `bool Tensor`. +func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"k": k} + opspec := tf.OpSpec{ + Type: "InTopK", + Input: []tf.Input{ + predictions, targets, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. +// +// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) +// ](http://arxiv.org/abs/1511.07289) +func Elu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Elu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2. +type InitializeTableFromTextFileV2Attr func(optionalAttr) + +// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value. +// +// value: Number of elements of the file, use -1 if unknown. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr { + return func(m optionalAttr) { + m["vocab_size"] = value + } +} + +// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value. +// +// value: Delimiter to separate fields in a line. +// If not specified, defaults to "\t" +func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr { + return func(m optionalAttr) { + m["delimiter"] = value + } +} + +// Initializes a table from a text file. +// +// It inserts one key-value pair into the table for each line of the file. +// The key and value is extracted from the whole line content, elements from the +// split line based on `delimiter` or the line number (starting from zero). +// Where to extract the key and value from a line is specified by `key_index` and +// `value_index`. +// +// - A value of -1 means use the line number(starting from zero), expects `int64`. +// - A value of -2 means use the whole line content, expects `string`. +// - A value >= 0 means use the index (starting at zero) of the split line based +// on `delimiter`. +// +// Arguments: +// table_handle: Handle to a table which will be initialized. +// filename: Filename of a vocabulary text file. +// key_index: Column index in a line to get the table `key` values from. +// value_index: Column index that represents information of a line to get the table +// `value` values from. +// +// Returns the created operation. +func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InitializeTableFromTextFileV2", + Input: []tf.Input{ + table_handle, filename, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // MapUnstageAttr is an optional argument to MapUnstage. type MapUnstageAttr func(optionalAttr) @@ -8152,733 +9468,21 @@ func MapUnstage(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.Data return values } -// Creates a dataset that emits the records from one or more TFRecord files. +// Computes softsign gradients for a softsign operation. // // Arguments: -// filenames: A scalar or vector containing the name(s) of the file(s) to be -// read. -// compression_type: A scalar containing either (i) the empty string (no -// compression), (ii) "ZLIB", or (iii) "GZIP". -// buffer_size: A scalar representing the number of bytes to buffer. A value of -// 0 means no buffering will be performed. -func TFRecordDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { +// gradients: The backpropagated gradients to the corresponding softsign operation. +// features: The features passed as input to the corresponding softsign operation. +// +// Returns The gradients: `gradients / (1 + abs(features)) ** 2`. +func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "TFRecordDataset", + Type: "SoftsignGrad", Input: []tf.Input{ - filenames, compression_type, buffer_size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that emits the lines of one or more text files. -// -// Arguments: -// filenames: A scalar or a vector containing the name(s) of the file(s) to be -// read. -// compression_type: A scalar containing either (i) the empty string (no -// compression), (ii) "ZLIB", or (iii) "GZIP". -// buffer_size: A scalar containing the number of bytes to buffer. -func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TextLineDataset", - Input: []tf.Input{ - filenames, compression_type, buffer_size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that caches elements from `input_dataset`. -// -// A CacheDataset will iterate over the input_dataset, and store tensors. If the -// cache already exists, the cache will be used. If the cache is inappropriate -// (e.g. cannot be opened, contains tensors of the wrong shape / size), an error -// will the returned when used. -// -// Arguments: -// -// filename: A path on the filesystem where we should cache the dataset. Note: this -// will be a directory. -// -// -func CacheDataset(scope *Scope, input_dataset tf.Output, filename tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "CacheDataset", - Input: []tf.Input{ - input_dataset, filename, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that shuffles and repeats elements from `input_dataset` -// -// pseudorandomly. -// -// Arguments: -// -// buffer_size: The number of output elements to buffer in an iterator over -// this dataset. Compare with the `min_after_dequeue` attr when creating a -// `RandomShuffleQueue`. -// seed: A scalar seed for the random number generator. If either `seed` or -// `seed2` is set to be non-zero, the random number generator is seeded -// by the given seed. Otherwise, a random seed is used. -// seed2: A second scalar seed to avoid seed collision. -// count: A scalar representing the number of times the underlying dataset -// should be repeated. The default is `-1`, which results in infinite repetition. -// -// -func ShuffleAndRepeatDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ShuffleAndRepeatDataset", - Input: []tf.Input{ - input_dataset, buffer_size, seed, seed2, count, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PaddedBatchDatasetV2Attr is an optional argument to PaddedBatchDatasetV2. -type PaddedBatchDatasetV2Attr func(optionalAttr) - -// PaddedBatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. -// If not specified, defaults to false -func PaddedBatchDatasetV2ParallelCopy(value bool) PaddedBatchDatasetV2Attr { - return func(m optionalAttr) { - m["parallel_copy"] = value - } -} - -// Creates a dataset that batches and pads `batch_size` elements from the input. -// -// Arguments: -// -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. -// padded_shapes: A list of int64 tensors representing the desired padded shapes -// of the corresponding output components. These shapes may be partially -// specified, using `-1` to indicate that a particular dimension should be -// padded to the maximum size of all batch elements. -// padding_values: A list of scalars containing the padding value to use for -// each of the outputs. -// drop_remainder: A scalar representing whether the last batch should be dropped in case its size -// is smaller than desired. -// -func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape, optional ...PaddedBatchDatasetV2Attr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "PaddedBatchDatasetV2", - Input: []tf.Input{ - input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that batches and pads `batch_size` elements from the input. -// -// Arguments: -// -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. -// padded_shapes: A list of int64 tensors representing the desired padded shapes -// of the corresponding output components. These shapes may be partially -// specified, using `-1` to indicate that a particular dimension should be -// padded to the maximum size of all batch elements. -// padding_values: A list of scalars containing the padding value to use for -// each of the outputs. -// -func PaddedBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "PaddedBatchDataset", - Input: []tf.Input{ - input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// BatchDatasetV2Attr is an optional argument to BatchDatasetV2. -type BatchDatasetV2Attr func(optionalAttr) - -// BatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. -// If not specified, defaults to false -func BatchDatasetV2ParallelCopy(value bool) BatchDatasetV2Attr { - return func(m optionalAttr) { - m["parallel_copy"] = value - } -} - -// Creates a dataset that batches `batch_size` elements from `input_dataset`. -// -// Arguments: -// -// batch_size: A scalar representing the number of elements to accumulate in a batch. -// drop_remainder: A scalar representing whether the last batch should be dropped in case its size -// is smaller than desired. -// -// -func BatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...BatchDatasetV2Attr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BatchDatasetV2", - Input: []tf.Input{ - input_dataset, batch_size, drop_remainder, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset containing elements of first component of `input_dataset` having true in the last component. -func FilterByLastComponentDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "FilterByLastComponentDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Quantized Batch normalization. -// -// This op is deprecated and will be removed in the future. Prefer -// `tf.nn.batch_normalization`. -// -// Arguments: -// t: A 4D input Tensor. -// t_min: The value represented by the lowest quantized input. -// t_max: The value represented by the highest quantized input. -// m: A 1D mean Tensor with size matching the last dimension of t. -// This is the first output from tf.nn.moments, -// or a saved moving average thereof. -// m_min: The value represented by the lowest quantized mean. -// m_max: The value represented by the highest quantized mean. -// v: A 1D variance Tensor with size matching the last dimension of t. -// This is the second output from tf.nn.moments, -// or a saved moving average thereof. -// v_min: The value represented by the lowest quantized variance. -// v_max: The value represented by the highest quantized variance. -// beta: A 1D beta Tensor with size matching the last dimension of t. -// An offset to be added to the normalized tensor. -// beta_min: The value represented by the lowest quantized offset. -// beta_max: The value represented by the highest quantized offset. -// gamma: A 1D gamma Tensor with size matching the last dimension of t. -// If "scale_after_normalization" is true, this tensor will be multiplied -// with the normalized tensor. -// gamma_min: The value represented by the lowest quantized gamma. -// gamma_max: The value represented by the highest quantized gamma. -// -// variance_epsilon: A small float number to avoid dividing by 0. -// scale_after_normalization: A bool indicating whether the resulted tensor -// needs to be multiplied with gamma. -func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} - opspec := tf.OpSpec{ - Type: "QuantizedBatchNormWithGlobalNormalization", - Input: []tf.Input{ - t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Produces the max pool of the input tensor for quantized types. -// -// Arguments: -// input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. -// min_input: The float value that the lowest quantized input value represents. -// max_input: The float value that the highest quantized input value represents. -// ksize: The size of the window for each dimension of the input tensor. -// The length must be 4 to match the number of dimensions of the input. -// strides: The stride of the sliding window for each dimension of the input -// tensor. The length must be 4 to match the number of dimensions of the input. -// padding: The type of padding algorithm to use. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "QuantizedMaxPool", - Input: []tf.Input{ - input, min_input, max_input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// NthElementAttr is an optional argument to NthElement. -type NthElementAttr func(optionalAttr) - -// NthElementReverse sets the optional reverse attribute to value. -// -// value: When set to True, find the nth-largest value in the vector and vice -// versa. -// If not specified, defaults to false -func NthElementReverse(value bool) NthElementAttr { - return func(m optionalAttr) { - m["reverse"] = value - } -} - -// Finds values of the `n`-th order statistic for the last dimension. -// -// If the input is a vector (rank-1), finds the entries which is the nth-smallest -// value in the vector and outputs their values as scalar tensor. -// -// For matrices (resp. higher rank input), computes the entries which is the -// nth-smallest value in each row (resp. vector along the last dimension). Thus, -// -// values.shape = input.shape[:-1] -// -// Arguments: -// input: 1-D or higher with last dimension at least `n+1`. -// n: 0-D. Position of sorted vector to select along the last dimension (along -// each row for matrices). Valid range of n is `[0, input.shape[:-1])` -// -// Returns The `n`-th order statistic along each last dimensional slice. -func NthElement(scope *Scope, input tf.Output, n tf.Output, optional ...NthElementAttr) (values tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "NthElement", - Input: []tf.Input{ - input, n, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes softmax activations. -// -// For each batch `i` and class `j` we have -// -// $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ -// -// Arguments: -// logits: 2-D with shape `[batch_size, num_classes]`. -// -// Returns Same shape as `logits`. -func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Softmax", - Input: []tf.Input{ - logits, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MapSizeAttr is an optional argument to MapSize. -type MapSizeAttr func(optionalAttr) - -// MapSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapSizeCapacity(value int64) MapSizeAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapSizeMemoryLimit(value int64) MapSizeAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapSizeContainer(value string) MapSizeAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapSizeSharedName(value string) MapSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of elements in the underlying container. -func MapSize(scope *Scope, dtypes []tf.DataType, optional ...MapSizeAttr) (size tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapSize", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` -// -// if < 0, `scale * features` otherwise. -// -// To be used together with -// `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. -// For correct dropout, use `tf.contrib.nn.alpha_dropout`. -// -// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) -func Selu(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Selu", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes gradients for the exponential linear (Elu) operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding Elu operation. -// outputs: The outputs of the corresponding Elu operation. -// -// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, -// `gradients` otherwise. -func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EluGrad", - Input: []tf.Input{ - gradients, outputs, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes rectified linear 6: `min(max(features, 0), 6)`. -func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Relu6", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process. -// -// Note that the hash function may change from time to time. -// This functionality will be deprecated and it's recommended to use -// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. -// -// Arguments: -// -// num_buckets: The number of buckets. -// -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "StringToHashBucket", - Input: []tf.Input{ - string_tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. -// -// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) -// ](http://arxiv.org/abs/1511.07289) -func Elu(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Elu", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TopKV2Attr is an optional argument to TopKV2. -type TopKV2Attr func(optionalAttr) - -// TopKV2Sorted sets the optional sorted attribute to value. -// -// value: If true the resulting `k` elements will be sorted by the values in -// descending order. -// If not specified, defaults to true -func TopKV2Sorted(value bool) TopKV2Attr { - return func(m optionalAttr) { - m["sorted"] = value - } -} - -// Finds values and indices of the `k` largest elements for the last dimension. -// -// If the input is a vector (rank-1), finds the `k` largest entries in the vector -// and outputs their values and indices as vectors. Thus `values[j]` is the -// `j`-th largest entry in `input`, and its index is `indices[j]`. -// -// For matrices (resp. higher rank input), computes the top `k` entries in each -// row (resp. vector along the last dimension). Thus, -// -// values.shape = indices.shape = input.shape[:-1] + [k] -// -// If two elements are equal, the lower-index element appears first. -// -// Arguments: -// input: 1-D or higher with last dimension at least `k`. -// k: 0-D. Number of top elements to look for along the last dimension (along each -// row for matrices). -// -// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. -func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TopKV2", - Input: []tf.Input{ - input, k, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler. -type LogUniformCandidateSamplerAttr func(optionalAttr) - -// LogUniformCandidateSamplerSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Generates labels for candidate sampling with a log-uniform distribution. -// -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. -// -// For each batch, this op picks a single set of sampled candidate labels. -// -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. -// -// Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to randomly sample. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. -// range_max: The sampler will sample integers from the interval [0, range_max). -// -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LogUniformCandidateSampler", - Input: []tf.Input{ - true_classes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Computes the maximum along segments of a tensor. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. -// -// This operator is similar to the unsorted segment sum operator found -// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). -// Instead of computing the sum over segments, it computes the maximum such that: -// -// \\(output_i = \max_{j...} data[j...]\\) where max is over tuples `j...` such -// that `segment_ids[j...] == i`. -// -// If the maximum is empty for a given segment ID `i`, it outputs the smallest -// possible value for the specific numeric type, -// `output[i] = numeric_limits::lowest()`. -// -// If the given segment ID `i` is negative, then the corresponding value is -// dropped, and will not be included in the result. -// -//
-// -//
-// -// For example: -// -// ``` python -// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) -// tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2) -// # ==> [[ 4, 3, 3, 4], -// # [5, 6, 7, 8]] -// ``` -// -// -// Arguments: -// -// segment_ids: A tensor whose shape is a prefix of `data.shape`. -// -// -// Returns Has same shape as data, except for the first `segment_ids.rank` -// dimensions, which are replaced with a single dimension which has size -// `num_segments`. -func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnsortedSegmentMax", - Input: []tf.Input{ - data, segment_ids, num_segments, + gradients, features, }, } op := scope.AddOperation(opspec) @@ -8907,172 +9511,76 @@ func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops return op.Output(0) } -// Computes the gradient of morphological 2-D dilation with respect to the filter. +// Check if the input matches the regex pattern. +// +// The input is a string tensor of any shape. The pattern is a scalar +// string tensor which is applied to every element of the input tensor. +// The boolean values (True or False) of the output tensor indicate +// if the input matches the regex pattern provided. +// +// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) // // Arguments: -// input: 4-D with shape `[batch, in_height, in_width, depth]`. -// filter: 3-D with shape `[filter_height, filter_width, depth]`. -// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. -// strides: 1-D of length 4. The stride of the sliding window for each dimension of -// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. -// rates: 1-D of length 4. The input stride for atrous morphological dilation. -// Must be: `[1, rate_height, rate_width, 1]`. -// padding: The type of padding algorithm to use. +// input: A string tensor of the text to be processed. +// pattern: A scalar string tensor containing the regular expression to match the input. // -// Returns 3-D with shape `[filter_height, filter_width, depth]`. -func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) { +// Returns A bool tensor with the same shape as `input`. +func RegexFullMatch(scope *Scope, input tf.Output, pattern tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} opspec := tf.OpSpec{ - Type: "Dilation2DBackpropFilter", + Type: "RegexFullMatch", Input: []tf.Input{ - input, filter, out_backprop, + input, pattern, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. -// -// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the -// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each -// input channel is processed independently of the others with its own structuring -// function. The `output` tensor has shape -// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output -// tensor depend on the `padding` algorithm. We currently only support the default -// "NHWC" `data_format`. -// -// In detail, the grayscale morphological 2-D dilation is the max-sum correlation -// (for consistency with `conv2d`, we use unmirrored filters): -// -// output[b, y, x, c] = -// max_{dy, dx} input[b, -// strides[1] * y + rates[1] * dy, -// strides[2] * x + rates[2] * dx, -// c] + -// filter[dy, dx, c] -// -// Max-pooling is a special case when the filter has size equal to the pooling -// kernel size and contains all zeros. -// -// Note on duality: The dilation of `input` by the `filter` is equal to the -// negation of the erosion of `-input` by the reflected `filter`. -// -// Arguments: -// input: 4-D with shape `[batch, in_height, in_width, depth]`. -// filter: 3-D with shape `[filter_height, filter_width, depth]`. -// strides: The stride of the sliding window for each dimension of the input -// tensor. Must be: `[1, stride_height, stride_width, 1]`. -// rates: The input stride for atrous morphological dilation. Must be: -// `[1, rate_height, rate_width, 1]`. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape `[batch, out_height, out_width, depth]`. -func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) { +// Computes cos of x element-wise. +func Cos(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} opspec := tf.OpSpec{ - Type: "Dilation2D", + Type: "Cos", Input: []tf.Input{ - input, filter, + x, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage. -type CropAndResizeGradImageAttr func(optionalAttr) +// MaxPoolGradWithArgmaxAttr is an optional argument to MaxPoolGradWithArgmax. +type MaxPoolGradWithArgmaxAttr func(optionalAttr) -// CropAndResizeGradImageMethod sets the optional method attribute to value. +// MaxPoolGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. // -// value: A string specifying the interpolation method. Only 'bilinear' is -// supported for now. -// If not specified, defaults to "bilinear" -func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr { +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradWithArgmaxAttr { return func(m optionalAttr) { - m["method"] = value - } -} - -// Computes the gradient of the crop_and_resize op wrt the input image tensor. -// -// Arguments: -// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. -// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor -// specifies the coordinates of a box in the `box_ind[i]` image and is specified -// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of -// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the -// `[0, 1]` interval of normalized image height is mapped to -// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in -// which case the sampled crop is an up-down flipped version of the original -// image. The width dimension is treated similarly. Normalized coordinates -// outside the `[0, 1]` range are allowed, in which case we use -// `extrapolation_value` to extrapolate the input image values. -// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. -// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. -// image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` -// containing the original image size. Both `image_height` and `image_width` need -// to be positive. -// -// -// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`. -func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"T": T} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CropAndResizeGradImage", - Input: []tf.Input{ - grads, boxes, box_ind, image_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolGradAttr is an optional argument to MaxPoolGrad. -type MaxPoolGradAttr func(optionalAttr) - -// MaxPoolGradDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolGradDataFormat(value string) MaxPoolGradAttr { - return func(m optionalAttr) { - m["data_format"] = value + m["include_batch_in_index"] = value } } // Computes gradients of the maxpooling function. // // Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// input: The original input. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the +// output of `max_pool`. +// argmax: The indices of the maximum values chosen for each output of `max_pool`. // ksize: The size of the window for each dimension of the input tensor. // strides: The stride of the sliding window for each dimension of the // input tensor. // padding: The type of padding algorithm to use. // -// Returns Gradients w.r.t. the input to `max_pool`. -func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) { +// Returns Gradients w.r.t. the input of `max_pool`. +func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradWithArgmaxAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -9081,9 +9589,9 @@ func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad a(attrs) } opspec := tf.OpSpec{ - Type: "MaxPoolGrad", + Type: "MaxPoolGradWithArgmax", Input: []tf.Input{ - orig_input, orig_output, grad, + input, grad, argmax, }, Attrs: attrs, } @@ -9091,6 +9599,52 @@ func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad return op.Output(0) } +// VarHandleOpAttr is an optional argument to VarHandleOp. +type VarHandleOpAttr func(optionalAttr) + +// VarHandleOpContainer sets the optional container attribute to value. +// +// value: the container this variable is placed in. +// If not specified, defaults to "" +func VarHandleOpContainer(value string) VarHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// VarHandleOpSharedName sets the optional shared_name attribute to value. +// +// value: the name by which this variable is referred to. +// If not specified, defaults to "" +func VarHandleOpSharedName(value string) VarHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a Variable resource. +// +// Arguments: +// dtype: the type of this variable. Must agree with the dtypes +// of all ops using this variable. +// shape: The (possibly partially specified) shape of this variable. +func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VarHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2. type MaxPoolGradGradV2Attr func(optionalAttr) @@ -9139,204 +9693,79 @@ func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output return op.Output(0) } -// LRNAttr is an optional argument to LRN. -type LRNAttr func(optionalAttr) - -// LRNDepthRadius sets the optional depth_radius attribute to value. +// Inverse fast Fourier transform. // -// value: 0-D. Half-width of the 1-D normalization window. -// If not specified, defaults to 5 -func LRNDepthRadius(value int64) LRNAttr { - return func(m optionalAttr) { - m["depth_radius"] = value - } -} - -// LRNBias sets the optional bias attribute to value. -// -// value: An offset (usually positive to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNBias(value float32) LRNAttr { - return func(m optionalAttr) { - m["bias"] = value - } -} - -// LRNAlpha sets the optional alpha attribute to value. -// -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNAlpha(value float32) LRNAttr { - return func(m optionalAttr) { - m["alpha"] = value - } -} - -// LRNBeta sets the optional beta attribute to value. -// -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNBeta(value float32) LRNAttr { - return func(m optionalAttr) { - m["beta"] = value - } -} - -// Local Response Normalization. -// -// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last -// dimension), and each vector is normalized independently. Within a given vector, -// each component is divided by the weighted, squared sum of inputs within -// `depth_radius`. In detail, -// -// sqr_sum[a, b, c, d] = -// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) -// output = input / (bias + alpha * sqr_sum) ** beta -// -// For details, see [Krizhevsky et al., ImageNet classification with deep -// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). +// Computes the inverse 1-dimensional discrete Fourier transform over the +// inner-most dimension of `input`. // // Arguments: -// input: 4-D. -func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its inverse 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft +// @end_compatibility +func IFFT(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "LRN", + Type: "IFFT", Input: []tf.Input{ input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Converts a flat index or array of flat indices into a tuple of -// -// coordinate arrays. -// -// @compatibility(numpy) -// Equivalent to np.unravel_index -// @end_compatibility -// -// Arguments: -// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the -// flattened version of an array of dimensions dims. -// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling -// indices. -// -// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the -// same shape as the indices array. -func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnravelIndex", - Input: []tf.Input{ - indices, dims, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// DecodeWavAttr is an optional argument to DecodeWav. +type DecodeWavAttr func(optionalAttr) -// Returns the max of x and y (i.e. x > y ? x : y) element-wise. +// DecodeWavDesiredChannels sets the optional desired_channels attribute to value. // -// *NOTE*: `Maximum` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Maximum", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Saves tensors in V2 checkpoint format. -// -// By default, saves the named tensors in full. If the caller wishes to save -// specific slices of full tensors, "shape_and_slices" should be non-empty strings -// and correspondingly well-formed. -// -// Arguments: -// prefix: Must have a single element. The prefix of the V2 checkpoint to which we -// write the tensors. -// tensor_names: shape {N}. The names of the tensors to be saved. -// shape_and_slices: shape {N}. The slice specs of the tensors to be saved. -// Empty strings indicate that they are non-partitioned tensors. -// tensors: `N` tensors to save. -// -// Returns the created operation. -func SaveV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, tensors []tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SaveV2", - Input: []tf.Input{ - prefix, tensor_names, shape_and_slices, tf.OutputList(tensors), - }, - } - return scope.AddOperation(opspec) -} - -// DecodePngAttr is an optional argument to DecodePng. -type DecodePngAttr func(optionalAttr) - -// DecodePngChannels sets the optional channels attribute to value. -// -// value: Number of color channels for the decoded image. -// If not specified, defaults to 0 -func DecodePngChannels(value int64) DecodePngAttr { +// value: Number of sample channels wanted. +// If not specified, defaults to -1 +func DecodeWavDesiredChannels(value int64) DecodeWavAttr { return func(m optionalAttr) { - m["channels"] = value + m["desired_channels"] = value } } -// DecodePngDtype sets the optional dtype attribute to value. -// If not specified, defaults to DT_UINT8 -func DecodePngDtype(value tf.DataType) DecodePngAttr { +// DecodeWavDesiredSamples sets the optional desired_samples attribute to value. +// +// value: Length of audio requested. +// If not specified, defaults to -1 +func DecodeWavDesiredSamples(value int64) DecodeWavAttr { return func(m optionalAttr) { - m["dtype"] = value + m["desired_samples"] = value } } -// Decode a PNG-encoded image to a uint8 or uint16 tensor. +// Decode a 16-bit PCM WAV file to a float tensor. // -// The attr `channels` indicates the desired number of color channels for the -// decoded image. +// The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float. // -// Accepted values are: +// When desired_channels is set, if the input contains fewer channels than this +// then the last channel will be duplicated to give the requested number, else if +// the input has more channels than requested then the additional channels will be +// ignored. // -// * 0: Use the number of channels in the PNG-encoded image. -// * 1: output a grayscale image. -// * 3: output an RGB image. -// * 4: output an RGBA image. +// If desired_samples is set, then the audio will be cropped or padded with zeroes +// to the requested length. // -// If needed, the PNG-encoded image is transformed to match the requested number -// of color channels. -// -// This op also supports decoding JPEGs and non-animated GIFs since the interface -// is the same, though it is cleaner to use `tf.image.decode_image`. +// The first output contains a Tensor with the content of the audio samples. The +// lowest dimension will be the number of channels, and the second will be the +// number of samples. For example, a ten-sample-long stereo WAV file should give an +// output shape of [10, 2]. // // Arguments: -// contents: 0-D. The PNG-encoded image. +// contents: The WAV-encoded audio, usually from a file. // -// Returns 3-D with shape `[height, width, channels]`. -func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) { +// Returns 2-D with shape `[length, channels]`.Scalar holding the sample rate found in the WAV header. +func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (audio tf.Output, sample_rate tf.Output) { if scope.Err() != nil { return } @@ -9345,36 +9774,62 @@ func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (ima a(attrs) } opspec := tf.OpSpec{ - Type: "DecodePng", + Type: "DecodeWav", Input: []tf.Input{ contents, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Delete the TensorArray from its resource container. +// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2. +type MaxPoolGradV2Attr func(optionalAttr) + +// MaxPoolGradV2DataFormat sets the optional data_format attribute to value. // -// This enables the user to close and release the resource in the middle -// of a step/run. +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the maxpooling function. // // Arguments: -// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. // -// Returns the created operation. -func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { +// Returns Gradients w.r.t. the input to `max_pool`. +func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "TensorArrayCloseV3", - Input: []tf.Input{ - handle, - }, + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) } - return scope.AddOperation(opspec) + opspec := tf.OpSpec{ + Type: "MaxPoolGradV2", + Input: []tf.Input{ + orig_input, orig_output, grad, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) } // MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. @@ -9426,103 +9881,10 @@ func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output return op.Output(0) } -// ResourceSparseApplyAdadeltaAttr is an optional argument to ResourceSparseApplyAdadelta. -type ResourceSparseApplyAdadeltaAttr func(optionalAttr) +// Conv3DBackpropInputV2Attr is an optional argument to Conv3DBackpropInputV2. +type Conv3DBackpropInputV2Attr func(optionalAttr) -// ResourceSparseApplyAdadeltaUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyAdadeltaUseLocking(value bool) ResourceSparseApplyAdadeltaAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// var: Should be from a Variable(). -// -// Arguments: -// -// accum: Should be from a Variable(). -// accum_update: : Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// rho: Decay factor. Must be a scalar. -// epsilon: Constant factor. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// -// Returns the created operation. -func ResourceSparseApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdadeltaAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdadelta", - Input: []tf.Input{ - var_, accum, accum_update, lr, rho, epsilon, grad, indices, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Convert the quantized 'input' tensor into a lower-precision 'output', using the -// -// actual distribution of the values to maximize the usage of the lower bit depth -// and adjusting the output min and max ranges accordingly. -// -// [input_min, input_max] are scalar floats that specify the range for the float -// interpretation of the 'input' data. For example, if input_min is -1.0f and -// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 -// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. -// -// This operator tries to squeeze as much precision as possible into an output with -// a lower bit depth by calculating the actual min and max values found in the -// data. For example, maybe that quint16 input has no values lower than 16,384 and -// none higher than 49,152. That means only half the range is actually needed, all -// the float interpretations are between -0.5f and 0.5f, so if we want to compress -// the data into a quint8 output, we can use that range rather than the theoretical -// -1.0f to 1.0f that is suggested by the input min and max. -// -// In practice, this is most useful for taking output from operations like -// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and -// may have large potential output ranges, but in practice have a distribution of -// input values that only uses a small fraction of the possible range. By feeding -// that output into this operator, we can reduce it from 32 bits down to 8 with -// minimal loss of accuracy. -// -// Arguments: -// -// input_min: The float value that the minimum quantized input value represents. -// input_max: The float value that the maximum quantized input value represents. -// out_type: The type of the output. Should be a lower bit depth than Tinput. -// -// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"out_type": out_type} - opspec := tf.OpSpec{ - Type: "QuantizeDownAndShrinkRange", - Input: []tf.Input{ - input, input_min, input_max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad. -type AvgPool3DGradAttr func(optionalAttr) - -// AvgPool3DGradDataFormat sets the optional data_format attribute to value. +// Conv3DBackpropInputV2DataFormat sets the optional data_format attribute to value. // // value: The data format of the input and output data. With the // default format "NDHWC", the data is stored in the order of: @@ -9530,205 +9892,13 @@ type AvgPool3DGradAttr func(optionalAttr) // Alternatively, the format could be "NCDHW", the data storage order is: // [batch, in_channels, in_depth, in_height, in_width]. // If not specified, defaults to "NDHWC" -func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr { +func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr { return func(m optionalAttr) { m["data_format"] = value } } -// Computes gradients of average pooling function. -// -// Arguments: -// orig_input_shape: The original input dimensions. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -// -// Returns The backprop for input. -func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AvgPool3DGrad", - Input: []tf.Input{ - orig_input_shape, grad, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2. -type SampleDistortedBoundingBoxV2Attr func(optionalAttr) - -// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to non-zero, the random number -// generator is seeded by the given `seed`. Otherwise, it is seeded by a random -// seed. -// If not specified, defaults to 0 -func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// SampleDistortedBoundingBoxV2Seed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value. -// -// value: The cropped area of the image must have an aspect ratio = -// width / height within this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["aspect_ratio_range"] = value - } -} - -// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. -// -// value: The cropped area of the image must contain a fraction of the -// supplied image within this range. -// If not specified, defaults to -func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["area_range"] = value - } -} - -// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value. -// -// value: Number of attempts at generating a cropped region of the image -// of the specified constraints. After `max_attempts` failures, return the entire -// image. -// If not specified, defaults to 100 -func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["max_attempts"] = value - } -} - -// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. -// -// value: Controls behavior if no bounding boxes supplied. -// If true, assume an implicit bounding box covering the whole input. If false, -// raise an error. -// If not specified, defaults to false -func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr { - return func(m optionalAttr) { - m["use_image_if_no_bounding_boxes"] = value - } -} - -// Generate a single randomly distorted bounding box for an image. -// -// Bounding box annotations are often supplied in addition to ground-truth labels -// in image recognition or object localization tasks. A common technique for -// training such a system is to randomly distort an image while preserving -// its content, i.e. *data augmentation*. This Op outputs a randomly distorted -// localization of an object, i.e. bounding box, given an `image_size`, -// `bounding_boxes` and a series of constraints. -// -// The output of this Op is a single bounding box that may be used to crop the -// original image. The output is returned as 3 tensors: `begin`, `size` and -// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the -// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize -// what the bounding box looks like. -// -// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The -// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and -// height of the underlying image. -// -// For example, -// -// ```python -// # Generate a single distorted bounding box. -// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( -// tf.shape(image), -// bounding_boxes=bounding_boxes) -// -// # Draw the bounding box in an image summary. -// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), -// bbox_for_draw) -// tf.summary.image('images_with_box', image_with_box) -// -// # Employ the bounding box to distort the image. -// distorted_image = tf.slice(image, begin, size) -// ``` -// -// Note that if no bounding box information is available, setting -// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit -// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is -// false and no bounding boxes are supplied, an error is raised. -// -// Arguments: -// image_size: 1-D, containing `[height, width, channels]`. -// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes -// associated with the image. -// min_object_covered: The cropped area of the image must contain at least this -// fraction of any bounding box supplied. The value of this parameter should be -// non-negative. In the case of 0, the cropped area does not need to overlap -// any of the bounding boxes supplied. -// -// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to -// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to -// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. -// Provide as input to `tf.image.draw_bounding_boxes`. -func SampleDistortedBoundingBoxV2(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, min_object_covered tf.Output, optional ...SampleDistortedBoundingBoxV2Attr) (begin tf.Output, size tf.Output, bboxes tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SampleDistortedBoundingBoxV2", - Input: []tf.Input{ - image_size, bounding_boxes, min_object_covered, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. -type Conv3DBackpropFilterV2Attr func(optionalAttr) - -// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. +// Conv3DBackpropInputV2Dilations sets the optional dilations attribute to value. // // value: 1-D tensor of length 5. The dilation factor for each dimension of // `input`. If set to k > 1, there will be k-1 skipped cells between each @@ -9736,133 +9906,18 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. // If not specified, defaults to -func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { +func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr { return func(m optionalAttr) { m["dilations"] = value } } -// Computes the gradients of 3-D convolution with respect to the filter. +// Computes the gradients of 3-D convolution with respect to the input. // // Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter_sizes: An integer vector representing the tensor shape of `filter`, -// where `filter` is a 5-D -// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` -// tensor. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Conv3DBackpropFilterV2", - Input: []tf.Input{ - input, filter_sizes, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CTCLossAttr is an optional argument to CTCLoss. -type CTCLossAttr func(optionalAttr) - -// CTCLossPreprocessCollapseRepeated sets the optional preprocess_collapse_repeated attribute to value. -// -// value: Scalar, if true then repeated labels are -// collapsed prior to the CTC calculation. -// If not specified, defaults to false -func CTCLossPreprocessCollapseRepeated(value bool) CTCLossAttr { - return func(m optionalAttr) { - m["preprocess_collapse_repeated"] = value - } -} - -// CTCLossCtcMergeRepeated sets the optional ctc_merge_repeated attribute to value. -// -// value: Scalar. If set to false, *during* CTC calculation -// repeated non-blank labels will not be merged and are interpreted as -// individual labels. This is a simplified version of CTC. -// If not specified, defaults to true -func CTCLossCtcMergeRepeated(value bool) CTCLossAttr { - return func(m optionalAttr) { - m["ctc_merge_repeated"] = value - } -} - -// CTCLossIgnoreLongerOutputsThanInputs sets the optional ignore_longer_outputs_than_inputs attribute to value. -// -// value: Scalar. If set to true, during CTC -// calculation, items that have longer output sequences than input sequences -// are skipped: they don't contribute to the loss term and have zero-gradient. -// If not specified, defaults to false -func CTCLossIgnoreLongerOutputsThanInputs(value bool) CTCLossAttr { - return func(m optionalAttr) { - m["ignore_longer_outputs_than_inputs"] = value - } -} - -// Calculates the CTC Loss (log probability) for each batch entry. Also calculates -// -// the gradient. This class performs the softmax operation for you, so inputs -// should be e.g. linear projections of outputs by an LSTM. -// -// Arguments: -// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. -// labels_indices: The indices of a `SparseTensor`. -// `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for -// `(batch b, time t)`. -// labels_values: The values (labels) associated with the given batch and time. -// sequence_length: A vector containing sequence lengths (batch). -// -// Returns A vector (batch) containing log-probabilities.The gradient of `loss`. 3-D, shape: -// `(max_time x batch_size x num_classes)`. -func CTCLoss(scope *Scope, inputs tf.Output, labels_indices tf.Output, labels_values tf.Output, sequence_length tf.Output, optional ...CTCLossAttr) (loss tf.Output, gradient tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CTCLoss", - Input: []tf.Input{ - inputs, labels_indices, labels_values, sequence_length, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Conv3DBackpropFilterAttr is an optional argument to Conv3DBackpropFilter. -type Conv3DBackpropFilterAttr func(optionalAttr) - -// Conv3DBackpropFilterDilations sets the optional dilations attribute to value. -// If not specified, defaults to -func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of 3-D convolution with respect to the filter. -// -// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2 -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. +// input_sizes: An integer vector representing the tensor shape of `input`, +// where `input` is a 5-D +// `[batch, depth, rows, cols, in_channels]` tensor. // filter: Shape `[depth, rows, cols, in_channels, out_channels]`. // `in_channels` must match between `input` and `filter`. // out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, @@ -9870,7 +9925,7 @@ func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr { // strides: 1-D tensor of length 5. The stride of the sliding window for each // dimension of `input`. Must have `strides[0] = strides[4] = 1`. // padding: The type of padding algorithm to use. -func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterAttr) (output tf.Output) { +func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputV2Attr) (output tf.Output) { if scope.Err() != nil { return } @@ -9879,483 +9934,7 @@ func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_b a(attrs) } opspec := tf.OpSpec{ - Type: "Conv3DBackpropFilter", - Input: []tf.Input{ - input, filter, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Makes the summary of quantiles for the batch. -// -// An op that takes a list of tensors (one tensor per feature) and outputs the -// quantile summaries for each tensor. -// -// Arguments: -// float_values: float; List of Rank 1 Tensors each containing values for a single feature. -// example_weights: float; Rank 1 Tensor with weights per instance. -// epsilon: float; The required maximum approximation error. -// -// Returns float; List of Rank 2 Tensors each containing the quantile summary -// (value, weight, min_rank, max_rank) of a single feature. -func BoostedTreesMakeQuantileSummaries(scope *Scope, float_values []tf.Output, example_weights tf.Output, epsilon tf.Output) (summaries []tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BoostedTreesMakeQuantileSummaries", - Input: []tf.Input{ - tf.OutputList(float_values), example_weights, epsilon, - }, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if summaries, idx, err = makeOutputList(op, idx, "summaries"); err != nil { - scope.UpdateErr("BoostedTreesMakeQuantileSummaries", err) - return - } - return summaries -} - -// Performs a padding as a preprocess during a convolution. -// -// Similar to FusedResizeAndPadConv2d, this op allows for an optimized -// implementation where the spatial padding transformation stage is fused with the -// im2col lookup, but in this case without the bilinear filtering required for -// resizing. Fusing the padding prevents the need to write out the intermediate -// results as whole tensors, reducing memory pressure, and we can get some latency -// gains by merging the transformation calculations. -// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' -// order is used instead. -// Internally this op uses a single per-graph scratch buffer, which means that it -// will block if multiple versions are being run in parallel. This is because this -// operator is primarily an optimization to minimize memory usage. -// -// Arguments: -// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. -// paddings: A two-column matrix specifying the padding sizes. The number of -// rows must be the same as the rank of `input`. -// filter: 4-D with shape -// `[filter_height, filter_width, in_channels, out_channels]`. -// -// strides: 1-D of length 4. The stride of the sliding window for each dimension -// of `input`. Must be in the same order as the dimension specified with format. -// padding: The type of padding algorithm to use. -func FusedPadConv2D(scope *Scope, input tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "FusedPadConv2D", - Input: []tf.Input{ - input, paddings, filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad. -type MaxPoolGradGradAttr func(optionalAttr) - -// MaxPoolGradGradDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes second-order gradients of the maxpooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients of gradients w.r.t. the input to `max_pool`. -func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradGrad", - Input: []tf.Input{ - orig_input, orig_output, grad, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap. -type AddSparseToTensorsMapAttr func(optionalAttr) - -// AddSparseToTensorsMapContainer sets the optional container attribute to value. -// -// value: The container name for the `SparseTensorsMap` created by this op. -// If not specified, defaults to "" -func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value. -// -// value: The shared name for the `SparseTensorsMap` created by this op. -// If blank, the new Operation's unique name is used. -// If not specified, defaults to "" -func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Add a `SparseTensor` to a `SparseTensorsMap` return its handle. -// -// A `SparseTensor` is represented by three tensors: `sparse_indices`, -// `sparse_values`, and `sparse_shape`. -// -// This operator takes the given `SparseTensor` and adds it to a container -// object (a `SparseTensorsMap`). A unique key within this container is generated -// in the form of an `int64`, and this is the value that is returned. -// -// The `SparseTensor` can then be read out as part of a minibatch by passing -// the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure -// the correct `SparseTensorsMap` is accessed, ensure that the same -// `container` and `shared_name` are passed to that Op. If no `shared_name` -// is provided here, instead use the *name* of the Operation created by calling -// `AddSparseToTensorsMap` as the `shared_name` passed to -// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. -// -// Arguments: -// sparse_indices: 2-D. The `indices` of the `SparseTensor`. -// sparse_values: 1-D. The `values` of the `SparseTensor`. -// sparse_shape: 1-D. The `shape` of the `SparseTensor`. -// -// Returns 0-D. The handle of the `SparseTensor` now stored in the -// `SparseTensorsMap`. -func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AddSparseToTensorsMap", - Input: []tf.Input{ - sparse_indices, sparse_values, sparse_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Compute the regularized incomplete beta integral \\(I_x(a, b)\\). -// -// The regularized incomplete beta integral is defined as: -// -// -// \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\) -// -// where -// -// -// \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\) -// -// -// is the incomplete beta function and \\(B(a, b)\\) is the *complete* -// beta function. -func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Betainc", - Input: []tf.Input{ - a, b, x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DataFormatVecPermuteAttr is an optional argument to DataFormatVecPermute. -type DataFormatVecPermuteAttr func(optionalAttr) - -// DataFormatVecPermuteSrcFormat sets the optional src_format attribute to value. -// -// value: source data format. -// If not specified, defaults to "NHWC" -func DataFormatVecPermuteSrcFormat(value string) DataFormatVecPermuteAttr { - return func(m optionalAttr) { - m["src_format"] = value - } -} - -// DataFormatVecPermuteDstFormat sets the optional dst_format attribute to value. -// -// value: destination data format. -// If not specified, defaults to "NCHW" -func DataFormatVecPermuteDstFormat(value string) DataFormatVecPermuteAttr { - return func(m optionalAttr) { - m["dst_format"] = value - } -} - -// Returns the permuted vector/tensor in the destination data format given the -// -// one in the source data format. -// -// Arguments: -// x: Vector of size 4 or Tensor of shape (4, 2) in source data format. -// -// Returns Vector of size 4 or Tensor of shape (4, 2) in destination data format. -func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPermuteAttr) (y tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DataFormatVecPermute", - Input: []tf.Input{ - x, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. -type DataFormatDimMapAttr func(optionalAttr) - -// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. -// -// value: source data format. -// If not specified, defaults to "NHWC" -func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { - return func(m optionalAttr) { - m["src_format"] = value - } -} - -// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. -// -// value: destination data format. -// If not specified, defaults to "NCHW" -func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { - return func(m optionalAttr) { - m["dst_format"] = value - } -} - -// Returns the dimension index in the destination data format given the one in -// -// the source data format. -// -// Arguments: -// x: A Tensor with each element as a dimension index in source data format. -// Must be in the range [-4, 4). -// -// Returns A Tensor with each element as a dimension index in destination data format. -func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DataFormatDimMap", - Input: []tf.Input{ - x, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Fetches multiple values from infeed as an XLA tuple. -// -// Arguments: -// dtypes: The element types of each element in `outputs`. -// shapes: The shapes of each tensor in `outputs`. -// -// Returns A list of tensors that will be provided using the infeed mechanism. -func InfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} - opspec := tf.OpSpec{ - Type: "InfeedDequeueTuple", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("InfeedDequeueTuple", err) - return - } - return outputs -} - -// StackPushV2Attr is an optional argument to StackPushV2. -type StackPushV2Attr func(optionalAttr) - -// StackPushV2SwapMemory sets the optional swap_memory attribute to value. -// -// value: Swap `elem` to CPU. Default to false. -// If not specified, defaults to false -func StackPushV2SwapMemory(value bool) StackPushV2Attr { - return func(m optionalAttr) { - m["swap_memory"] = value - } -} - -// Push an element onto the stack. -// -// Arguments: -// handle: The handle to a stack. -// elem: The tensor to be pushed onto the stack. -// -// Returns The same tensor as the input 'elem'. -func StackPushV2(scope *Scope, handle tf.Output, elem tf.Output, optional ...StackPushV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StackPushV2", - Input: []tf.Input{ - handle, elem, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Conv2DBackpropInputAttr is an optional argument to Conv2DBackpropInput. -type Conv2DBackpropInputAttr func(optionalAttr) - -// Conv2DBackpropInputUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. -// If not specified, defaults to true -func Conv2DBackpropInputUseCudnnOnGpu(value bool) Conv2DBackpropInputAttr { - return func(m optionalAttr) { - m["use_cudnn_on_gpu"] = value - } -} - -// Conv2DBackpropInputExplicitPaddings sets the optional explicit_paddings attribute to value. -// -// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith -// dimension, the amount of padding inserted before and after the dimension is -// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If -// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. -// If not specified, defaults to <> -func Conv2DBackpropInputExplicitPaddings(value []int64) Conv2DBackpropInputAttr { - return func(m optionalAttr) { - m["explicit_paddings"] = value - } -} - -// Conv2DBackpropInputDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Conv2DBackpropInputDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of convolution with respect to the input. -// -// Arguments: -// input_sizes: An integer vector representing the shape of `input`, -// where `input` is a 4-D `[batch, height, width, channels]` tensor. -// filter: 4-D with shape -// `[filter_height, filter_width, in_channels, out_channels]`. -// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. -// Gradients w.r.t. the output of the convolution. -// strides: The stride of the sliding window for each dimension of the input -// of the convolution. Must be in the same order as the dimension specified with -// format. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient -// w.r.t. the input of the convolution. -func Conv2DBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropInputAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Conv2DBackpropInput", + Type: "Conv3DBackpropInputV2", Input: []tf.Input{ input_sizes, filter, out_backprop, }, @@ -10365,148 +9944,83 @@ func Conv2DBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, return op.Output(0) } -// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad. -type FractionalMaxPoolGradAttr func(optionalAttr) - -// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value. +// Deprecated. Use TensorArraySplitV3 // -// value: When set to True, it means when pooling, the values at the boundary -// of adjacent pooling cells are used by both cells. For example: -// -// `index 0 1 2 3 4` -// -// `value 20 5 16 3 7` -// -// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. -// The result would be [20, 16] for fractional max pooling. -// If not specified, defaults to false -func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr { - return func(m optionalAttr) { - m["overlapping"] = value - } -} - -// Computes gradient of the FractionalMaxPool function. -// -// Arguments: -// orig_input: Original input for `fractional_max_pool` -// orig_output: Original output for `fractional_max_pool` -// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients -// w.r.t. the output of `fractional_max_pool`. -// row_pooling_sequence: row pooling sequence, form pooling region with -// col_pooling_sequence. -// col_pooling_sequence: column pooling sequence, form pooling region with -// row_pooling sequence. -// -// Returns 4-D. Gradients w.r.t. the input of `fractional_max_pool`. -func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) { +// DEPRECATED at GraphDef version 26: Use TensorArraySplitV3 +func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "FractionalMaxPoolGrad", + Type: "TensorArraySplitV2", Input: []tf.Input{ - orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence, + handle, value, lengths, flow_in, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Conv2DAttr is an optional argument to Conv2D. -type Conv2DAttr func(optionalAttr) +// Converts the given `resource_handle` representing an iterator to a string. +// +// Arguments: +// resource_handle: A handle to an iterator resource. +// +// Returns A string representation of the given handle. +func IteratorToStringHandle(scope *Scope, resource_handle tf.Output) (string_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IteratorToStringHandle", + Input: []tf.Input{ + resource_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. -// If not specified, defaults to true -func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr { +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize. +type QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QUINT8 +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { return func(m optionalAttr) { - m["use_cudnn_on_gpu"] = value + m["out_type"] = value } } -// Conv2DExplicitPaddings sets the optional explicit_paddings attribute to value. +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value. // -// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith -// dimension, the amount of padding inserted before and after the dimension is -// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If -// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. -// If not specified, defaults to <> -func Conv2DExplicitPaddings(value []int64) Conv2DAttr { - return func(m optionalAttr) { - m["explicit_paddings"] = value - } -} - -// Conv2DDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, height, width, channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, channels, height, width]. -// If not specified, defaults to "NHWC" -func Conv2DDataFormat(value string) Conv2DAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Conv2DDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each -// filter element on that dimension. The dimension order is determined by the -// value of `data_format`, see above for details. Dilations in the batch and -// depth dimensions must be 1. +// value: List of dilation values. // If not specified, defaults to -func Conv2DDilations(value []int64) Conv2DAttr { +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { return func(m optionalAttr) { m["dilations"] = value } } -// Computes a 2-D convolution given 4-D `input` and `filter` tensors. -// -// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` -// and a filter / kernel tensor of shape -// `[filter_height, filter_width, in_channels, out_channels]`, this op -// performs the following: -// -// 1. Flattens the filter to a 2-D matrix with shape -// `[filter_height * filter_width * in_channels, output_channels]`. -// 2. Extracts image patches from the input tensor to form a *virtual* -// tensor of shape `[batch, out_height, out_width, -// filter_height * filter_width * in_channels]`. -// 3. For each patch, right-multiplies the filter matrix and the image patch -// vector. -// -// In detail, with the default NHWC format, -// -// output[b, i, j, k] = -// sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * -// filter[di, dj, q, k] -// -// Must have `strides[0] = strides[3] = 1`. For the most common case of the same -// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. +// Computes quantized depthwise Conv2D with Bias, Relu and Requantize. // // Arguments: -// input: A 4-D tensor. The dimension order is interpreted according to the value -// of `data_format`, see below for details. -// filter: A 4-D tensor of shape -// `[filter_height, filter_width, in_channels, out_channels]` -// strides: 1-D tensor of length 4. The stride of the sliding window for each -// dimension of `input`. The dimension order is determined by the value of -// `data_format`, see below for details. -// padding: The type of padding algorithm to use. +// input: The original input tensor. +// filter: The original filter tensor. +// bias: The original bias tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// min_freezed_output: The minimum float value of the output tensor. +// max_freezed_output: The maximum float value of the output tensor. +// strides: List of stride values. // -// Returns A 4-D tensor. The dimension order is determined by the value of -// `data_format`, see below for details. -func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) { +// +// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, min_freezed_output tf.Output, max_freezed_output tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { if scope.Err() != nil { return } @@ -10515,51 +10029,9 @@ func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, pa a(attrs) } opspec := tf.OpSpec{ - Type: "Conv2D", + Type: "QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize", Input: []tf.Input{ - input, filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RequantizePerChannelAttr is an optional argument to RequantizePerChannel. -type RequantizePerChannelAttr func(optionalAttr) - -// RequantizePerChannelOutType sets the optional out_type attribute to value. -// -// value: The quantized type of output tensor that needs to be converted. -// If not specified, defaults to DT_QUINT8 -func RequantizePerChannelOutType(value tf.DataType) RequantizePerChannelAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Requantizes input with min and max values known per channel. -// -// Arguments: -// input: The original input tensor. -// input_min: The minimum value of the input tensor -// input_max: The maximum value of the input tensor. -// requested_output_min: The minimum value of the output tensor requested. -// requested_output_max: The maximum value of the output tensor requested. -// -// Returns Output tensor.The minimum value of the final output tensorThe maximum value of the final output tensor. -func RequantizePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, optional ...RequantizePerChannelAttr) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RequantizePerChannel", - Input: []tf.Input{ - input, input_min, input_max, requested_output_min, requested_output_max, + input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, }, Attrs: attrs, } @@ -10567,181 +10039,10 @@ func RequantizePerChannel(scope *Scope, input tf.Output, input_min tf.Output, in return op.Output(0), op.Output(1), op.Output(2) } -// TextLineReaderV2Attr is an optional argument to TextLineReaderV2. -type TextLineReaderV2Attr func(optionalAttr) - -// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value. -// -// value: Number of lines to skip from the beginning of every file. -// If not specified, defaults to 0 -func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr { - return func(m optionalAttr) { - m["skip_header_lines"] = value - } -} - -// TextLineReaderV2Container sets the optional container attribute to value. -// -// value: If non-empty, this reader is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func TextLineReaderV2Container(value string) TextLineReaderV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// TextLineReaderV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this reader is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// A Reader that outputs the lines of a file delimited by '\n'. -// -// Returns The handle to reference the Reader. -func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TextLineReaderV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Scatter `updates` into an existing tensor according to `indices`. -// -// This operation creates a new tensor by applying sparse `updates` to the passed -// in `tensor`. -// This operation is very similar to `tf.scatter_nd`, except that the updates are -// scattered onto an existing tensor (as opposed to a zero-tensor). If the memory -// for the existing tensor cannot be re-used, a copy is made and updated. -// -// If `indices` contains duplicates, then their updates are accumulated (summed). -// -// **WARNING**: The order in which updates are applied is nondeterministic, so the -// output will be nondeterministic if `indices` contains duplicates -- because -// of some numerical approximation issues, numbers summed in different order -// may yield different results. -// -// `indices` is an integer tensor containing indices into a new tensor of shape -// `shape`. The last dimension of `indices` can be at most the rank of `shape`: -// -// indices.shape[-1] <= shape.rank -// -// The last dimension of `indices` corresponds to indices into elements -// (if `indices.shape[-1] = shape.rank`) or slices -// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of -// `shape`. `updates` is a tensor with shape -// -// indices.shape[:-1] + shape[indices.shape[-1]:] -// -// The simplest form of scatter is to insert individual elements in a tensor by -// index. For example, say we want to insert 4 scattered elements in a rank-1 -// tensor with 8 elements. -// -//
-// -//
-// -// In Python, this scatter operation would look like this: -// -// ```python -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// tensor = tf.ones([8], dtype=tf.int32) -// updated = tf.tensor_scatter_update(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [1, 11, 1, 10, 9, 1, 1, 12] -// -// We can also, insert entire slices of a higher rank tensor all at once. For -// example, if we wanted to insert two slices in the first dimension of a -// rank-3 tensor with two matrices of new values. -// -// In Python, this scatter operation would look like this: -// -// ```python -// indices = tf.constant([[0], [2]]) -// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]], -// [[5, 5, 5, 5], [6, 6, 6, 6], -// [7, 7, 7, 7], [8, 8, 8, 8]]]) -// tensor = tf.ones([4, 4, 4]) -// updated = tf.tensor_scatter_update(tensor, indices, updates) -// with tf.Session() as sess: -// print(sess.run(scatter)) -// ``` -// -// The resulting tensor would look like this: -// -// [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], -// [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], -// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] -// -// Note that on CPU, if an out of bound index is found, an error is returned. -// On GPU, if an out of bound index is found, the index is ignored. -// -// Arguments: -// tensor: Tensor to copy/update. -// indices: Index tensor. -// updates: Updates to scatter into output. -// -// Returns A new tensor with the given shape and updates applied according -// to the indices. -func TensorScatterUpdate(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorScatterUpdate", - Input: []tf.Input{ - tensor, indices, updates, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// BiasAddAttr is an optional argument to BiasAdd. -type BiasAddAttr func(optionalAttr) - -// BiasAddDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the bias tensor will be added to the last dimension -// of the value tensor. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// The tensor will be added to "in_channels", the third-to-the-last -// dimension. -// If not specified, defaults to "NHWC" -func BiasAddDataFormat(value string) BiasAddAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - // Adds `bias` to `value`. // +// This is a deprecated version of BiasAdd and will be soon removed. +// // This is a special case of `tf.add` where `bias` is restricted to be 1-D. // Broadcasting is supported, so `value` may have any number of dimensions. // @@ -10750,66 +10051,80 @@ func BiasAddDataFormat(value string) BiasAddAttr { // bias: 1-D with size the last dimension of `value`. // // Returns Broadcasted sum of `value` and `bias`. -func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) { +func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "BiasAdd", + Type: "BiasAddV1", Input: []tf.Input{ value, bias, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. -type FakeQuantWithMinMaxVarsAttr func(optionalAttr) +// FusedBatchNormGradV3Attr is an optional argument to FusedBatchNormGradV3. +type FusedBatchNormGradV3Attr func(optionalAttr) -// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { +// FusedBatchNormGradV3Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormGradV3Epsilon(value float32) FusedBatchNormGradV3Attr { return func(m optionalAttr) { - m["num_bits"] = value + m["epsilon"] = value } } -// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { +// FusedBatchNormGradV3DataFormat sets the optional data_format attribute to value. +// +// value: The data format for y_backprop, x, x_backprop. +// Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormGradV3DataFormat(value string) FusedBatchNormGradV3Attr { return func(m optionalAttr) { - m["narrow_range"] = value + m["data_format"] = value } } -// Fake-quantize the 'inputs' tensor of type float via global float scalars `min` +// FusedBatchNormGradV3IsTraining sets the optional is_training attribute to value. // -// and `max` to 'outputs' tensor of same shape as `inputs`. +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormGradV3IsTraining(value bool) FusedBatchNormGradV3Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Gradient for batch normalization. // -// `[min; max]` define the clamping range for the `inputs` data. -// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` -// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and -// then de-quantized and output as floats in `[min; max]` interval. -// `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. // -// Before quantization, `min` and `max` values are adjusted with the following -// logic. -// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values, -// the behavior can be unexpected: -// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`. -// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`. -// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `, -// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`. +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. +// reserve_space_3: When is_training is True, a 1D Tensor for some intermediate results to be reused +// in gradient computation. When is_training is False, a dummy empty Tensor will be +// created. // -// This operation has a gradient and thus allows for training `min` and `max` -// values. -func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { +// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGradV3(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output, optional ...FusedBatchNormGradV3Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_4 tf.Output, reserve_space_5 tf.Output) { if scope.Err() != nil { return } @@ -10818,134 +10133,56 @@ func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max a(attrs) } opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxVars", + Type: "FusedBatchNormGradV3", Input: []tf.Input{ - inputs, min, max, + y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } -// ExperimentalParseExampleDatasetAttr is an optional argument to ExperimentalParseExampleDataset. -type ExperimentalParseExampleDatasetAttr func(optionalAttr) +// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. +type MaxPool3DGradAttr func(optionalAttr) -// ExperimentalParseExampleDatasetSloppy sets the optional sloppy attribute to value. -// If not specified, defaults to false -func ExperimentalParseExampleDatasetSloppy(value bool) ExperimentalParseExampleDatasetAttr { +// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { return func(m optionalAttr) { - m["sloppy"] = value + m["data_format"] = value } } -// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. +// Computes gradients of max pooling function. // // Arguments: -// -// -// dense_defaults: A dict mapping string keys to `Tensor`s. -// The keys of the dict must match the dense_keys of the feature. -// sparse_keys: A list of string keys in the examples features. -// The results for these keys will be returned as `SparseTensor` objects. -// dense_keys: A list of Ndense string Tensors (scalars). -// The keys expected in the Examples features associated with dense values. -// sparse_types: A list of `DTypes` of the same length as `sparse_keys`. -// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), -// and `tf.string` (`BytesList`) are supported. -// dense_shapes: List of tuples with the same length as `dense_keys`. -// The shape of the data for each dense feature referenced by `dense_keys`. -// Required for any input tensors identified by `dense_keys`. Must be -// either fully defined, or may contain an unknown first dimension. -// An unknown first dimension means the feature is treated as having -// a variable number of blocks, and the output shape along this dimension -// is considered unknown at graph build time. Padding is applied for -// minibatch elements smaller than the maximum number of blocks for the -// given feature along this dimension. -// output_types: The type list for the return values. -// output_shapes: The list of shapes being produced. -func ExperimentalParseExampleDataset(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ExperimentalParseExampleDatasetAttr) (handle tf.Output) { +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "ExperimentalParseExampleDataset", + Type: "MaxPool3DGrad", Input: []tf.Input{ - input_dataset, num_parallel_calls, tf.OutputList(dense_defaults), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LRNGradAttr is an optional argument to LRNGrad. -type LRNGradAttr func(optionalAttr) - -// LRNGradDepthRadius sets the optional depth_radius attribute to value. -// -// value: A depth radius. -// If not specified, defaults to 5 -func LRNGradDepthRadius(value int64) LRNGradAttr { - return func(m optionalAttr) { - m["depth_radius"] = value - } -} - -// LRNGradBias sets the optional bias attribute to value. -// -// value: An offset (usually > 0 to avoid dividing by 0). -// If not specified, defaults to 1 -func LRNGradBias(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["bias"] = value - } -} - -// LRNGradAlpha sets the optional alpha attribute to value. -// -// value: A scale factor, usually positive. -// If not specified, defaults to 1 -func LRNGradAlpha(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["alpha"] = value - } -} - -// LRNGradBeta sets the optional beta attribute to value. -// -// value: An exponent. -// If not specified, defaults to 0.5 -func LRNGradBeta(value float32) LRNGradAttr { - return func(m optionalAttr) { - m["beta"] = value - } -} - -// Gradients for Local Response Normalization. -// -// Arguments: -// input_grads: 4-D with shape `[batch, height, width, channels]`. -// input_image: 4-D with shape `[batch, height, width, channels]`. -// output_image: 4-D with shape `[batch, height, width, channels]`. -// -// Returns The gradients for LRN. -func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LRNGrad", - Input: []tf.Input{ - input_grads, input_image, output_image, + orig_input, orig_output, grad, }, Attrs: attrs, } @@ -11028,79 +10265,6 @@ func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale t return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } -// FusedBatchNormV3Attr is an optional argument to FusedBatchNormV3. -type FusedBatchNormV3Attr func(optionalAttr) - -// FusedBatchNormV3Epsilon sets the optional epsilon attribute to value. -// -// value: A small float number added to the variance of x. -// If not specified, defaults to 0.0001 -func FusedBatchNormV3Epsilon(value float32) FusedBatchNormV3Attr { - return func(m optionalAttr) { - m["epsilon"] = value - } -} - -// FusedBatchNormV3DataFormat sets the optional data_format attribute to value. -// -// value: The data format for x and y. Either "NHWC" (default) or "NCHW". -// If not specified, defaults to "NHWC" -func FusedBatchNormV3DataFormat(value string) FusedBatchNormV3Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// FusedBatchNormV3IsTraining sets the optional is_training attribute to value. -// -// value: A bool value to indicate the operation is for training (default) -// or inference. -// If not specified, defaults to true -func FusedBatchNormV3IsTraining(value bool) FusedBatchNormV3Attr { - return func(m optionalAttr) { - m["is_training"] = value - } -} - -// Batch normalization. -// -// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". -// The size of 1D Tensors matches the dimension C of the 4D Tensors. -// -// Arguments: -// x: A 4D Tensor for input data. -// scale: A 1D Tensor for scaling factor, to scale the normalized x. -// offset: A 1D Tensor for offset, to shift to the normalized x. -// mean: A 1D Tensor for population mean. Used for inference only; -// must be empty for training. -// variance: A 1D Tensor for population variance. Used for inference only; -// must be empty for training. -// -// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow -// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by -// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused -// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance -// in the cuDNN case), to be reused in the gradient computation.A 1D Tensor for some intermediate results, to be reused in the gradient -// computation for better efficiency. -func FusedBatchNormV3(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV3Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FusedBatchNormV3", - Input: []tf.Input{ - x, scale, offset, mean, variance, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5) -} - // FusedBatchNormV2Attr is an optional argument to FusedBatchNormV2. type FusedBatchNormV2Attr func(optionalAttr) @@ -11173,75 +10337,271 @@ func FusedBatchNormV2(scope *Scope, x tf.Output, scale tf.Output, offset tf.Outp return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } -// DecodeCSVAttr is an optional argument to DecodeCSV. -type DecodeCSVAttr func(optionalAttr) +// Conv3DBackpropFilterAttr is an optional argument to Conv3DBackpropFilter. +type Conv3DBackpropFilterAttr func(optionalAttr) -// DecodeCSVFieldDelim sets the optional field_delim attribute to value. -// -// value: char delimiter to separate fields in a record. -// If not specified, defaults to "," -func DecodeCSVFieldDelim(value string) DecodeCSVAttr { +// Conv3DBackpropFilterDilations sets the optional dilations attribute to value. +// If not specified, defaults to +func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr { return func(m optionalAttr) { - m["field_delim"] = value + m["dilations"] = value } } -// DecodeCSVUseQuoteDelim sets the optional use_quote_delim attribute to value. +// Computes the gradients of 3-D convolution with respect to the filter. // -// value: If false, treats double quotation marks as regular -// characters inside of the string fields (ignoring RFC 4180, Section 2, -// Bullet 5). -// If not specified, defaults to true -func DecodeCSVUseQuoteDelim(value bool) DecodeCSVAttr { - return func(m optionalAttr) { - m["use_quote_delim"] = value - } -} - -// DecodeCSVNaValue sets the optional na_value attribute to value. -// -// value: Additional string to recognize as NA/NaN. -// If not specified, defaults to "" -func DecodeCSVNaValue(value string) DecodeCSVAttr { - return func(m optionalAttr) { - m["na_value"] = value - } -} - -// DecodeCSVSelectCols sets the optional select_cols attribute to value. -// If not specified, defaults to <> -func DecodeCSVSelectCols(value []int64) DecodeCSVAttr { - return func(m optionalAttr) { - m["select_cols"] = value - } -} - -// Convert CSV records to tensors. Each column maps to one tensor. -// -// RFC 4180 format is expected for the CSV records. -// (https://tools.ietf.org/html/rfc4180) -// Note that we allow leading and trailing spaces with int or float field. +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2 // // Arguments: -// records: Each string is a record/row in the csv and all records should have -// the same format. -// record_defaults: One tensor per column of the input record, with either a -// scalar default value for that column or an empty vector if the column is -// required. -// -// Returns Each tensor will have the same shape as records. -func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) { +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DecodeCSV", + Type: "Conv3DBackpropFilter", Input: []tf.Input{ - records, tf.OutputList(record_defaults), + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for the exponential linear (Elu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Elu operation. +// outputs: The outputs of the corresponding Elu operation. +// +// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, +// `gradients` otherwise. +func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnicodeEncodeAttr is an optional argument to UnicodeEncode. +type UnicodeEncodeAttr func(optionalAttr) + +// UnicodeEncodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeEncodeErrors(value string) UnicodeEncodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeEncodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD (U+65533). +// If not specified, defaults to 65533 +func UnicodeEncodeReplacementChar(value int64) UnicodeEncodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// Encode a tensor of ints into unicode strings. +// +// Returns a vector of strings, where `output[i]` is constructed by encoding the +// Unicode codepoints in `input_values[input_splits[i]:input_splits[i+1]]` +// using `output_encoding`. +// +// --- +// +// Example: +// +// ``` +// input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100] +// input_splits = [0, 5, 10] +// output_encoding = 'UTF-8' +// +// output = ['Hello', 'World'] +// ``` +// +// Arguments: +// input_values: A 1D tensor containing the unicode codepoints that should be encoded. +// input_splits: A 1D tensor specifying how the unicode codepoints should be split into strings. +// In particular, `output[i]` is constructed by encoding the codepoints in the +// slice `input_values[input_splits[i]:input_splits[i+1]]`. +// output_encoding: Unicode encoding of the output strings. Valid encodings are: `"UTF-8", +// "UTF-16-BE", and "UTF-32-BE"`. +// +// Returns The 1-D Tensor of strings encoded from the provided unicode codepoints. +func UnicodeEncode(scope *Scope, input_values tf.Output, input_splits tf.Output, output_encoding string, optional ...UnicodeEncodeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_encoding": output_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeEncode", + Input: []tf.Input{ + input_values, input_splits, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug. +type RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve RMSProp embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the RMSProp optimization algorithm.Parameter ms updated by the RMSProp optimization algorithm.Parameter mom updated by the RMSProp optimization algorithm.Parameter gradient_accumulators updated by the RMSProp optimization algorithm. +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. +// +// Note that the hash function may change from time to time. +// This functionality will be deprecated and it's recommended to use +// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// +// Arguments: +// +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucket", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnpackAttr is an optional argument to Unpack. +type UnpackAttr func(optionalAttr) + +// UnpackAxis sets the optional axis attribute to value. +// +// value: Dimension along which to unpack. Negative values wrap around, so the +// valid range is `[-R, R)`. +// If not specified, defaults to 0 +func UnpackAxis(value int64) UnpackAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. +// +// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. +// For example, given a tensor of shape `(A, B, C, D)`; +// +// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` +// and each tensor in `output` will have shape `(B, C, D)`. (Note that the +// dimension unpacked along is gone, unlike `split`). +// +// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` +// and each tensor in `output` will have shape `(A, C, D)`. +// Etc. +// +// This is the opposite of `pack`. +// +// Arguments: +// value: 1-D or higher, with `axis` dimension size equal to `num`. +// +// +// Returns The list of tensors unpacked from `value`. +func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num": num} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unpack", + Input: []tf.Input{ + value, }, Attrs: attrs, } @@ -11252,21 +10612,1145 @@ func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, opt var idx int var err error if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("DecodeCSV", err) + scope.UpdateErr("Unpack", err) return } return output } -// Computes numerical negative value element-wise. +// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`. // -// I.e., \\(y = -x\\). -func Neg(scope *Scope, x tf.Output) (y tf.Output) { +// Each comparison returns a boolean `true` (if `input_value > threshold`) +// or and `false` otherwise. +// +// This operation is useful for Locality-Sensitive-Hashing (LSH) and other +// algorithms that use hashing approximations of cosine and `L2` distances; +// codes can be generated from an input via: +// +// ```python +// codebook_size = 50 +// codebook_bits = codebook_size * 32 +// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits], +// dtype=x.dtype, +// initializer=tf.orthogonal_initializer()) +// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.) +// codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32 +// # now codes has shape x.shape[:-1] + [codebook_size] +// ``` +// +// **NOTE**: Currently, the innermost dimension of the tensor must be divisible +// by 8. +// +// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is +// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`. +// +// Arguments: +// input: Values to compare against `threshold` and bitpack. +// threshold: Threshold to compare against. +// +// Returns The bitpacked comparisons. +func CompareAndBitpack(scope *Scope, input tf.Output, threshold tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Neg", + Type: "CompareAndBitpack", + Input: []tf.Input{ + input, threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AvgPoolAttr is an optional argument to AvgPool. +type AvgPoolAttr func(optionalAttr) + +// AvgPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolDataFormat(value string) AvgPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs average pooling on the input. +// +// Each entry in `output` is the mean of the corresponding size `ksize` +// window in `value`. +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// ksize: The size of the sliding window for each dimension of `value`. +// strides: The stride of the sliding window for each dimension of `value`. +// padding: The type of padding algorithm to use. +// +// Returns The average pooled output tensor. +func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. +type ResourceApplyProximalGradientDescentAttr func(optionalAttr) + +// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If True, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' as FOBOS algorithm with fixed learning rate. +// +// prox_v = var - alpha * delta +// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// delta: The change. +// +// Returns the created operation. +func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalGradientDescent", + Input: []tf.Input{ + var_, alpha, l1, l2, delta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient of morphological 2-D dilation with respect to the filter. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. +// strides: 1-D of length 4. The stride of the sliding window for each dimension of +// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: 1-D of length 4. The input stride for atrous morphological dilation. +// Must be: `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 3-D with shape `[filter_height, filter_width, depth]`. +func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2DBackpropFilter", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNBackpropV3Attr is an optional argument to CudnnRNNBackpropV3. +type CudnnRNNBackpropV3Attr func(optionalAttr) + +// CudnnRNNBackpropV3RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNBackpropV3RnnMode(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNBackpropV3InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNBackpropV3InputMode(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNBackpropV3Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNBackpropV3Direction(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNBackpropV3Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Dropout(value float32) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNBackpropV3Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Seed(value int64) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNBackpropV3Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Seed2(value int64) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNBackpropV3TimeMajor sets the optional time_major attribute to value. +// If not specified, defaults to true +func CudnnRNNBackpropV3TimeMajor(value bool) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["time_major"] = value + } +} + +// Backprop step of CudnnRNNV3. +// +// Compute the backprop of both data and weights in a RNN. Takes an extra +// "sequence_lengths" input than CudnnRNNBackprop. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, input_size]. If time_major is false, the shape is +// [batch_size, seq_length, input_size]. +// input_h: If time_major is true, this is a 3-D tensor with the shape of +// [num_layer * dir, batch_size, num_units]. If time_major is false, the shape +// is [batch_size, num_layer * dir, num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// sequence_lengths: a vector of lengths of each input sequence. +// output: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, dir * num_units]. If time_major is false, the +// shape is [batch_size, seq_length, dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// output_backprop: A 3-D tensor with the same shape as output in the forward pass. +// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward +// pass. +// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward +// pass. +// time_major: Indicates whether the input/output format is time major or batch +// major. +// reserve_space: The same reserve_space produced in the forward operation. +// input_backprop: The backprop to input in the forward pass. Has the same shape +// as input. +// input_h_backprop: The backprop to input_h in the forward pass. Has the same +// shape as input_h. +// input_c_backprop: The backprop to input_c in the forward pass. Has the same +// shape as input_c. +// params_backprop: The backprop to the params buffer in the forward pass. Has the +// same shape as params. +func CudnnRNNBackpropV3(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, sequence_lengths tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, host_reserved tf.Output, optional ...CudnnRNNBackpropV3Attr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNBackpropV3", + Input: []tf.Input{ + input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// OutfeedDequeueAttr is an optional argument to OutfeedDequeue. +type OutfeedDequeueAttr func(optionalAttr) + +// OutfeedDequeueDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func OutfeedDequeueDeviceOrdinal(value int64) OutfeedDequeueAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Retrieves a single tensor from the computation outfeed. +// +// This operation will block indefinitely until data is available. +// +// Arguments: +// dtype: The type of elements in the tensor. +// shape: The shape of the tensor. +// +// Returns A tensor that will be read from the device outfeed. +func OutfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...OutfeedDequeueAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OutfeedDequeue", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. +// +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` +// +// if < 0, `scale * features` otherwise. +// +// To be used together with +// `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. +// For correct dropout, use `tf.contrib.nn.alpha_dropout`. +// +// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) +func Selu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Selu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the number of tensors in the input tensor list. +// +// input_handle: the input list +// length: the number of tensors in the list +func TensorListLength(scope *Scope, input_handle tf.Output) (length tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListLength", + Input: []tf.Input{ + input_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample. +type ParseSingleSequenceExampleAttr func(optionalAttr) + +// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value. +// +// value: A list of Ncontext_sparse types; the data types of data in +// each context Feature given in context_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["context_sparse_types"] = value + } +} + +// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_types"] = value + } +} + +// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value. +// +// value: A list of Ncontext_dense shapes; the shapes of data in +// each context Feature given in context_dense_keys. +// The number of elements in the Feature corresponding to context_dense_key[j] +// must always equal context_dense_shapes[j].NumEntries(). +// The shape of context_dense_values[j] will match context_dense_shapes[j]. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["context_dense_shapes"] = value + } +} + +// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value. +// +// value: A list of Nfeature_list_sparse types; the data types +// of data in each FeatureList given in feature_list_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_sparse_types"] = value + } +} + +// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value. +// +// value: A list of Nfeature_list_dense shapes; the shapes of +// data in each FeatureList given in feature_list_dense_keys. +// The shape of each Feature in the FeatureList corresponding to +// feature_list_dense_key[j] must always equal +// feature_list_dense_shapes[j].NumEntries(). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_shapes"] = value + } +} + +// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. +// +// Arguments: +// serialized: A scalar containing a binary serialized SequenceExample proto. +// feature_list_dense_missing_assumed_empty: A vector listing the +// FeatureList keys which may be missing from the SequenceExample. If the +// associated FeatureList is missing, it is treated as empty. By default, +// any FeatureList not listed in this vector must exist in the SequenceExample. +// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars). +// The keys expected in the Examples' features associated with context_sparse +// values. +// context_dense_keys: A list of Ncontext_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' context features associated with +// dense values. +// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors +// (scalars). The keys expected in the FeatureLists associated with sparse +// values. +// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' feature_lists associated +// with lists of dense values. +// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). +// context_dense_defaults[j] provides default values +// when the SequenceExample's context map lacks context_dense_key[j]. +// If an empty Tensor is provided for context_dense_defaults[j], +// then the Feature context_dense_keys[j] is required. +// The input type is inferred from context_dense_defaults[j], even when it's +// empty. If context_dense_defaults[j] is not empty, its shape must match +// context_dense_shapes[j]. +// debug_name: A scalar containing the name of the serialized proto. +// May contain, for example, table key (descriptive) name for the +// corresponding serialized proto. This is purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty scalar if no name is available. +func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseSingleSequenceExample", + Input: []tf.Input{ + serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values +} + +// Computes gradients for SparseSegmentMean. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentMean op. +// indices: indices passed to the corresponding SparseSegmentMean op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. +func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMeanGrad", + Input: []tf.Input{ + grad, indices, segment_ids, output_dim0, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise AND of `x` and `y`. +// +// The result will have those bits set, that are set in both `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseAnd", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a tree resource and returns a handle to it. +// +// Arguments: +// tree_handle: Handle to the tree resource to be created. +// tree_config: Serialized proto string of the boosted_trees.Tree. +// +// Returns the created operation. +func TensorForestCreateTreeVariable(scope *Scope, tree_handle tf.Output, tree_config tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestCreateTreeVariable", + Input: []tf.Input{ + tree_handle, tree_config, + }, + } + return scope.AddOperation(opspec) +} + +// Transforms a tf.Example proto (as a string) into typed tensors. +// +// Arguments: +// serialized: A vector containing a batch of binary serialized Example protos. +// dense_defaults: A list of Tensors (some may be empty), whose length matches +// the length of `dense_keys`. dense_defaults[j] provides default values +// when the example's feature_map lacks dense_key[j]. If an empty Tensor is +// provided for dense_defaults[j], then the Feature dense_keys[j] is required. +// The input type is inferred from dense_defaults[j], even when it's empty. +// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, +// then the shape of dense_defaults[j] must match that of dense_shapes[j]. +// If dense_shapes[j] has an undefined major dimension (variable strides dense +// feature), dense_defaults[j] must contain a single element: +// the padding element. +// num_sparse: The number of sparse features to be parsed from the example. This +// must match the lengths of `sparse_keys` and `sparse_types`. +// sparse_keys: A list of `num_sparse` strings. +// The keys expected in the Examples' features associated with sparse values. +// dense_keys: The keys expected in the Examples' features associated with dense +// values. +// sparse_types: A list of `num_sparse` types; the data types of data in each +// Feature given in sparse_keys. +// Currently the ParseSingleExample op supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// dense_shapes: The shapes of data in each Feature given in dense_keys. +// The length of this list must match the length of `dense_keys`. The +// number of elements in the Feature corresponding to dense_key[j] must +// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == +// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] +// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1, +// ..., DN), the shape of the output Tensor dense_values[j] will be (M, +// D1, .., DN), where M is the number of blocks of elements of length +// D1 * .... * DN, in the input. +func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes} + opspec := tf.OpSpec{ + Type: "ParseSingleExample", + Input: []tf.Input{ + serialized, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + return sparse_indices, sparse_values, sparse_shapes, dense_values +} + +// Adds Tensor 'bias' to Tensor 'input' for Quantized types. +// +// Broadcasts the values of bias on dimensions 0..N-2 of 'input'. +// +// Arguments: +// +// bias: A 1D bias Tensor with size matching the last dimension of 'input'. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// min_bias: The float value that the lowest quantized bias value represents. +// max_bias: The float value that the highest quantized bias value represents. +// +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "QuantizedBiasAdd", + Input: []tf.Input{ + input, bias, min_input, max_input, min_bias, max_bias, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// UnbatchGradAttr is an optional argument to UnbatchGrad. +type UnbatchGradAttr func(optionalAttr) + +// UnbatchGradContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func UnbatchGradContainer(value string) UnbatchGradAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// UnbatchGradSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func UnbatchGradSharedName(value string) UnbatchGradAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Gradient of Unbatch. +// +// Acts like Batch but using the given batch_index index of batching things as they +// become available. This ensures that the gradients are propagated back in the +// same session which did the forward pass. +// +// original_input: The input to the Unbatch operation this is the gradient of. +// batch_index: The batch_index given to the Unbatch operation this is the gradient +// of. +// grad: The downstream gradient. +// id: The id scalar emitted by Batch. +// batched_grad: The return value, either an empty tensor or the batched gradient. +// container: Container to control resource sharing. +// shared_name: Instances of UnbatchGrad with the same container and shared_name +// are assumed to possibly belong to the same batch. If left empty, the op name +// will be used as the shared name. +func UnbatchGrad(scope *Scope, original_input tf.Output, batch_index tf.Output, grad tf.Output, id tf.Output, optional ...UnbatchGradAttr) (batched_grad tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnbatchGrad", + Input: []tf.Input{ + original_input, batch_index, grad, id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BiasAddAttr is an optional argument to BiasAdd. +type BiasAddAttr func(optionalAttr) + +// BiasAddDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the bias tensor will be added to the last dimension +// of the value tensor. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// The tensor will be added to "in_channels", the third-to-the-last +// dimension. +// If not specified, defaults to "NHWC" +func BiasAddDataFormat(value string) BiasAddAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Adds `bias` to `value`. +// +// This is a special case of `tf.add` where `bias` is restricted to be 1-D. +// Broadcasting is supported, so `value` may have any number of dimensions. +// +// Arguments: +// value: Any number of dimensions. +// bias: 1-D with size the last dimension of `value`. +// +// Returns Broadcasted sum of `value` and `bias`. +func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BiasAdd", + Input: []tf.Input{ + value, bias, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a vector of brain.Example protos (as strings) into typed tensors. +// +// Arguments: +// serialized: A vector containing a batch of binary serialized Example protos. +// names: A vector containing the names of the serialized protos. +// May contain, for example, table key (descriptive) names for the +// corresponding serialized protos. These are purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty vector if no names are available. +// If non-empty, this vector must be the same length as "serialized". +// sparse_keys: A list of Nsparse string Tensors (scalars). +// The keys expected in the Examples' features associated with sparse values. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples' features associated with dense values. +// dense_defaults: A list of Ndense Tensors (some may be empty). +// dense_defaults[j] provides default values +// when the example's feature_map lacks dense_key[j]. If an empty Tensor is +// provided for dense_defaults[j], then the Feature dense_keys[j] is required. +// The input type is inferred from dense_defaults[j], even when it's empty. +// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, +// then the shape of dense_defaults[j] must match that of dense_shapes[j]. +// If dense_shapes[j] has an undefined major dimension (variable strides dense +// feature), dense_defaults[j] must contain a single element: +// the padding element. +// sparse_types: A list of Nsparse types; the data types of data in each Feature +// given in sparse_keys. +// Currently the ParseExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// dense_shapes: A list of Ndense shapes; the shapes of data in each Feature +// given in dense_keys. +// The number of elements in the Feature corresponding to dense_key[j] +// must always equal dense_shapes[j].NumEntries(). +// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output +// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): +// The dense outputs are just the inputs row-stacked by batch. +// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case +// the shape of the output Tensor dense_values[j] will be +// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks +// of elements of length D1 * .... * DN, across all minibatch entries +// in the input. Any minibatch entry with less than M blocks of elements of +// length D1 * ... * DN will be padded with the corresponding default_value +// scalar element along the second dimension. +func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes} + opspec := tf.OpSpec{ + Type: "ParseExample", + Input: []tf.Input{ + serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + return sparse_indices, sparse_values, sparse_shapes, dense_values +} + +// An Op to sum inputs across replicated TPU instances. +// +// Each instance supplies its own input. +// +// For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`. +// Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0, +// and `B, D, F, H` as group 1. Thus we get the outputs: +// `[A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]`. +// +// Arguments: +// input: The local input to the sum. +// group_assignment: An int32 tensor with shape +// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the +// replica ids in the ith subgroup. +// +// Returns The sum of all the distributed inputs. +func CrossReplicaSum(scope *Scope, input tf.Output, group_assignment tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CrossReplicaSum", + Input: []tf.Input{ + input, group_assignment, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 2D fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapSizeAttr is an optional argument to MapSize. +type MapSizeAttr func(optionalAttr) + +// MapSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapSizeCapacity(value int64) MapSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapSizeMemoryLimit(value int64) MapSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapSizeContainer(value string) MapSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapSizeSharedName(value string) MapSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func MapSize(scope *Scope, dtypes []tf.DataType, optional ...MapSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomPoissonV2Attr is an optional argument to RandomPoissonV2. +type RandomPoissonV2Attr func(optionalAttr) + +// RandomPoissonV2Seed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomPoissonV2Seed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// RandomPoissonV2Dtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_INT64 +func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from the Poisson distribution(s) described by rate. +// +// This op uses two algorithms, depending on rate. If rate >= 10, then +// the algorithm by Hormann is used to acquire samples via +// transformation-rejection. +// See http://www.sciencedirect.com/science/article/pii/0167668793909974. +// +// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform +// random variables. +// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer +// Programming, Volume 2. Addison Wesley +// +// Arguments: +// shape: 1-D integer tensor. Shape of independent samples to draw from each +// distribution described by the shape parameters given in rate. +// rate: A tensor in which each scalar is a "rate" parameter describing the +// associated poisson distribution. +// +// Returns A tensor with shape `shape + shape(rate)`. Each slice +// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for +// `rate[i0, i1, ...iN]`. +func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomPoissonV2", + Input: []tf.Input{ + shape, rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient of morphological 2-D dilation with respect to the input. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. +// strides: 1-D of length 4. The stride of the sliding window for each dimension of +// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: 1-D of length 4. The input stride for atrous morphological dilation. +// Must be: `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, in_height, in_width, depth]`. +func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes Psi, the derivative of Lgamma (the log of the absolute value of +// +// `Gamma(x)`), element-wise. +func Digamma(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Digamma", Input: []tf.Input{ x, }, @@ -11275,6 +11759,2837 @@ func Neg(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// QuantizedMulAttr is an optional argument to QuantizedMul. +type QuantizedMulAttr func(optionalAttr) + +// QuantizedMulToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// Returns x * y element-wise, working on quantized buffers. +// +// Arguments: +// +// +// min_x: The float value that the lowest quantized `x` value represents. +// max_x: The float value that the highest quantized `x` value represents. +// min_y: The float value that the lowest quantized `y` value represents. +// max_y: The float value that the highest quantized `y` value represents. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +// +// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about +// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMul", + Input: []tf.Input{ + x, y, min_x, max_x, min_y, max_y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// CudnnRNNAttr is an optional argument to CudnnRNN. +type CudnnRNNAttr func(optionalAttr) + +// CudnnRNNRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNRnnMode(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNInputMode(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNDirection(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNDropout(value float32) CudnnRNNAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNSeed(value int64) CudnnRNNAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNSeed2(value int64) CudnnRNNAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNIsTraining sets the optional is_training attribute to value. +// If not specified, defaults to true +func CudnnRNNIsTraining(value bool) CudnnRNNAttr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// A RNN backed by cuDNN. +// +// Computes the RNN from the input and initial states, with respect to the params +// buffer. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// is_training: Indicates whether this operation is used for inferenece or +// training. +// reserve_space: An opaque tensor that can be used in backprop calculation. It +// is only produced if is_training is false. +func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNAttr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNN", + Input: []tf.Input{ + input, input_h, input_c, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// TruncatedNormalAttr is an optional argument to TruncatedNormal. +type TruncatedNormalAttr func(optionalAttr) + +// TruncatedNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func TruncatedNormalSeed(value int64) TruncatedNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// TruncatedNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func TruncatedNormalSeed2(value int64) TruncatedNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with random truncated normal +// values. +func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TruncatedNormal", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gather slices from `params` into a Tensor with shape specified by `indices`. +// +// `indices` is an K-dimensional integer tensor, best thought of as a +// (K-1)-dimensional tensor of indices into `params`, where each element defines a +// slice of `params`: +// +// output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]] +// +// Whereas in `tf.gather` `indices` defines slices into the first +// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the +// first `N` dimensions of `params`, where `N = indices.shape[-1]`. +// +// The last dimension of `indices` can be at most the rank of +// `params`: +// +// indices.shape[-1] <= params.rank +// +// The last dimension of `indices` corresponds to elements +// (if `indices.shape[-1] == params.rank`) or slices +// (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]` +// of `params`. The output tensor has shape +// +// indices.shape[:-1] + params.shape[indices.shape[-1]:] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, a 0 is stored in the +// corresponding output value. +// +// Some examples below. +// +// Simple indexing into a matrix: +// +// ```python +// indices = [[0, 0], [1, 1]] +// params = [['a', 'b'], ['c', 'd']] +// output = ['a', 'd'] +// ``` +// +// Slice indexing into a matrix: +// +// ```python +// indices = [[1], [0]] +// params = [['a', 'b'], ['c', 'd']] +// output = [['c', 'd'], ['a', 'b']] +// ``` +// +// Indexing into a 3-tensor: +// +// ```python +// indices = [[1]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[['a1', 'b1'], ['c1', 'd1']]] +// +// +// indices = [[0, 1], [1, 0]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [['c0', 'd0'], ['a1', 'b1']] +// +// +// indices = [[0, 0, 1], [1, 0, 1]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = ['b0', 'b1'] +// ``` +// +// Batched indexing into a matrix: +// +// ```python +// indices = [[[0, 0]], [[0, 1]]] +// params = [['a', 'b'], ['c', 'd']] +// output = [['a'], ['b']] +// ``` +// +// Batched slice indexing into a matrix: +// +// ```python +// indices = [[[1]], [[0]]] +// params = [['a', 'b'], ['c', 'd']] +// output = [[['c', 'd']], [['a', 'b']]] +// ``` +// +// Batched indexing into a 3-tensor: +// +// ```python +// indices = [[[1]], [[0]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[[['a1', 'b1'], ['c1', 'd1']]], +// [[['a0', 'b0'], ['c0', 'd0']]]] +// +// indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[['c0', 'd0'], ['a1', 'b1']], +// [['a0', 'b0'], ['c1', 'd1']]] +// +// +// indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [['b0', 'b1'], ['d0', 'c1']] +// ``` +// +// See also `tf.gather` and `tf.batch_gather`. +// +// Arguments: +// params: The tensor from which to gather values. +// indices: Index tensor. +// +// Returns Values from `params` gathered from indices given by `indices`, with +// shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`. +func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GatherNd", + Input: []tf.Input{ + params, indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) + +// MaxPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolDataFormat(value string) MaxPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Advance the counter of a counter-based RNG. +// +// The state of the RNG after +// `rng_skip(n)` will be the same as that after `stateful_uniform([n])` +// (or any other distribution). The actual increment added to the +// counter is an unspecified implementation detail. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// delta: The amount of advancement. +// +// Returns the created operation. +func RngSkip(scope *Scope, resource tf.Output, algorithm tf.Output, delta tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RngSkip", + Input: []tf.Input{ + resource, algorithm, delta, + }, + } + return scope.AddOperation(opspec) +} + +// StatefulUniformFullIntAttr is an optional argument to StatefulUniformFullInt. +type StatefulUniformFullIntAttr func(optionalAttr) + +// StatefulUniformFullIntDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_UINT64 +func StatefulUniformFullIntDtype(value tf.DataType) StatefulUniformFullIntAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random integers from a uniform distribution. +// +// The generated values are uniform integers covering the whole range of `dtype`. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns Random values with specified shape. +func StatefulUniformFullInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformFullIntAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulUniformFullInt", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the max of x and y (i.e. x > y ? x : y) element-wise. +// +// *NOTE*: `Maximum` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Maximum", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RequantizePerChannelAttr is an optional argument to RequantizePerChannel. +type RequantizePerChannelAttr func(optionalAttr) + +// RequantizePerChannelOutType sets the optional out_type attribute to value. +// +// value: The quantized type of output tensor that needs to be converted. +// If not specified, defaults to DT_QUINT8 +func RequantizePerChannelOutType(value tf.DataType) RequantizePerChannelAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Requantizes input with min and max values known per channel. +// +// Arguments: +// input: The original input tensor. +// input_min: The minimum value of the input tensor +// input_max: The maximum value of the input tensor. +// requested_output_min: The minimum value of the output tensor requested. +// requested_output_max: The maximum value of the output tensor requested. +// +// Returns Output tensor.The minimum value of the final output tensorThe maximum value of the final output tensor. +func RequantizePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, optional ...RequantizePerChannelAttr) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RequantizePerChannel", + Input: []tf.Input{ + input, input_min, input_max, requested_output_min, requested_output_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatefulUniformAttr is an optional argument to StatefulUniform. +type StatefulUniformAttr func(optionalAttr) + +// StatefulUniformDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulUniformDtype(value tf.DataType) StatefulUniformAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns Random values with specified shape. +func StatefulUniform(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulUniform", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SnapshotDatasetAttr is an optional argument to SnapshotDataset. +type SnapshotDatasetAttr func(optionalAttr) + +// SnapshotDatasetCompression sets the optional compression attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetCompression(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["compression"] = value + } +} + +// SnapshotDatasetReaderPathPrefix sets the optional reader_path_prefix attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetReaderPathPrefix(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["reader_path_prefix"] = value + } +} + +// SnapshotDatasetWriterPathPrefix sets the optional writer_path_prefix attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetWriterPathPrefix(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["writer_path_prefix"] = value + } +} + +// Creates a dataset that will write to / read from a snapshot. +// +// This dataset attempts to determine whether a valid snapshot exists at the +// `snapshot_path`, and reads from the snapshot in lieu of using `input_dataset`. +// If not, it will run the preprocessing pipeline as usual, and write out a +// snapshot of the data processed for future use. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// path: The path we should write snapshots to / read snapshots from. +// +// +func SnapshotDataset(scope *Scope, input_dataset tf.Output, path tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...SnapshotDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SnapshotDataset", + Input: []tf.Input{ + input_dataset, path, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CTCLossAttr is an optional argument to CTCLoss. +type CTCLossAttr func(optionalAttr) + +// CTCLossPreprocessCollapseRepeated sets the optional preprocess_collapse_repeated attribute to value. +// +// value: Scalar, if true then repeated labels are +// collapsed prior to the CTC calculation. +// If not specified, defaults to false +func CTCLossPreprocessCollapseRepeated(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["preprocess_collapse_repeated"] = value + } +} + +// CTCLossCtcMergeRepeated sets the optional ctc_merge_repeated attribute to value. +// +// value: Scalar. If set to false, *during* CTC calculation +// repeated non-blank labels will not be merged and are interpreted as +// individual labels. This is a simplified version of CTC. +// If not specified, defaults to true +func CTCLossCtcMergeRepeated(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["ctc_merge_repeated"] = value + } +} + +// CTCLossIgnoreLongerOutputsThanInputs sets the optional ignore_longer_outputs_than_inputs attribute to value. +// +// value: Scalar. If set to true, during CTC +// calculation, items that have longer output sequences than input sequences +// are skipped: they don't contribute to the loss term and have zero-gradient. +// If not specified, defaults to false +func CTCLossIgnoreLongerOutputsThanInputs(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["ignore_longer_outputs_than_inputs"] = value + } +} + +// Calculates the CTC Loss (log probability) for each batch entry. Also calculates +// +// the gradient. This class performs the softmax operation for you, so inputs +// should be e.g. linear projections of outputs by an LSTM. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// labels_indices: The indices of a `SparseTensor`. +// `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for +// `(batch b, time t)`. +// labels_values: The values (labels) associated with the given batch and time. +// sequence_length: A vector containing sequence lengths (batch). +// +// Returns A vector (batch) containing log-probabilities.The gradient of `loss`. 3-D, shape: +// `(max_time x batch_size x num_classes)`. +func CTCLoss(scope *Scope, inputs tf.Output, labels_indices tf.Output, labels_values tf.Output, sequence_length tf.Output, optional ...CTCLossAttr) (loss tf.Output, gradient tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCLoss", + Input: []tf.Input{ + inputs, labels_indices, labels_values, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// RandomUniformAttr is an optional argument to RandomUniform. +type RandomUniformAttr func(optionalAttr) + +// RandomUniformSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomUniformSeed(value int64) RandomUniformAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomUniformSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomUniformSeed2(value int64) RandomUniformAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with uniform random values. +func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomUniform", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PrelinearizeAttr is an optional argument to Prelinearize. +type PrelinearizeAttr func(optionalAttr) + +// PrelinearizeShape sets the optional shape attribute to value. +// +// value: The shape of the tensor. +// If not specified, defaults to <> +func PrelinearizeShape(value tf.Shape) PrelinearizeAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// PrelinearizeLayout sets the optional layout attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence. If a layout +// attribute is passed but its values are all -1 the layout will be computed by +// the infeed operation. +// If not specified, defaults to <> +func PrelinearizeLayout(value []int64) PrelinearizeAttr { + return func(m optionalAttr) { + m["layout"] = value + } +} + +// An op which linearizes one Tensor value to an opaque variant tensor. +// +// Arguments: +// input: A tensor that will be linearized. +func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Prelinearize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Locks a mutex resource. The output is the lock. So long as the lock tensor +// +// is alive, any other request to use `MutexLock` with this mutex will wait. +// +// This is particularly useful for creating a critical section when used in +// conjunction with `MutexLockIdentity`: +// +// ```python +// +// mutex = mutex_v2( +// shared_name=handle_name, container=container, name=name) +// +// def execute_in_critical_section(fn, *args, **kwargs): +// lock = gen_resource_variable_ops.mutex_lock(mutex) +// +// with ops.control_dependencies([lock]): +// r = fn(*args, **kwargs) +// +// with ops.control_dependencies(nest.flatten(r)): +// with ops.colocate_with(mutex): +// ensure_lock_exists = mutex_lock_identity(lock) +// +// # Make sure that if any element of r is accessed, all of +// # them are executed together. +// r = nest.map_structure(tf.identity, r) +// +// with ops.control_dependencies([ensure_lock_exists]): +// return nest.map_structure(tf.identity, r) +// ``` +// +// While `fn` is running in the critical section, no other functions which wish to +// use this critical section may run. +// +// Often the use case is that two executions of the same graph, in parallel, +// wish to run `fn`; and we wish to ensure that only one of them executes +// at a time. This is especially important if `fn` modifies one or more +// variables at a time. +// +// It is also useful if two separate functions must share a resource, but we +// wish to ensure the usage is exclusive. +// +// Arguments: +// mutex: The mutex resource to lock. +// +// Returns A tensor that keeps a shared pointer to a lock on the mutex; +// when the Tensor is destroyed, the use count on the shared pointer is decreased +// by 1. When it reaches 0, the lock is released. +func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MutexLock", + Input: []tf.Input{ + mutex, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Divides sparse updates into the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] /= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] /= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterDiv", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Does nothing. Only useful as a placeholder for control edges. +// +// Returns the created operation. +func NoOp(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NoOp", + } + return scope.AddOperation(opspec) +} + +// LRNGradAttr is an optional argument to LRNGrad. +type LRNGradAttr func(optionalAttr) + +// LRNGradDepthRadius sets the optional depth_radius attribute to value. +// +// value: A depth radius. +// If not specified, defaults to 5 +func LRNGradDepthRadius(value int64) LRNGradAttr { + return func(m optionalAttr) { + m["depth_radius"] = value + } +} + +// LRNGradBias sets the optional bias attribute to value. +// +// value: An offset (usually > 0 to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNGradBias(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["bias"] = value + } +} + +// LRNGradAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNGradAlpha(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNGradBeta sets the optional beta attribute to value. +// +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNGradBeta(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Gradients for Local Response Normalization. +// +// Arguments: +// input_grads: 4-D with shape `[batch, height, width, channels]`. +// input_image: 4-D with shape `[batch, height, width, channels]`. +// output_image: 4-D with shape `[batch, height, width, channels]`. +// +// Returns The gradients for LRN. +func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LRNGrad", + Input: []tf.Input{ + input_grads, input_image, output_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Splits a tensor into `num_split` tensors along one dimension. +// +// Arguments: +// value: The tensor to split. +// size_splits: list containing the sizes of each output tensor along the split +// dimension. Must sum to the dimension of value along split_dim. +// Can contain one -1 indicating that dimension is to be inferred. +// axis: 0-D. The dimension along which to split. Must be in the range +// `[-rank(value), rank(value))`. +// +// +// Returns Tensors whose shape matches that of `value` +// except along `axis`, where their sizes are +// `size_splits[i]`. +func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_split": num_split} + opspec := tf.OpSpec{ + Type: "SplitV", + Input: []tf.Input{ + value, size_splits, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("SplitV", err) + return + } + return output +} + +// Multiplies sparse updates into the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] *= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] *= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMul(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterMul", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient for the inverse of `x` wrt its input. +// +// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` +// is the corresponding input gradient. +func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReciprocalGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds sparse updates to the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] += updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] += updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions add. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterAdd", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// RealAttr is an optional argument to Real. +type RealAttr func(optionalAttr) + +// RealTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func RealTout(value tf.DataType) RealAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the real part of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the real part of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real +// part returned by this operation and *b* is the imaginary part. +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.real(input) ==> [-2.25, 3.25] +// ``` +func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Real", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize. +type OrderedMapIncompleteSizeAttr func(optionalAttr) + +// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapIncompleteSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of incomplete elements in the underlying container. +func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapIncompleteSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Convert the quantized 'input' tensor into a lower-precision 'output', using the +// +// actual distribution of the values to maximize the usage of the lower bit depth +// and adjusting the output min and max ranges accordingly. +// +// [input_min, input_max] are scalar floats that specify the range for the float +// interpretation of the 'input' data. For example, if input_min is -1.0f and +// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 +// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. +// +// This operator tries to squeeze as much precision as possible into an output with +// a lower bit depth by calculating the actual min and max values found in the +// data. For example, maybe that quint16 input has no values lower than 16,384 and +// none higher than 49,152. That means only half the range is actually needed, all +// the float interpretations are between -0.5f and 0.5f, so if we want to compress +// the data into a quint8 output, we can use that range rather than the theoretical +// -1.0f to 1.0f that is suggested by the input min and max. +// +// In practice, this is most useful for taking output from operations like +// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and +// may have large potential output ranges, but in practice have a distribution of +// input values that only uses a small fraction of the possible range. By feeding +// that output into this operator, we can reduce it from 32 bits down to 8 with +// minimal loss of accuracy. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// out_type: The type of the output. Should be a lower bit depth than Tinput. +// +// Returns The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "QuantizeDownAndShrinkRange", + Input: []tf.Input{ + input, input_min, input_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes softmax activations. +// +// For each batch `i` and class `j` we have +// +// $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ +// +// Arguments: +// logits: 2-D with shape `[batch_size, num_classes]`. +// +// Returns Same shape as `logits`. +func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softmax", + Input: []tf.Input{ + logits, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdadeltaParametersAttr is an optional argument to LoadTPUEmbeddingAdadeltaParameters. +type LoadTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingAdadeltaParametersTableId(value int64) LoadTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersTableName(value string) LoadTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Adadelta embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adadelta optimization algorithm. +// accumulators: Value of accumulators used in the Adadelta optimization algorithm. +// updates: Value of updates used in the Adadelta optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdadeltaParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdadeltaParameters", + Input: []tf.Input{ + parameters, accumulators, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient for the inverse of `x` wrt its input. +// +// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` +// is the corresponding input gradient. +func InvGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InvGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesEnsembleResourceHandleOpAttr is an optional argument to BoostedTreesEnsembleResourceHandleOp. +type BoostedTreesEnsembleResourceHandleOpAttr func(optionalAttr) + +// BoostedTreesEnsembleResourceHandleOpContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func BoostedTreesEnsembleResourceHandleOpContainer(value string) BoostedTreesEnsembleResourceHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// BoostedTreesEnsembleResourceHandleOpSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func BoostedTreesEnsembleResourceHandleOpSharedName(value string) BoostedTreesEnsembleResourceHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a BoostedTreesEnsembleResource +func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTreesEnsembleResourceHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesEnsembleResourceHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts a value from the current value of a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to +// see the decremented value or a subsequent newer one. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value by which the variable will be incremented. +// +// Returns the created operation. +func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignSubVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// Returns the element-wise max of two SparseTensors. +// +// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// +// Arguments: +// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, in the canonical lexicographic ordering. +// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. +// a_shape: 1-D. Shape of the input SparseTensor. +// b_indices: counterpart to `a_indices` for the other operand. +// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. +// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// +// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. +func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSparseMaximum", + Input: []tf.Input{ + a_indices, a_values, a_shape, b_indices, b_values, b_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Batch normalization. +// +// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() +// +// This op is deprecated. Prefer `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// beta: A 1D beta Tensor with size matching the last dimension of t. +// An offset to be added to the normalized tensor. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this tensor will be multiplied +// with the normalized tensor. +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "BatchNormWithGlobalNormalization", + Input: []tf.Input{ + t, m, v, beta, gamma, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softmax cross entropy cost and gradients to backpropagate. +// +// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept +// a matrix of label probabilities, but rather a single label per row +// of features. This label is considered to have probability 1.0 for the +// given row. +// +// Inputs are the logits, not probabilities. +// +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size vector with values in [0, num_classes). +// This is the label for the given minibatch entry. +// +// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). +func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes fingerprints of the input strings. +// +// Arguments: +// input: vector of strings to compute fingerprints on. +// +// Returns a (N,2) shaped matrix where N is the number of elements in the input +// vector. Each row contains the low and high parts of the fingerprint. +func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SdcaFprint", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes rectified linear 6 gradients for a Relu6 operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Relu6 operation. +// features: The features passed as input to the corresponding Relu6 operation, or +// its output; using either one produces the same result. +// +// Returns The gradients: +// `gradients * (features > 0) * (features < 6)`. +func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu6Grad", + Input: []tf.Input{ + gradients, features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Sets the index-th position of the list to contain the given tensor. +// +// input_handle: the list +// index: the position in the list to which the tensor will be assigned +// item: the element to be assigned to that position +// output_handle: the new list, with the element in the proper position +// +func TensorListSetItem(scope *Scope, input_handle tf.Output, index tf.Output, item tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListSetItem", + Input: []tf.Input{ + input_handle, index, item, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the value of the tensor specified by its handle. +// +// Arguments: +// handle: The handle for a tensor stored in the session state. +// dtype: The type of the output value. +// +// Returns The tensor for the given handle. +func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "GetSessionTensor", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AvgPool3DAttr is an optional argument to AvgPool3D. +type AvgPool3DAttr func(optionalAttr) + +// AvgPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func AvgPool3DDataFormat(value string) AvgPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D average pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The average pooled output tensor. +func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns 0 if x == 0, and x / y otherwise, elementwise. +func Xdivy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Xdivy", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodePaddedRawAttr is an optional argument to DecodePaddedRaw. +type DecodePaddedRawAttr func(optionalAttr) + +// DecodePaddedRawLittleEndian sets the optional little_endian attribute to value. +// +// value: Whether the input `input_bytes` is in little-endian order. Ignored for +// `out_type` values that are stored in a single byte, like `uint8` +// If not specified, defaults to true +func DecodePaddedRawLittleEndian(value bool) DecodePaddedRawAttr { + return func(m optionalAttr) { + m["little_endian"] = value + } +} + +// Reinterpret the bytes of a string as a vector of numbers. +// +// Arguments: +// input_bytes: Tensor of string to be decoded. +// fixed_length: Length in bytes for each element of the decoded output. Must be a multiple +// of the size of the output type. +// +// +// Returns A Tensor with one more dimension than the input `bytes`. The added dimension +// will have size equal to the length of the elements of `bytes` divided by the +// number of bytes to represent `out_type`. +func DecodePaddedRaw(scope *Scope, input_bytes tf.Output, fixed_length tf.Output, out_type tf.DataType, optional ...DecodePaddedRawAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodePaddedRaw", + Input: []tf.Input{ + input_bytes, fixed_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TryRpcAttr is an optional argument to TryRpc. +type TryRpcAttr func(optionalAttr) + +// TryRpcProtocol sets the optional protocol attribute to value. +// +// value: RPC protocol to use. Empty string means use the default protocol. +// Options include 'grpc'. +// If not specified, defaults to "" +func TryRpcProtocol(value string) TryRpcAttr { + return func(m optionalAttr) { + m["protocol"] = value + } +} + +// TryRpcFailFast sets the optional fail_fast attribute to value. +// +// value: `boolean`. If `true` (default), then failures to connect +// (i.e., the server does not immediately respond) cause an RPC failure. +// If not specified, defaults to true +func TryRpcFailFast(value bool) TryRpcAttr { + return func(m optionalAttr) { + m["fail_fast"] = value + } +} + +// TryRpcTimeoutInMs sets the optional timeout_in_ms attribute to value. +// +// value: `int`. If `0` (default), then the kernel will run the RPC +// request and only time out if the RPC deadline passes or the session times out. +// If this value is greater than `0`, then the op will raise an exception if +// the RPC takes longer than `timeout_in_ms`. +// If not specified, defaults to 0 +func TryRpcTimeoutInMs(value int64) TryRpcAttr { + return func(m optionalAttr) { + m["timeout_in_ms"] = value + } +} + +// Perform batches of RPC requests. +// +// This op asynchronously performs either a single RPC request, or a batch +// of requests. RPC requests are defined by three main parameters: +// +// - `address` (the host+port or BNS address of the request) +// - `method` (the method name for the request) +// - `request` (the serialized proto string, or vector of strings, +// of the RPC request argument). +// +// For example, if you have an RPC service running on port localhost:2345, +// and its interface is configured with the following proto declaration: +// +// ``` +// service MyService { +// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { +// } +// }; +// ``` +// +// then call this op with arguments: +// +// ``` +// address = "localhost:2345" +// method = "MyService/MyMethod" +// ``` +// +// The `request` tensor is a string tensor representing serialized `MyRequestProto` +// strings; and the output string tensor `response` will have the same shape +// and contain (upon successful completion) corresponding serialized +// `MyResponseProto` strings. +// +// For example, to send a single, empty, `MyRequestProto`, call +// this op with `request = ""`. To send 5 **parallel** empty requests, +// call this op with `request = ["", "", "", "", ""]`. +// +// More generally, one can create a batch of `MyRequestProto` serialized protos +// from regular batched tensors using the `encode_proto` op, and convert +// the response `MyResponseProto` serialized protos to batched tensors +// using the `decode_proto` op. +// +// **NOTE** Working with serialized proto strings is faster than instantiating +// actual proto objects in memory, so no performance degradation is expected +// compared to writing custom kernels for this workflow. +// +// Unlike the standard `Rpc` op, if the connection fails or the remote worker +// returns an error status, this op does **not** reraise the exception. +// Instead, the `status_code` and `status_message` entry for the corresponding RPC +// call is set with the error returned from the RPC call. The `response` tensor +// will contain valid response values for those minibatch entries whose RPCs did +// not fail; the rest of the entries will have empty strings. +// +// Arguments: +// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `method` and `request`. +// method: `0-D` or `1-D`. The method address on the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `request`. +// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `method`. +// +// Returns Same shape as `request`. Serialized proto strings: the rpc responses.Same shape as `request`. Values correspond to tensorflow Status enum codes.Same shape as `request`. Values correspond to Status messages +// returned from the RPC calls. +func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...TryRpcAttr) (response tf.Output, status_code tf.Output, status_message tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TryRpc", + Input: []tf.Input{ + address, method, request, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Counts the number of occurrences of each value in an integer array. +// +// Outputs a vector with length `size` and the same dtype as `weights`. If +// `weights` are empty, then index `i` stores the number of times the value `i` is +// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of +// the value in `weights` at each index where the corresponding value in `arr` is +// `i`. +// +// Values in `arr` outside of the range [0, size) are ignored. +// +// Arguments: +// arr: int32 `Tensor`. +// size: non-negative int32 scalar `Tensor`. +// weights: is an int32, int64, float32, or float64 `Tensor` with the same +// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights +// equal to 1. +// +// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for +// each value in the range [0, size). +func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Bincount", + Input: []tf.Input{ + arr, size, weights, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EditDistanceAttr is an optional argument to EditDistance. +type EditDistanceAttr func(optionalAttr) + +// EditDistanceNormalize sets the optional normalize attribute to value. +// +// value: boolean (if true, edit distances are normalized by length of truth). +// +// The output is: +// If not specified, defaults to true +func EditDistanceNormalize(value bool) EditDistanceAttr { + return func(m optionalAttr) { + m["normalize"] = value + } +} + +// Computes the (possibly normalized) Levenshtein Edit Distance. +// +// The inputs are variable-length sequences provided by SparseTensors +// (hypothesis_indices, hypothesis_values, hypothesis_shape) +// and +// (truth_indices, truth_values, truth_shape). +// +// The inputs are: +// +// Arguments: +// hypothesis_indices: The indices of the hypothesis list SparseTensor. +// This is an N x R int64 matrix. +// hypothesis_values: The values of the hypothesis list SparseTensor. +// This is an N-length vector. +// hypothesis_shape: The shape of the hypothesis list SparseTensor. +// This is an R-length vector. +// truth_indices: The indices of the truth list SparseTensor. +// This is an M x R int64 matrix. +// truth_values: The values of the truth list SparseTensor. +// This is an M-length vector. +// truth_shape: truth indices, vector. +// +// Returns A dense float tensor with rank R - 1. +// +// For the example input: +// +// // hypothesis represents a 2x1 matrix with variable-length values: +// // (0,0) = ["a"] +// // (1,0) = ["b"] +// hypothesis_indices = [[0, 0, 0], +// [1, 0, 0]] +// hypothesis_values = ["a", "b"] +// hypothesis_shape = [2, 1, 1] +// +// // truth represents a 2x2 matrix with variable-length values: +// // (0,0) = [] +// // (0,1) = ["a"] +// // (1,0) = ["b", "c"] +// // (1,1) = ["a"] +// truth_indices = [[0, 1, 0], +// [1, 0, 0], +// [1, 0, 1], +// [1, 1, 0]] +// truth_values = ["a", "b", "c", "a"] +// truth_shape = [2, 2, 2] +// normalize = true +// +// The output will be: +// +// // output is a 2x2 matrix with edit distances normalized by truth lengths. +// output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis +// [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis +func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EditDistance", + Input: []tf.Input{ + hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RpcAttr is an optional argument to Rpc. +type RpcAttr func(optionalAttr) + +// RpcProtocol sets the optional protocol attribute to value. +// +// value: RPC protocol to use. Empty string means use the default protocol. +// Options include 'grpc'. +// If not specified, defaults to "" +func RpcProtocol(value string) RpcAttr { + return func(m optionalAttr) { + m["protocol"] = value + } +} + +// RpcFailFast sets the optional fail_fast attribute to value. +// +// value: `boolean`. If `true` (default), then failures to connect +// (i.e., the server does not immediately respond) cause an RPC failure. +// If not specified, defaults to true +func RpcFailFast(value bool) RpcAttr { + return func(m optionalAttr) { + m["fail_fast"] = value + } +} + +// RpcTimeoutInMs sets the optional timeout_in_ms attribute to value. +// +// value: `int`. If `0` (default), then the kernel will run the RPC +// request and only time out if the RPC deadline passes or the session times out. +// If this value is greater than `0`, then the op will raise an exception if +// the RPC takes longer than `timeout_in_ms`. +// If not specified, defaults to 0 +func RpcTimeoutInMs(value int64) RpcAttr { + return func(m optionalAttr) { + m["timeout_in_ms"] = value + } +} + +// Perform batches of RPC requests. +// +// This op asynchronously performs either a single RPC request, or a batch +// of requests. RPC requests are defined by three main parameters: +// +// - `address` (the host+port or BNS address of the request) +// - `method` (the RPC method name for the request) +// - `request` (the serialized proto string, or vector of strings, +// of the RPC request argument). +// +// For example, if you have an RPC service running on port localhost:2345, +// and its interface is configured with the following proto declaration: +// +// ``` +// service MyService { +// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { +// } +// }; +// ``` +// +// then call this op with arguments: +// +// ``` +// address = "localhost:2345" +// method = "MyService/MyMethod" +// ``` +// +// The `request` tensor is a string tensor representing serialized `MyRequestProto` +// strings; and the output string tensor `response` will have the same shape +// and contain (upon successful completion) corresponding serialized +// `MyResponseProto` strings. +// +// For example, to send a single, empty, `MyRequestProto`, call +// this op with `request = ""`. To send 5 **parallel** empty requests, +// call this op with `request = ["", "", "", "", ""]`. +// +// More generally, one can create a batch of `MyRequestProto` serialized protos +// from regular batched tensors using the `encode_proto` op, and convert +// the response `MyResponseProto` serialized protos to batched tensors +// using the `decode_proto` op. +// +// **NOTE** Working with serialized proto strings is faster than instantiating +// actual proto objects in memory, so no performance degradation is expected +// compared to writing custom kernels for this workflow. +// +// If the connection fails or the remote worker returns an error +// status, the op reraises this exception locally. +// +// See the `TryRpc` op if you prefer to handle RPC failures manually in the graph. +// +// Arguments: +// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `method` and `request`. +// method: `0-D` or `1-D`. The method address on the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `request`. +// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `method`. +// +// Returns Same shape as `request`. Serialized proto strings: the rpc responses. +func Rpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...RpcAttr) (response tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Rpc", + Input: []tf.Input{ + address, method, request, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reads the value of a variable. +// +// The tensor returned by this operation is immutable. +// +// The value returned by this operation is guaranteed to be influenced by all the +// writes on which this operation depends directly or indirectly, and to not be +// influenced by any of the writes which depend directly or indirectly on this +// operation. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// dtype: the dtype of the value. +func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "ReadVariableOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to LoadTPUEmbeddingMDLAdagradLightParameters. +type LoadTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingMDLAdagradLightParametersTableId(value int64) LoadTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMDLAdagradLightParametersTableName(value string) LoadTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load MDL Adagrad Light embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the MDL Adagrad Light optimization algorithm. +// accumulators: Value of accumulators used in the MDL Adagrad Light optimization algorithm. +// weights: Value of weights used in the MDL Adagrad Light optimization algorithm. +// benefits: Value of benefits used in the MDL Adagrad Light optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMDLAdagradLightParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMDLAdagradLightParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMDLAdagradLightParameters", + Input: []tf.Input{ + parameters, accumulators, weights, benefits, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Fast Fourier transform. +// +// Computes the 1-dimensional discrete Fourier transform over the inner-most +// dimension of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft +// @end_compatibility +func FFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Performs gradient updates of embedding tables. +// +// Arguments: +// inputs: A TensorList of gradients with which to update embedding tables. +// This argument has the same length and shapes as the return value of +// RecvTPUEmbeddingActivations, but contains gradients of the model's loss +// with respect to the embedding activations. The embedding tables are updated +// from these gradients via the optimizer specified in the TPU embedding +// configuration given to tpu.initialize_system. +// learning_rates: A TensorList of float32 scalars, one for each dynamic learning +// rate tag: see the comments in +// //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto. +// Multiple tables can share the same dynamic learning rate tag as specified +// in the configuration. If the learning rates for all tables are constant, +// this list should be empty. +// config: Serialized TPUEmbeddingConfiguration proto. +// +// Returns the created operation. +func SendTPUEmbeddingGradients(scope *Scope, inputs []tf.Output, learning_rates []tf.Output, config string) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"config": config} + opspec := tf.OpSpec{ + Type: "SendTPUEmbeddingGradients", + Input: []tf.Input{ + tf.OutputList(inputs), tf.OutputList(learning_rates), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ExtractGlimpseAttr is an optional argument to ExtractGlimpse. +type ExtractGlimpseAttr func(optionalAttr) + +// ExtractGlimpseCentered sets the optional centered attribute to value. +// +// value: indicates if the offset coordinates are centered relative to +// the image, in which case the (0, 0) offset is relative to the center +// of the input images. If false, the (0,0) offset corresponds to the +// upper left corner of the input images. +// If not specified, defaults to true +func ExtractGlimpseCentered(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["centered"] = value + } +} + +// ExtractGlimpseNormalized sets the optional normalized attribute to value. +// +// value: indicates if the offset coordinates are normalized. +// If not specified, defaults to true +func ExtractGlimpseNormalized(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["normalized"] = value + } +} + +// ExtractGlimpseUniformNoise sets the optional uniform_noise attribute to value. +// +// value: indicates if the noise should be generated using a +// uniform distribution or a Gaussian distribution. +// If not specified, defaults to true +func ExtractGlimpseUniformNoise(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["uniform_noise"] = value + } +} + +// ExtractGlimpseNoise sets the optional noise attribute to value. +// +// value: indicates if the noise should `uniform`, `gaussian`, or +// `zero`. The default is `uniform` which means the the noise type +// will be decided by `uniform_noise`. +// If not specified, defaults to "uniform" +func ExtractGlimpseNoise(value string) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["noise"] = value + } +} + +// Extracts a glimpse from the input tensor. +// +// Returns a set of windows called glimpses extracted at location +// `offsets` from the input tensor. If the windows only partially +// overlaps the inputs, the non overlapping areas will be filled with +// random noise. +// +// The result is a 4-D tensor of shape `[batch_size, glimpse_height, +// glimpse_width, channels]`. The channels and batch dimensions are the +// same as that of the input tensor. The height and width of the output +// windows are specified in the `size` parameter. +// +// The argument `normalized` and `centered` controls how the windows are built: +// +// * If the coordinates are normalized but not centered, 0.0 and 1.0 +// correspond to the minimum and maximum of each height and width +// dimension. +// * If the coordinates are both normalized and centered, they range from +// -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper +// left corner, the lower right corner is located at (1.0, 1.0) and the +// center is at (0, 0). +// * If the coordinates are not normalized they are interpreted as +// numbers of pixels. +// +// Arguments: +// input: A 4-D float tensor of shape `[batch_size, height, width, channels]`. +// size: A 1-D tensor of 2 elements containing the size of the glimpses +// to extract. The glimpse height must be specified first, following +// by the glimpse width. +// offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing +// the y, x locations of the center of each window. +// +// Returns A tensor representing the glimpses `[batch_size, +// glimpse_height, glimpse_width, channels]`. +func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Output, optional ...ExtractGlimpseAttr) (glimpse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExtractGlimpse", + Input: []tf.Input{ + input, size, offsets, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) + +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { + return func(m optionalAttr) { + m["ignore_lookup_error"] = value + } +} + +// Deletes the resource specified by the handle. +// +// All subsequent operations using the resource will result in a NotFound +// error status. +// +// Arguments: +// resource: handle to the resource to delete. +// +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DestroyResourceOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Outputs the single element from the given dataset. +// +// Arguments: +// dataset: A handle to a dataset that contains a single element. +// +// +// +// Returns The components of the single element of `input`. +func DatasetToSingleElement(scope *Scope, dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "DatasetToSingleElement", + Input: []tf.Input{ + dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("DatasetToSingleElement", err) + return + } + return components +} + +// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2. +type MutableDenseHashTableV2Attr func(optionalAttr) + +// MutableDenseHashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// If not specified, defaults to false +func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value. +// +// value: The shape of each value. +// If not specified, defaults to <> +func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["value_shape"] = value + } +} + +// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value. +// +// value: The initial number of hash table buckets. Must be a power +// to 2. +// If not specified, defaults to 131072 +func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["initial_num_buckets"] = value + } +} + +// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value. +// +// value: The maximum ratio between number of entries and number of +// buckets before growing the table. Must be between 0 and 1. +// If not specified, defaults to 0.8 +func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["max_load_factor"] = value + } +} + +// Creates an empty hash table that uses tensors as the backing store. +// +// It uses "open addressing" with quadratic reprobing to resolve +// collisions. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// empty_key: The key used to represent empty key buckets internally. Must not +// be used in insert or lookup operations. +// +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, deleted_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableDenseHashTableV2", + Input: []tf.Input{ + empty_key, deleted_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An op enabling differentiation of TPU Embeddings. +// +// This op simply returns its first input, which is assumed to have been sliced +// from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of +// this op, and its first argument being a trainable Variable, enables automatic +// differentiation of graphs containing embeddings via the TPU Embedding Python +// libraries. +// +// Arguments: +// embedding_variable: A trainable variable, enabling optimizers to find this op. +// sliced_activations: The embedding activations Tensor to return. +// table_id: The id of the table in the embedding layer configuration from which +// these activations were computed. +// lookup_id: Identifier of the set of embedding indices which produced these +// activations. +func TPUEmbeddingActivations(scope *Scope, embedding_variable tf.Output, sliced_activations tf.Output, table_id int64, lookup_id int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"table_id": table_id, "lookup_id": lookup_id} + opspec := tf.OpSpec{ + Type: "TPUEmbeddingActivations", + Input: []tf.Input{ + embedding_variable, sliced_activations, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An Op to exchange data across TPU replicas. +// +// On each replica, the input is split into `split_count` blocks along +// `split_dimension` and send to the other replicas given group_assignment. After +// receiving `split_count` - 1 blocks from other replicas, we concatenate the +// blocks along `concat_dimension` as the output. +// +// For example, suppose there are 2 TPU replicas: +// replica 0 receives input: `[[A, B]]` +// replica 1 receives input: `[[C, D]]` +// +// group_assignment=`[[0, 1]]` +// concat_dimension=0 +// split_dimension=1 +// split_count=2 +// +// replica 0's output: `[[A], [C]]` +// replica 1's output: `[[B], [D]]` +// +// Arguments: +// input: The local input to the sum. +// group_assignment: An int32 tensor with shape +// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the +// replica ids in the ith subgroup. +// concat_dimension: The dimension number to concatenate. +// split_dimension: The dimension number to split. +// split_count: The number of splits, this number must equal to the sub-group +// size(group_assignment.get_shape()[1]) +// +// Returns The exchanged result. +func AllToAll(scope *Scope, input tf.Output, group_assignment tf.Output, concat_dimension int64, split_dimension int64, split_count int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"concat_dimension": concat_dimension, "split_dimension": split_dimension, "split_count": split_count} + opspec := tf.OpSpec{ + Type: "AllToAll", + Input: []tf.Input{ + input, group_assignment, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the Eigen Decomposition of a batch of square self-adjoint matrices. +// +// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices, with the same constraints as the single matrix +// SelfAdjointEig. +// +// The result is a [..., M+1, M] matrix with [..., 0,:] containing the +// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues +// are sorted in non-decreasing order. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M+1, M]`. +func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SelfAdjointEig", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomPoissonAttr is an optional argument to RandomPoisson. +type RandomPoissonAttr func(optionalAttr) + +// RandomPoissonSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func RandomPoissonSeed(value int64) RandomPoissonAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomPoissonSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func RandomPoissonSeed2(value int64) RandomPoissonAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Use RandomPoissonV2 instead. +// +// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2 +func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomPoisson", + Input: []tf.Input{ + shape, rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation. +type DenseToDenseSetOperationAttr func(optionalAttr) + +// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of 2 `Tensor` inputs. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseToDenseSetOperation", + Input: []tf.Input{ + set1, set2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap. +type AddManySparseToTensorsMapAttr func(optionalAttr) + +// AddManySparseToTensorsMapContainer sets the optional container attribute to value. +// +// value: The container name for the `SparseTensorsMap` created by this op. +// If not specified, defaults to "" +func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` created by this op. +// If blank, the new Operation's unique name is used. +// If not specified, defaults to "" +func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles. +// +// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`, +// `sparse_values`, and `sparse_shape`, where +// +// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R``` +// +// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor` +// having a first `sparse_indices` column taking values between `[0, N)`, where +// the minibatch size `N == sparse_shape[0]`. +// +// The input `SparseTensor` must have rank `R` greater than 1, and the first +// dimension is treated as the minibatch dimension. Elements of the `SparseTensor` +// must be sorted in increasing order of this first dimension. The stored +// `SparseTensor` objects pointed to by each row of the output `sparse_handles` +// will have rank `R-1`. +// +// The `SparseTensor` values can then be read out as part of a minibatch by passing +// the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure +// the correct `SparseTensorsMap` is accessed, ensure that the same +// `container` and `shared_name` are passed to that Op. If no `shared_name` +// is provided here, instead use the *name* of the Operation created by calling +// `AddManySparseToTensorsMap` as the `shared_name` passed to +// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. +// `sparse_indices[:, 0]` must be ordered values in `[0, N)`. +// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. +// The minibatch size `N == sparse_shape[0]`. +// +// Returns 1-D. The handles of the `SparseTensor` now stored in the +// `SparseTensorsMap`. Shape: `[N]`. +func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AddManySparseToTensorsMap", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the element-wise min of two SparseTensors. +// +// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// +// Arguments: +// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, in the canonical lexicographic ordering. +// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. +// a_shape: 1-D. Shape of the input SparseTensor. +// b_indices: counterpart to `a_indices` for the other operand. +// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. +// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// +// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. +func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSparseMinimum", + Input: []tf.Input{ + a_indices, a_values, a_shape, b_indices, b_values, b_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes softsign: `features / (abs(features) + 1)`. +func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softsign", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // ParseSequenceExampleAttr is an optional argument to ParseSequenceExample. type ParseSequenceExampleAttr func(optionalAttr) @@ -11483,87 +14798,216 @@ func ParseSequenceExample(scope *Scope, serialized tf.Output, debug_name tf.Outp return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values, feature_list_dense_lengths } -// LearnedUnigramCandidateSamplerAttr is an optional argument to LearnedUnigramCandidateSampler. -type LearnedUnigramCandidateSamplerAttr func(optionalAttr) - -// LearnedUnigramCandidateSamplerSeed sets the optional seed attribute to value. +// Applies softmax to a batched N-D `SparseTensor`. // -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func LearnedUnigramCandidateSamplerSeed(value int64) LearnedUnigramCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// LearnedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. // -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func LearnedUnigramCandidateSamplerSeed2(value int64) LearnedUnigramCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Generates labels for candidate sampling with a learned unigram distribution. +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: // -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. // -// For each batch, this op picks a single set of sampled candidate labels. -// -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. +// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. // // Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to randomly sample. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. -// range_max: The sampler will sample integers from the interval [0, range_max). +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. // -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func LearnedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LearnedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + opspec := tf.OpSpec{ + Type: "SparseSoftmax", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds up a SparseTensor and a dense Tensor, using these special rules: +// +// (1) Broadcasts the dense side to have the same shape as the sparse side, if +// eligible; +// (2) Then, only the dense values pointed to by the indices of the SparseTensor +// participate in the cwise addition. +// +// By these rules, the result is a logical SparseTensor with exactly the same +// indices and shape, but possibly with different non-zero values. The output of +// this Op is the resultant non-zero values. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseAdd", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// The gradient operator for the SparseSlice op. +// +// This op takes in the upstream gradient w.r.t. non-empty values of +// the sliced `SparseTensor`, and outputs the gradients w.r.t. +// the non-empty values of input `SparseTensor`. +// +// Arguments: +// backprop_val_grad: 1-D. The gradient with respect to +// the non-empty values of the sliced `SparseTensor`. +// input_indices: 2-D. The `indices` of the input `SparseTensor`. +// input_start: 1-D. tensor represents the start of the slice. +// output_indices: 2-D. The `indices` of the sliced `SparseTensor`. +// +// Returns 1-D. The gradient with respect to the non-empty values of input `SparseTensor`. +func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf.Output, input_start tf.Output, output_indices tf.Output) (val_grad tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSliceGrad", + Input: []tf.Input{ + backprop_val_grad, input_indices, input_start, output_indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the power of one value to another. +// +// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for +// corresponding elements in `x` and `y`. For example: +// +// ``` +// # tensor 'x' is [[2, 2]], [3, 3]] +// # tensor 'y' is [[8, 16], [2, 3]] +// tf.pow(x, y) ==> [[256, 65536], [9, 27]] +// ``` +func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Pow", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ArgMaxAttr is an optional argument to ArgMax. +type ArgMaxAttr func(optionalAttr) + +// ArgMaxOutputType sets the optional output_type attribute to value. +// If not specified, defaults to DT_INT64 +func ArgMaxOutputType(value tf.DataType) ArgMaxAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Returns the index with the largest value across dimensions of a tensor. +// +// Note that in case of ties the identity of the return value is not guaranteed. +// +// Usage: +// ```python +// import tensorflow as tf +// a = [1, 10, 26.9, 2.8, 166.32, 62.3] +// b = tf.math.argmax(input = a) +// c = tf.keras.backend.eval(b) +// # c = 4 +// # here a[4] = 166.32 which is the largest element of a across axis 0 +// ``` +// +// Arguments: +// +// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. +// Describes which dimension of the input Tensor to reduce across. For vectors, +// use dimension = 0. +func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "LearnedUnigramCandidateSampler", + Type: "ArgMax", Input: []tf.Input{ - true_classes, + input, dimension, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return op.Output(0) } -// SparseReduceMaxAttr is an optional argument to SparseReduceMax. -type SparseReduceMaxAttr func(optionalAttr) +// A dataset that creates window datasets from the input dataset. +// +// Arguments: +// +// size: A scalar representing the number of elements to accumulate in a window. +// shift: A scalar representing the steps moving the sliding window forward in one +// iteration. It must be positive. +// stride: A scalar representing the stride of the input elements of the sliding window. +// It must be positive. +// drop_remainder: A scalar representing whether a window should be dropped in case its size is +// smaller than desired. +// +// +func WindowDataset(scope *Scope, input_dataset tf.Output, size tf.Output, shift tf.Output, stride tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "WindowDataset", + Input: []tf.Input{ + input_dataset, size, shift, stride, drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// SparseReduceMaxKeepDims sets the optional keep_dims attribute to value. +// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse. +type SparseReduceMaxSparseAttr func(optionalAttr) + +// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value. // // value: If true, retain reduced dimensions with length 1. // If not specified, defaults to false -func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr { +func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr { return func(m optionalAttr) { m["keep_dims"] = value } @@ -11572,8 +15016,8 @@ func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr { // Computes the max of elements across dimensions of a SparseTensor. // // This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_max()`. In particular, this Op also returns a dense `Tensor` -// instead of a sparse one. +// `tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a +// SparseTensor. // // Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless // `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in @@ -11590,9 +15034,7 @@ func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr { // input_values: 1-D. `N` non-empty values corresponding to `input_indices`. // input_shape: 1-D. Shape of the input SparseTensor. // reduction_axes: 1-D. Length-`K` vector containing the reduction axes. -// -// Returns `R-K`-D. The reduced Tensor. -func SparseReduceMax(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxAttr) (output tf.Output) { +func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { if scope.Err() != nil { return } @@ -11601,14 +15043,14 @@ func SparseReduceMax(scope *Scope, input_indices tf.Output, input_values tf.Outp a(attrs) } opspec := tf.OpSpec{ - Type: "SparseReduceMax", + Type: "SparseReduceMaxSparse", Input: []tf.Input{ input_indices, input_values, input_shape, reduction_axes, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } // Conv3DAttr is an optional argument to Conv3D. @@ -11676,6 +15118,4167 @@ func Conv3D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, pa return op.Output(0) } +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. +// +// This Op does not require `a_indices` be sorted in standard lexicographic order. +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseAdd", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Slice a `SparseTensor` based on the `start` and `size`. +// +// For example, if the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSlice", + Input: []tf.Input{ + indices, values, shape, start, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ComplexAbsAttr is an optional argument to ComplexAbs. +type ComplexAbsAttr func(optionalAttr) + +// ComplexAbsTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ComplexAbsTout(value tf.DataType) ComplexAbsAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Computes the complex absolute value of a tensor. +// +// Given a tensor `x` of complex numbers, this operation returns a tensor of type +// `float` or `double` that is the absolute value of each element in `x`. All +// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute +// value is computed as \\( \sqrt{a^2 + b^2}\\). +func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ComplexAbs", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the determinant of one or more square matrices. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor containing the determinants +// for all input submatrices `[..., :, :]`. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[...]`. +func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDeterminant", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// +// thread_pool: A resource produced by the ThreadPoolHandle op. +// +// +func ExperimentalThreadPoolDataset(scope *Scope, input_dataset tf.Output, thread_pool tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalThreadPoolDataset", + Input: []tf.Input{ + input_dataset, thread_pool, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates a list of `SparseTensor` along the specified dimension. +// +// Concatenation is with respect to the dense versions of these sparse tensors. +// It is assumed that each input is a `SparseTensor` whose elements are ordered +// along increasing dimension number. +// +// All inputs' shapes must match, except for the concat dimension. The +// `indices`, `values`, and `shapes` lists must have the same length. +// +// The output shape is identical to the inputs', except along the concat +// dimension, where it is the sum of the inputs' sizes along that dimension. +// +// The output elements will be resorted to preserve the sort order along +// increasing dimension number. +// +// This op runs in `O(M log M)` time, where `M` is the total number of non-empty +// values across all inputs. This is due to the need for an internal sort in +// order to concatenate efficiently across an arbitrary dimension. +// +// For example, if `concat_dim = 1` and the inputs are +// +// sp_inputs[0]: shape = [2, 3] +// [0, 2]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// sp_inputs[1]: shape = [2, 4] +// [0, 1]: "d" +// [0, 2]: "e" +// +// then the output will be +// +// shape = [2, 7] +// [0, 2]: "a" +// [0, 4]: "d" +// [0, 5]: "e" +// [1, 0]: "b" +// [1, 1]: "c" +// +// Graphically this is equivalent to doing +// +// [ a] concat [ d e ] = [ a d e ] +// [b c ] [ ] [b c ] +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. Non-empty values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), +// where rank is the number of dimensions in each input `SparseTensor`. +// +// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. +func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"concat_dim": concat_dim} + opspec := tf.OpSpec{ + Type: "SparseConcat", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// CropAndResizeGradBoxesAttr is an optional argument to CropAndResizeGradBoxes. +type CropAndResizeGradBoxesAttr func(optionalAttr) + +// CropAndResizeGradBoxesMethod sets the optional method attribute to value. +// +// value: A string specifying the interpolation method. Only 'bilinear' is +// supported for now. +// If not specified, defaults to "bilinear" +func CropAndResizeGradBoxesMethod(value string) CropAndResizeGradBoxesAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// Computes the gradient of the crop_and_resize op wrt the input boxes tensor. +// +// Arguments: +// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +// Both `image_height` and `image_width` need to be positive. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// +// Returns A 2-D tensor of shape `[num_boxes, 4]`. +func CropAndResizeGradBoxes(scope *Scope, grads tf.Output, image tf.Output, boxes tf.Output, box_ind tf.Output, optional ...CropAndResizeGradBoxesAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResizeGradBoxes", + Input: []tf.Input{ + grads, image, boxes, box_ind, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deserialize `SparseTensor` objects. +// +// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where +// the last dimension stores serialized `SparseTensor` objects and the other N +// dimensions (N >= 0) correspond to a batch. The ranks of the original +// `SparseTensor` objects must all match. When the final `SparseTensor` is +// created, its rank is the rank of the incoming `SparseTensor` objects plus N; +// the sparse tensors have been concatenated along new dimensions, one for each +// batch. +// +// The output `SparseTensor` object's shape values for the original dimensions +// are the max across the input `SparseTensor` objects' shape values for the +// corresponding dimensions. The new dimensions match the size of the batch. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: The serialized `SparseTensor` objects. The last dimension +// must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "DeserializeSparse", + Input: []tf.Input{ + serialized_sparse, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes the gradient for the sqrt of `x` wrt its input. +// +// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy` +// is the corresponding input gradient. +func SqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SqrtGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces a summary of any statistics recorded by the given statistics manager. +func ExperimentalStatsAggregatorSummary(scope *Scope, iterator tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalStatsAggregatorSummary", + Input: []tf.Input{ + iterator, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds v into specified rows of x. +// +// Computes y = x; y[i, :] += v; return y. +// +// Arguments: +// x: A `Tensor` of type T. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceAdd(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceAdd", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PaddedBatchDatasetV2Attr is an optional argument to PaddedBatchDatasetV2. +type PaddedBatchDatasetV2Attr func(optionalAttr) + +// PaddedBatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. +// If not specified, defaults to false +func PaddedBatchDatasetV2ParallelCopy(value bool) PaddedBatchDatasetV2Attr { + return func(m optionalAttr) { + m["parallel_copy"] = value + } +} + +// Creates a dataset that batches and pads `batch_size` elements from the input. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// padded_shapes: A list of int64 tensors representing the desired padded shapes +// of the corresponding output components. These shapes may be partially +// specified, using `-1` to indicate that a particular dimension should be +// padded to the maximum size of all batch elements. +// padding_values: A list of scalars containing the padding value to use for +// each of the outputs. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape, optional ...PaddedBatchDatasetV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PaddedBatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul. +type SparseTensorDenseMatMulAttr func(optionalAttr) + +// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value. +// +// value: Use the adjoint of A in the matrix multiply. If A is complex, this +// is transpose(conj(A)). Otherwise it's transpose(A). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_a"] = value + } +} + +// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value. +// +// value: Use the adjoint of B in the matrix multiply. If B is complex, this +// is transpose(conj(B)). Otherwise it's transpose(B). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_b"] = value + } +} + +// Multiply SparseTensor (of rank 2) "A" by dense matrix "B". +// +// No validity checking is performed on the indices of A. However, the following +// input format is recommended for optimal behavior: +// +// if adjoint_a == false: +// A should be sorted in lexicographically increasing order. Use SparseReorder +// if you're not sure. +// if adjoint_a == true: +// A should be sorted in order of increasing dimension 1 (i.e., "column major" +// order instead of "row major" order). +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. +// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. +// b: 2-D. A dense Matrix. +func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseMatMul", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise largest integer not greater than x. +func Floor(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Floor", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FusedBatchNormV3Attr is an optional argument to FusedBatchNormV3. +type FusedBatchNormV3Attr func(optionalAttr) + +// FusedBatchNormV3Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormV3Epsilon(value float32) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormV3DataFormat sets the optional data_format attribute to value. +// +// value: The data format for x and y. Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormV3DataFormat(value string) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormV3IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormV3IsTraining(value bool) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// offset: A 1D Tensor for offset, to shift to the normalized x. +// mean: A 1D Tensor for population mean. Used for inference only; +// must be empty for training. +// variance: A 1D Tensor for population variance. Used for inference only; +// must be empty for training. +// +// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow +// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by +// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused +// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance +// in the cuDNN case), to be reused in the gradient computation.A 1D Tensor for some intermediate results, to be reused in the gradient +// computation for better efficiency. +func FusedBatchNormV3(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV3Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormV3", + Input: []tf.Input{ + x, scale, offset, mean, variance, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5) +} + +// Fetches multiple values from infeed as an XLA tuple. +// +// Arguments: +// dtypes: The element types of each element in `outputs`. +// shapes: The shapes of each tensor in `outputs`. +// +// Returns A list of tensors that will be provided using the infeed mechanism. +func InfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} + opspec := tf.OpSpec{ + Type: "InfeedDequeueTuple", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("InfeedDequeueTuple", err) + return + } + return outputs +} + +// Computes offsets of concat inputs within its output. +// +// For example: +// +// ``` +// # 'x' is [2, 2, 7] +// # 'y' is [2, 3, 7] +// # 'z' is [2, 5, 7] +// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] +// ``` +// +// This is typically used by gradient computations for a concat operation. +// +// Arguments: +// concat_dim: The dimension along which to concatenate. +// shape: The `N` int32 vectors representing shape of tensors being concatenated. +// +// Returns The `N` int32 vectors representing the starting offset +// of input tensors within the concatenated output. +func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatOffset", + Input: []tf.Input{ + concat_dim, tf.OutputList(shape), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil { + scope.UpdateErr("ConcatOffset", err) + return + } + return offset +} + +// CumprodAttr is an optional argument to Cumprod. +type CumprodAttr func(optionalAttr) + +// CumprodExclusive sets the optional exclusive attribute to value. +// +// value: If `True`, perform exclusive cumprod. +// If not specified, defaults to false +func CumprodExclusive(value bool) CumprodAttr { + return func(m optionalAttr) { + m["exclusive"] = value + } +} + +// CumprodReverse sets the optional reverse attribute to value. +// +// value: A `bool` (default: False). +// If not specified, defaults to false +func CumprodReverse(value bool) CumprodAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Compute the cumulative product of the tensor `x` along `axis`. +// +// By default, this op performs an inclusive cumprod, which means that the first +// element of the input is identical to the first element of the output: +// +// ```python +// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] +// ``` +// +// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is +// performed instead: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] +// ``` +// +// By setting the `reverse` kwarg to `True`, the cumprod is performed in the +// opposite direction: +// +// ```python +// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] +// ``` +// +// This is more efficient than using separate `tf.reverse` ops. +// +// The `reverse` and `exclusive` kwargs can also be combined: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] +// ``` +// +// Arguments: +// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, +// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, +// `complex128`, `qint8`, `quint8`, `qint32`, `half`. +// axis: A `Tensor` of type `int32` (default: 0). Must be in the range +// `[-rank(x), rank(x))`. +func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Cumprod", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Read an element from the TensorArray into output `value`. +// +// Arguments: +// handle: The handle to a TensorArray. +// +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. +// +// Returns The tensor that is read from the TensorArray. +func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "TensorArrayReadV3", + Input: []tf.Input{ + handle, index, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayV3Attr is an optional argument to TensorArrayV3. +type TensorArrayV3Attr func(optionalAttr) + +// TensorArrayV3ElementShape sets the optional element_shape attribute to value. +// +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value. +// +// value: A boolean that determines whether writes to the TensorArray +// are allowed to grow the size. By default, this is not allowed. +// If not specified, defaults to false +func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["dynamic_size"] = value + } +} + +// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value. +// +// value: If true (default), Tensors in the TensorArray are cleared +// after being read. This disables multiple read semantics but allows early +// release of memory. +// If not specified, defaults to true +func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["clear_after_read"] = value + } +} + +// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// +// value: If true (default is false), then all +// elements in the TensorArray will be expected to have have identical shapes. +// This allows certain behaviors, like dynamically checking for +// consistent shapes on write, and being able to fill in properly +// shaped zero tensors on stack -- even if the element_shape attribute +// is not fully defined. +// If not specified, defaults to false +func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["identical_element_shapes"] = value + } +} + +// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// +// value: Overrides the name used for the temporary tensor_array +// resource. Default value is the name of the 'TensorArray' op (which +// is guaranteed unique). +// If not specified, defaults to "" +func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { + return func(m optionalAttr) { + m["tensor_array_name"] = value + } +} + +// An array of Tensors of given size. +// +// Write data via Write and read via Read or Pack. +// +// Arguments: +// size: The size of the array. +// dtype: The type of the elements on the tensor_array. +// +// Returns The handle to the TensorArray.A scalar used to control gradient flow. +func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayV3", + Input: []tf.Input{ + size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Produces the average pool of the input tensor for quantized types. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// ksize: The size of the window for each dimension of the input tensor. +// The length must be 4 to match the number of dimensions of the input. +// strides: The stride of the sliding window for each dimension of the input +// tensor. The length must be 4 to match the number of dimensions of the input. +// padding: The type of padding algorithm to use. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "QuantizedAvgPool", + Input: []tf.Input{ + input, min_input, max_input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// The gradient operator for the SparseAdd op. +// +// The SparseAdd op calculates A + B, where A, B, and the sum are all represented +// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. +// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty +// values of A and B. +// +// Arguments: +// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to +// the non-empty values of the sum. +// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. +// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. +// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size +// `[nnz(sum), ndims]`. +// +// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the +// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the +// non-empty values of B. +func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseAddGrad", + Input: []tf.Input{ + backprop_val_grad, a_indices, b_indices, sum_indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Returns true if and only if the given Optional variant has a value. +func OptionalHasValue(scope *Scope, optional tf.Output) (has_value tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalHasValue", + Input: []tf.Input{ + optional, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is +// +// true, this follows Python semantics in that the result here is consistent +// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. +// +// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FloorMod", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for the scaled exponential linear (Selu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Selu operation. +// outputs: The outputs of the corresponding Selu operation. +// +// Returns The gradients: `gradients * (outputs + scale * alpha)` +// if outputs < 0, `scale * gradients` otherwise. +func SeluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SeluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv3DBackpropInputAttr is an optional argument to Conv3DBackpropInput. +type Conv3DBackpropInputAttr func(optionalAttr) + +// Conv3DBackpropInputDilations sets the optional dilations attribute to value. +// If not specified, defaults to +func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the input. +// +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the mean along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. +// +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_mean(c, tf.constant([0, 0, 1])) +// # ==> [[2.5, 2.5, 2.5, 2.5], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMean", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AvgPoolGradAttr is an optional argument to AvgPoolGrad. +type AvgPoolGradAttr func(optionalAttr) + +// AvgPoolGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the average pooling function. +// +// Arguments: +// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. +// the output of `avg_pool`. +// ksize: The size of the sliding window for each dimension of the input. +// strides: The stride of the sliding window for each dimension of the input. +// padding: The type of padding algorithm to use. +// +// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. +func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPoolGrad", + Input: []tf.Input{ + orig_input_shape, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeCSVAttr is an optional argument to DecodeCSV. +type DecodeCSVAttr func(optionalAttr) + +// DecodeCSVFieldDelim sets the optional field_delim attribute to value. +// +// value: char delimiter to separate fields in a record. +// If not specified, defaults to "," +func DecodeCSVFieldDelim(value string) DecodeCSVAttr { + return func(m optionalAttr) { + m["field_delim"] = value + } +} + +// DecodeCSVUseQuoteDelim sets the optional use_quote_delim attribute to value. +// +// value: If false, treats double quotation marks as regular +// characters inside of the string fields (ignoring RFC 4180, Section 2, +// Bullet 5). +// If not specified, defaults to true +func DecodeCSVUseQuoteDelim(value bool) DecodeCSVAttr { + return func(m optionalAttr) { + m["use_quote_delim"] = value + } +} + +// DecodeCSVNaValue sets the optional na_value attribute to value. +// +// value: Additional string to recognize as NA/NaN. +// If not specified, defaults to "" +func DecodeCSVNaValue(value string) DecodeCSVAttr { + return func(m optionalAttr) { + m["na_value"] = value + } +} + +// DecodeCSVSelectCols sets the optional select_cols attribute to value. +// If not specified, defaults to <> +func DecodeCSVSelectCols(value []int64) DecodeCSVAttr { + return func(m optionalAttr) { + m["select_cols"] = value + } +} + +// Convert CSV records to tensors. Each column maps to one tensor. +// +// RFC 4180 format is expected for the CSV records. +// (https://tools.ietf.org/html/rfc4180) +// Note that we allow leading and trailing spaces with int or float field. +// +// Arguments: +// records: Each string is a record/row in the csv and all records should have +// the same format. +// record_defaults: One tensor per column of the input record, with either a +// scalar default value for that column or an empty vector if the column is +// required. +// +// Returns Each tensor will have the same shape as records. +func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeCSV", + Input: []tf.Input{ + records, tf.OutputList(record_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("DecodeCSV", err) + return + } + return output +} + +// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam. +type ResourceApplyAdamAttr func(optionalAttr) + +// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, uses the nesterov update. +// If not specified, defaults to false +func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the Adam algorithm. +// +// $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ +// $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ +// $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ +// $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// beta1_power: Must be a scalar. +// beta2_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdam", + Input: []tf.Input{ + var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ComplexAttr is an optional argument to Complex. +type ComplexAttr func(optionalAttr) + +// ComplexTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func ComplexTout(value tf.DataType) ComplexAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Converts two real numbers to a complex number. +// +// Given a tensor `real` representing the real part of a complex number, and a +// tensor `imag` representing the imaginary part of a complex number, this +// operation returns complex numbers elementwise of the form \\(a + bj\\), where +// *a* represents the `real` part and *b* represents the `imag` part. +// +// The input tensors `real` and `imag` must have the same shape. +// +// For example: +// +// ``` +// # tensor 'real' is [2.25, 3.25] +// # tensor `imag` is [4.75, 5.75] +// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] +// ``` +func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Complex", + Input: []tf.Input{ + real, imag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseToDenseAttr is an optional argument to SparseToDense. +type SparseToDenseAttr func(optionalAttr) + +// SparseToDenseValidateIndices sets the optional validate_indices attribute to value. +// +// value: If true, indices are checked to make sure they are sorted in +// lexicographic order and that there are no repeats. +// If not specified, defaults to true +func SparseToDenseValidateIndices(value bool) SparseToDenseAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Converts a sparse representation into a dense tensor. +// +// Builds an array `dense` with shape `output_shape` such that +// +// ``` +// # If sparse_indices is scalar +// dense[i] = (i == sparse_indices ? sparse_values : default_value) +// +// # If sparse_indices is a vector, then for each i +// dense[sparse_indices[i]] = sparse_values[i] +// +// # If sparse_indices is an n by d matrix, then for each i in [0, n) +// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] +// ``` +// +// All other values in `dense` are set to `default_value`. If `sparse_values` is a +// scalar, all sparse indices are set to this single value. +// +// Indices should be sorted in lexicographic order, and indices must not +// contain any repeats. If `validate_indices` is true, these properties +// are checked during execution. +// +// Arguments: +// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete +// index where `sparse_values[i]` will be placed. +// output_shape: 1-D. Shape of the dense output tensor. +// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, +// or a scalar value to be used for all sparse indices. +// default_value: Scalar value to set for indices not specified in +// `sparse_indices`. +// +// Returns Dense output tensor of shape `output_shape`. +func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToDense", + Input: []tf.Input{ + sparse_indices, output_shape, sparse_values, default_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdagradParametersGradAccumDebug. +type RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve Adagrad embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the Adagrad optimization algorithm.Parameter accumulators updated by the Adagrad optimization algorithm.Parameter gradient_accumulators updated by the Adagrad optimization algorithm. +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// 3D fast Fourier transform. +// +// Computes the 3-dimensional discrete Fourier transform over the inner-most 3 +// dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 3 +// dimensions of `input` are replaced with their 3D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fftn with 3 dimensions. +// @end_compatibility +func FFT3D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT3D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise XOR of `x` and `y`. +// +// The result will have those bits set, that are different in `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseXor", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapClearAttr is an optional argument to MapClear. +type MapClearAttr func(optionalAttr) + +// MapClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapClearCapacity(value int64) MapClearAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapClearMemoryLimit(value int64) MapClearAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapClearContainer(value string) MapClearAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapClearSharedName(value string) MapClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes all elements in the underlying container. +// +// Returns the created operation. +func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapClear", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionV2(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV2", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter. +type Conv2DBackpropFilterAttr func(optionalAttr) + +// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DBackpropFilterExplicitPaddings sets the optional explicit_paddings attribute to value. +// +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DBackpropFilterExplicitPaddings(value []int64) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv2DBackpropFilterDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of convolution with respect to the filter. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 4-D +// `[filter_height, filter_width, in_channels, out_channels]` tensor. +// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. Must be in the same order as the dimension specified with +// format. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. +// the `filter` input of the convolution. +func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv2DBackpropFilter", + Input: []tf.Input{ + input, filter_sizes, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DataFormatVecPermuteAttr is an optional argument to DataFormatVecPermute. +type DataFormatVecPermuteAttr func(optionalAttr) + +// DataFormatVecPermuteSrcFormat sets the optional src_format attribute to value. +// +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatVecPermuteSrcFormat(value string) DataFormatVecPermuteAttr { + return func(m optionalAttr) { + m["src_format"] = value + } +} + +// DataFormatVecPermuteDstFormat sets the optional dst_format attribute to value. +// +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatVecPermuteDstFormat(value string) DataFormatVecPermuteAttr { + return func(m optionalAttr) { + m["dst_format"] = value + } +} + +// Returns the permuted vector/tensor in the destination data format given the +// +// one in the source data format. +// +// Arguments: +// x: Vector of size 4 or Tensor of shape (4, 2) in source data format. +// +// Returns Vector of size 4 or Tensor of shape (4, 2) in destination data format. +func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPermuteAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DataFormatVecPermute", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// IdentityReaderV2Attr is an optional argument to IdentityReaderV2. +type IdentityReaderV2Attr func(optionalAttr) + +// IdentityReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func IdentityReaderV2Container(value string) IdentityReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// IdentityReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A Reader that outputs the queued work as both the key and value. +// +// To use, enqueue strings in a Queue. ReaderRead will take the front +// work string and output (work, work). +// +// Returns The handle to reference the Reader. +func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IdentityReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RegexReplaceAttr is an optional argument to RegexReplace. +type RegexReplaceAttr func(optionalAttr) + +// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// +// value: If True, the replacement is global (that is, all matches of the `pattern` regular +// expression in each input string are rewritten), otherwise the `rewrite` +// substitution is only made for the first `pattern` match. +// If not specified, defaults to true +func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { + return func(m optionalAttr) { + m["replace_global"] = value + } +} + +// Replaces matches of the `pattern` regular expression in `input` with the +// replacement string provided in `rewrite`. +// +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: The text to be processed. +// pattern: The regular expression to be matched in the `input` strings. +// rewrite: The rewrite string to be substituted for the `pattern` expression where it is +// matched in the `input` strings. +// +// Returns The text after applying pattern match and rewrite substitution. +func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RegexReplace", + Input: []tf.Input{ + input, pattern, rewrite, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) + +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["a_is_sparse"] = value + } +} + +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["b_is_sparse"] = value + } +} + +// Multiply matrix "a" by matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not +// `SparseTensor`s. This op is optimized for the case where at least one of "a" or +// "b" is sparse, in the sense that they have a large proportion of zero values. +// The breakeven for using this versus a dense matrix multiply on one platform was +// 30% zero values in the sparse matrix. +// +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An Op to permute tensors across replicated TPU instances. +// +// Each instance supplies its own input. +// +// For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing +// source_target_pairs=`[[0,1],[1,2],[2,3],[3,0]]` gets the outputs: +// `[D, A, B, C]`. +// +// Arguments: +// input: The local input to be permuted. Currently only supports float and +// bfloat16. +// source_target_pairs: A tensor with shape [num_pairs, 2]. +// +// Returns The permuted input. +func CollectivePermute(scope *Scope, input tf.Output, source_target_pairs tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CollectivePermute", + Input: []tf.Input{ + input, source_target_pairs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the polygamma function \\(\psi^{(n)}(x)\\). +// +// The polygamma function is defined as: +// +// +// \\(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\\) +// +// where \\(\psi(x)\\) is the digamma function. +// The polygamma function is defined only for non-negative integer orders \\a\\. +func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Polygamma", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// InfeedEnqueueTupleAttr is an optional argument to InfeedEnqueueTuple. +type InfeedEnqueueTupleAttr func(optionalAttr) + +// InfeedEnqueueTupleLayouts sets the optional layouts attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence for +// all the tuple shapes, in the order the shapes appear in the "shapes" input. +// The layout elements for a sub-shape can be set to -1, in which case the +// corresponding layout will be computed by the infeed operation. +// If not specified, defaults to <> +func InfeedEnqueueTupleLayouts(value []int64) InfeedEnqueueTupleAttr { + return func(m optionalAttr) { + m["layouts"] = value + } +} + +// InfeedEnqueueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func InfeedEnqueueTupleDeviceOrdinal(value int64) InfeedEnqueueTupleAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Feeds multiple Tensor values into the computation as an XLA tuple. +// +// Arguments: +// inputs: A list of tensors that will be provided using the infeed mechanism. +// shapes: The shapes of each tensor in `inputs`. +// +// Returns the created operation. +func InfeedEnqueueTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...InfeedEnqueueTupleAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueueTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// NonDeterministicIntsAttr is an optional argument to NonDeterministicInts. +type NonDeterministicIntsAttr func(optionalAttr) + +// NonDeterministicIntsDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_INT64 +func NonDeterministicIntsDtype(value tf.DataType) NonDeterministicIntsAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Non-deterministically generates some integers. +// +// This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. +// +// Arguments: +// shape: The shape of the output tensor. +// +// Returns Non-deterministic integer values with specified shape. +func NonDeterministicInts(scope *Scope, shape tf.Output, optional ...NonDeterministicIntsAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonDeterministicInts", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingCenteredRMSPropParameters. +type LoadTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingCenteredRMSPropParametersTableId(value int64) LoadTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingCenteredRMSPropParametersTableName(value string) LoadTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load centered RMSProp embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the centered RMSProp optimization algorithm. +// ms: Value of ms used in the centered RMSProp optimization algorithm. +// mom: Value of mom used in the centered RMSProp optimization algorithm. +// mg: Value of mg used in the centered RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingCenteredRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingCenteredRMSPropParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingCenteredRMSPropParameters", + Input: []tf.Input{ + parameters, ms, mom, mg, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA. +type ResourceApplyAdagradDAAttr func(optionalAttr) + +// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. +type DataFormatDimMapAttr func(optionalAttr) + +// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. +// +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["src_format"] = value + } +} + +// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. +// +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["dst_format"] = value + } +} + +// Returns the dimension index in the destination data format given the one in +// +// the source data format. +// +// Arguments: +// x: A Tensor with each element as a dimension index in source data format. +// Must be in the range [-4, 4). +// +// Returns A Tensor with each element as a dimension index in destination data format. +func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DataFormatDimMap", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AllAttr is an optional argument to All. +type AllAttr func(optionalAttr) + +// AllKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AllKeepDims(value bool) AllAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical and" of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "All", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Encodes a `RaggedTensor` into a `variant` Tensor. +// +// +// Encodes the given `RaggedTensor` and returns a `variant` Tensor. If +// `batched_input` is True, then input `RaggedTensor` is unbatched along the +// zero-th dimension, each component `RaggedTensor` is encoded into a scalar +// `variant` Tensor, and these are stacked to return a 1-D `variant` Tensor. +// If `batched_input` is False, then the input `RaggedTensor` is encoded as is and +// a scalar `variant` Tensor is returned. A `RaggedTensor` is encoded by first +// creating a 1-D `variant` Tensor with `ragged_rank + 1` elements, containing the +// splits and values Tensors of the `RaggedTensor`. Then the 1-D `variant` Tensor +// is wrapped in a scalar `variant` Tensor. See `RaggedTensorFromVariant` for the +// corresponding decoding logic. +// +// +// Arguments: +// rt_nested_splits: A list of one or more Tensors representing the splits of the input +// `RaggedTensor`. +// rt_dense_values: A Tensor representing the values of the input `RaggedTensor`. +// batched_input: A `bool` denoting whether the input is a batched `RaggedTensor`. +// +// Returns A `variant` Tensor that containing encoded `RaggedTensor`. +func RaggedTensorToVariant(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output, batched_input bool) (encoded_ragged tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"batched_input": batched_input} + opspec := tf.OpSpec{ + Type: "RaggedTensorToVariant", + Input: []tf.Input{ + tf.OutputList(rt_nested_splits), rt_dense_values, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingADAMParametersAttr is an optional argument to RetrieveTPUEmbeddingADAMParameters. +type RetrieveTPUEmbeddingADAMParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingADAMParametersTableId(value int64) RetrieveTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersTableName(value string) RetrieveTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve ADAM embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the ADAM optimization algorithm.Parameter momenta updated by the ADAM optimization algorithm.Parameter velocities updated by the ADAM optimization algorithm. +func RetrieveTPUEmbeddingADAMParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingADAMParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Table initializer that takes two tensors for keys and values respectively. +// +// Arguments: +// table_handle: Handle to a table which will be initialized. +// keys: Keys of type Tkey. +// values: Values of type Tval. +// +// Returns the created operation. +func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InitializeTableV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// Reads and outputs the entire contents of the input filename. +func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReadFile", + Input: []tf.Input{ + filename, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingADAMParametersAttr is an optional argument to LoadTPUEmbeddingADAMParameters. +type LoadTPUEmbeddingADAMParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingADAMParametersTableId(value int64) LoadTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersTableName(value string) LoadTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load ADAM embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the ADAM optimization algorithm. +// momenta: Value of momenta used in the ADAM optimization algorithm. +// velocities: Value of velocities used in the ADAM optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingADAMParameters(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingADAMParameters", + Input: []tf.Input{ + parameters, momenta, velocities, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes rectified linear: `max(features, 0)`. +func Relu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParameterizedTruncatedNormalAttr is an optional argument to ParameterizedTruncatedNormal. +type ParameterizedTruncatedNormalAttr func(optionalAttr) + +// ParameterizedTruncatedNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ParameterizedTruncatedNormalSeed(value int64) ParameterizedTruncatedNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// ParameterizedTruncatedNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func ParameterizedTruncatedNormalSeed2(value int64) ParameterizedTruncatedNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a normal distribution. The parameters may each be a +// +// scalar which applies to the entire output, or a vector of length shape[0] which +// stores the parameters for each batch. +// +// Arguments: +// shape: The shape of the output tensor. Batches are indexed by the 0th dimension. +// means: The mean parameter of each batch. +// stdevs: The standard deviation parameter of each batch. Must be greater than 0. +// minvals: The minimum cutoff. May be -infinity. +// maxvals: The maximum cutoff. May be +infinity, and must be more than the minval +// for each batch. +// +// Returns A matrix of shape num_batches x samples_per_batch, filled with random +// truncated normal values using the parameters for each row. +func ParameterizedTruncatedNormal(scope *Scope, shape tf.Output, means tf.Output, stdevs tf.Output, minvals tf.Output, maxvals tf.Output, optional ...ParameterizedTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParameterizedTruncatedNormal", + Input: []tf.Input{ + shape, means, stdevs, minvals, maxvals, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter. +type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr) + +// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeBackpropFilterDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of depthwise convolution with respect to the filter. +// +// Arguments: +// input: 4-D with shape based on `data_format`. For example, if +// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height, +// in_width, in_channels]` tensor. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 4-D +// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor. +// out_backprop: 4-D with shape based on `data_format`. +// For example, if `data_format` is 'NHWC' then +// out_backprop shape is `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. +// the `filter` input of the convolution. +func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNativeBackpropFilter", + Input: []tf.Input{ + input, filter_sizes, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the rank of a tensor. +// +// This operation returns an integer representing the rank of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// # shape of tensor 't' is [2, 2, 3] +// rank(t) ==> 3 +// ``` +// +// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank +// of a tensor is the number of indices required to uniquely select each element +// of the tensor. Rank is also known as "order", "degree", or "ndims." +func Rank(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Rank", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that passes a sliding window over `input_dataset`. +// +// Arguments: +// +// window_size: A scalar representing the number of elements in the +// sliding window. +// window_shift: A scalar representing the steps moving the sliding window +// forward in one iteration. It must be positive. +// window_stride: A scalar representing the stride of the input elements of the sliding window. +// It must be positive. +// +// +func ExperimentalSlidingWindowDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, window_shift tf.Output, window_stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalSlidingWindowDataset", + Input: []tf.Input{ + input_dataset, window_size, window_shift, window_stride, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StaticRegexReplaceAttr is an optional argument to StaticRegexReplace. +type StaticRegexReplaceAttr func(optionalAttr) + +// StaticRegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// +// value: If True, the replacement is global, otherwise the replacement +// is done only on the first match. +// If not specified, defaults to true +func StaticRegexReplaceReplaceGlobal(value bool) StaticRegexReplaceAttr { + return func(m optionalAttr) { + m["replace_global"] = value + } +} + +// Replaces the match of pattern in input with rewrite. +// +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: The text to be processed. +// pattern: The regular expression to match the input. +// rewrite: The rewrite to be applied to the matched expression. +// +// Returns The text after applying pattern and rewrite. +func StaticRegexReplace(scope *Scope, input tf.Output, pattern string, rewrite string, optional ...StaticRegexReplaceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pattern": pattern, "rewrite": rewrite} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StaticRegexReplace", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnicodeDecodeAttr is an optional argument to UnicodeDecode. +type UnicodeDecodeAttr func(optionalAttr) + +// UnicodeDecodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeDecodeErrors(value string) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeDecodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD or U+65533.) +// If not specified, defaults to 65533 +func UnicodeDecodeReplacementChar(value int64) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// UnicodeDecodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. +// +// value: Whether to replace the C0 control characters (00-1F) with the +// `replacement_char`. Default is false. +// If not specified, defaults to false +func UnicodeDecodeReplaceControlCharacters(value bool) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["replace_control_characters"] = value + } +} + +// UnicodeDecodeTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func UnicodeDecodeTsplits(value tf.DataType) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Decodes each string in `input` into a sequence of Unicode code points. +// +// The character codepoints for all strings are returned using a single vector +// `char_values`, with strings expanded to characters in row-major order. +// +// The `row_splits` tensor indicates where the codepoints for +// each input string begin and end within the `char_values` tensor. +// In particular, the values for the `i`th +// string (in row-major order) are stored in the slice +// `[row_splits[i]:row_splits[i+1]]`. Thus: +// +// * `char_values[row_splits[i]+j]` is the Unicode codepoint for the `j`th +// character in the `i`th string (in row-major order). +// * `row_splits[i+1] - row_splits[i]` is the number of characters in the `i`th +// string (in row-major order). +// +// Arguments: +// input: The text to be decoded. Can have any shape. Note that the output is flattened +// to a vector of char values. +// input_encoding: Text encoding of the input strings. This is any of the encodings supported +// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. +// +// Returns A 1D int32 tensor containing the row splits.A 1D int32 Tensor containing the decoded codepoints. +func UnicodeDecode(scope *Scope, input tf.Output, input_encoding string, optional ...UnicodeDecodeAttr) (row_splits tf.Output, char_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_encoding": input_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeDecode", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Real-valued fast Fourier transform. +// +// Computes the 1-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most dimension of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the +// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term, +// followed by the `fft_length / 2` positive-frequency terms. +// +// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [1]. The FFT length. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most +// dimension of `input` is replaced with the `fft_length / 2 + 1` unique +// frequency components of its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft +// @end_compatibility +func RFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RFFT", + Input: []tf.Input{ + input, fft_length, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D real-valued fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 2 dimensions of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. +// +// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft2 +// @end_compatibility +func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RFFT2D", + Input: []tf.Input{ + input, fft_length, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorListConcatAttr is an optional argument to TensorListConcat. +type TensorListConcatAttr func(optionalAttr) + +// TensorListConcatElementShape sets the optional element_shape attribute to value. +// If not specified, defaults to +func TensorListConcatElementShape(value tf.Shape) TensorListConcatAttr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Concats all tensors in the list along the 0th dimension. +// +// Requires that all tensors have the same shape except the first dimension. +// +// input_handle: The input list. +// tensor: The concated result. +// lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. +// +func TensorListConcat(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListConcatAttr) (tensor tf.Output, lengths tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorListConcat", + Input: []tf.Input{ + input_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MatrixSolveLsAttr is an optional argument to MatrixSolveLs. +type MatrixSolveLsAttr func(optionalAttr) + +// MatrixSolveLsFast sets the optional fast attribute to value. +// If not specified, defaults to true +func MatrixSolveLsFast(value bool) MatrixSolveLsAttr { + return func(m optionalAttr) { + m["fast"] = value + } +} + +// Solves one or more linear least-squares problems. +// +// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions +// form real or complex matrices of size `[M, N]`. `Rhs` is a tensor of the same +// type as `matrix` and shape `[..., M, K]`. +// The output is a tensor shape `[..., N, K]` where each output matrix solves +// each of the equations +// `matrix[..., :, :]` * `output[..., :, :]` = `rhs[..., :, :]` +// in the least squares sense. +// +// We use the following notation for (complex) matrix and right-hand sides +// in the batch: +// +// `matrix`=\\(A \in \mathbb{C}^{m \times n}\\), +// `rhs`=\\(B \in \mathbb{C}^{m \times k}\\), +// `output`=\\(X \in \mathbb{C}^{n \times k}\\), +// `l2_regularizer`=\\(\lambda \in \mathbb{R}\\). +// +// If `fast` is `True`, then the solution is computed by solving the normal +// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then +// \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares +// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\). +// If \\(m \lt n\\) then `output` is computed as +// \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the +// minimum-norm solution to the under-determined linear system, i.e. +// \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\), +// subject to \\(A Z = B\\). Notice that the fast path is only numerically stable +// when \\(A\\) is numerically full rank and has a condition number +// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or \\(\lambda\\) is +// sufficiently large. +// +// If `fast` is `False` an algorithm based on the numerically robust complete +// orthogonal decomposition is used. This computes the minimum-norm +// least-squares solution, even when \\(A\\) is rank deficient. This path is +// typically 6-7 times slower than the fast path. If `fast` is `False` then +// `l2_regularizer` is ignored. +// +// Arguments: +// matrix: Shape is `[..., M, N]`. +// rhs: Shape is `[..., M, K]`. +// l2_regularizer: Scalar tensor. +// +// @compatibility(numpy) +// Equivalent to np.linalg.lstsq +// @end_compatibility +// +// Returns Shape is `[..., N, K]`. +func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixSolveLs", + Input: []tf.Input{ + matrix, rhs, l2_regularizer, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BiasAddGradAttr is an optional argument to BiasAddGrad. +type BiasAddGradAttr func(optionalAttr) + +// BiasAddGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the bias tensor will be added to the last dimension +// of the value tensor. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// The tensor will be added to "in_channels", the third-to-the-last +// dimension. +// If not specified, defaults to "NHWC" +func BiasAddGradDataFormat(value string) BiasAddGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// The backward operation for "BiasAdd" on the "bias" tensor. +// +// It accumulates all the values from out_backprop into the feature dimension. +// For NHWC data format, the feature dimension is the last. For NCHW data format, +// the feature dimension is the third-to-last. +// +// Arguments: +// out_backprop: Any number of dimensions. +// +// Returns 1-D with size the feature dimension of `out_backprop`. +func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BiasAddGrad", + Input: []tf.Input{ + out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the current size of the TensorArray. +// +// Arguments: +// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// flow_in: A float scalar that enforces proper chaining of operations. +// +// Returns The current size of the TensorArray. +func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArraySizeV3", + Input: []tf.Input{ + handle, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Worker heartbeat op. +// +// Heartbeats may be sent periodically to indicate the coordinator is still active, +// to retrieve the current worker status and to expedite shutdown when necessary. +// +// Arguments: +// request: A string tensor containing a serialized WorkerHeartbeatRequest +// +// Returns A string tensor containing a serialized WorkerHeartbeatResponse +func WorkerHeartbeat(scope *Scope, request tf.Output) (response tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "WorkerHeartbeat", + Input: []tf.Input{ + request, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns which elements of x are NaN. +// +// @compatibility(numpy) +// Equivalent to np.isnan +// @end_compatibility +func IsNan(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsNan", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient for the tanh of `x` wrt its input. +// +// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy` +// is the corresponding input gradient. +func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TanhGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Applies sparse addition to `input` using individual values or slices +// +// from `updates` according to indices `indices`. The updates are non-aliasing: +// `input` is only modified in-place if no other operations will use it. +// Otherwise, a copy of `input` is made. This operation has a gradient with +// respect to both `input` and `updates`. +// +// `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `input`. +// It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or `(P-K)`-dimensional slices +// (if `K < P`) along the `K`th dimension of `input`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$ +// +// For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 +// elements. In Python, that addition would look like this: +// +// input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// output = tf.scatter_nd_non_aliasing_add(input, indices, updates) +// with tf.Session() as sess: +// print(sess.run(output)) +// +// The resulting value `output` would look like this: +// +// [1, 13, 3, 14, 14, 6, 7, 20] +// +// See `tf.scatter_nd` for more details about how to make updates to slices. +// +// Arguments: +// input: A Tensor. +// indices: A Tensor. Must be one of the following types: `int32`, `int64`. +// A tensor of indices into `input`. +// updates: A Tensor. Must have the same type as ref. A tensor of updated values +// to add to `input`. +// +// Returns A `Tensor` with the same shape as `input`, containing values of `input` +// updated with `updates`. +func ScatterNdNonAliasingAdd(scope *Scope, input tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ScatterNdNonAliasingAdd", + Input: []tf.Input{ + input, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringSplitV2Attr is an optional argument to StringSplitV2. +type StringSplitV2Attr func(optionalAttr) + +// StringSplitV2Maxsplit sets the optional maxsplit attribute to value. +// +// value: An `int`. If `maxsplit > 0`, limit of the split of the result. +// If not specified, defaults to -1 +func StringSplitV2Maxsplit(value int64) StringSplitV2Attr { + return func(m optionalAttr) { + m["maxsplit"] = value + } +} + +// Split elements of `source` based on `sep` into a `SparseTensor`. +// +// Let N be the size of source (typically N will be the batch size). Split each +// element of `source` based on `sep` and return a `SparseTensor` +// containing the split tokens. Empty tokens are ignored. +// +// For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', +// then the output will be +// ``` +// st.indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// st.shape = [2, 3] +// st.values = ['hello', 'world', 'a', 'b', 'c'] +// ``` +// +// If `sep` is given, consecutive delimiters are not grouped together and are +// deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and +// sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty +// string, consecutive whitespace are regarded as a single separator, and the +// result will contain no empty strings at the startor end if the string has +// leading or trailing whitespace. +// +// Note that the above mentioned behavior matches python's str.split. +// +// Arguments: +// input: `1-D` string `Tensor`, the strings to split. +// sep: `0-D` string `Tensor`, the delimiter character. +func StringSplitV2(scope *Scope, input tf.Output, sep tf.Output, optional ...StringSplitV2Attr) (indices tf.Output, values tf.Output, shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringSplitV2", + Input: []tf.Input{ + input, sep, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent. +type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr) + +// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If True, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Sparse update '*var' as FOBOS algorithm with fixed learning rate. +// +// That is for rows we have grad for, we update var as follows: +// prox_v = var - alpha * grad +// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyProximalGradientDescent", + Input: []tf.Input{ + var_, alpha, l1, l2, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Saves input tensors slices to disk. +// +// This is like `Save` except that tensors can be listed in the saved file as being +// a slice of a larger tensor. `shapes_and_slices` specifies the shape of the +// larger tensor and the slice that this tensor covers. `shapes_and_slices` must +// have as many elements as `tensor_names`. +// +// Elements of the `shapes_and_slices` input must either be: +// +// * The empty string, in which case the corresponding tensor is +// saved normally. +// * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the +// `dimI` are the dimensions of the larger tensor and `slice-spec` +// specifies what part is covered by the tensor to save. +// +// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1` +// where each `sliceI` is either: +// +// * The string `-` meaning that the slice covers all indices of this dimension +// * `start,length` where `start` and `length` are integers. In that +// case the slice covers `length` indices starting at `start`. +// +// See also `Save`. +// +// Arguments: +// filename: Must have a single element. The name of the file to which we write the +// tensor. +// tensor_names: Shape `[N]`. The names of the tensors to be saved. +// shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when +// saving the tensors. +// data: `N` tensors to save. +// +// Returns the created operation. +func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SaveSlices", + Input: []tf.Input{ + filename, tensor_names, shapes_and_slices, tf.OutputList(data), + }, + } + return scope.AddOperation(opspec) +} + +// Component-wise multiplies a SparseTensor by a dense Tensor. +// +// The output locations corresponding to the implicitly zero elements in the sparse +// tensor will be zero (i.e., will not take up storage space), regardless of the +// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseMul", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 3D real-valued fast Fourier transform. +// +// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 3 dimensions of `input`. +// +// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 3 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along each axis `IRFFT3D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 3D real Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.irfftn with 3 dimensions. +// @end_compatibility +func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IRFFT3D", + Input: []tf.Input{ + input, fft_length, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchToSpace for 4-D tensors of type T. +// +// This is a legacy version of the more general BatchToSpaceND. +// +// Rearranges (permutes) data from batch into blocks of spatial data, followed by +// cropping. This is the reverse transformation of SpaceToBatch. More specifically, +// this op outputs a copy of the input tensor where values from the `batch` +// dimension are moved in spatial blocks to the `height` and `width` dimensions, +// followed by cropping along the `height` and `width` dimensions. +// +// Arguments: +// input: 4-D tensor with shape +// `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size, +// depth]`. Note that the batch size of the input tensor must be divisible by +// `block_size * block_size`. +// crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies +// how many elements to crop from the intermediate result across the spatial +// dimensions as follows: +// +// crops = [[crop_top, crop_bottom], [crop_left, crop_right]] +// +// +// Returns 4-D with shape `[batch, height, width, depth]`, where: +// +// height = height_pad - crop_top - crop_bottom +// width = width_pad - crop_left - crop_right +// +// The attr `block_size` must be greater than one. It indicates the block size. +// +// Some examples: +// +// (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 3]` and value: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// The output tensor has shape `[1, 4, 4, 1]` and value: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], +// [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] +// ``` +// +// The output tensor has shape `[2, 2, 4, 1]` and value: +// +// ``` +// x = [[[[1], [3]], [[5], [7]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +func BatchToSpace(scope *Scope, input tf.Output, crops tf.Output, block_size int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"block_size": block_size} + opspec := tf.OpSpec{ + Type: "BatchToSpace", + Input: []tf.Input{ + input, crops, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2. +type TFRecordReaderV2Attr func(optionalAttr) + +// TFRecordReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TFRecordReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value. +// If not specified, defaults to "" +func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["compression_type"] = value + } +} + +// A Reader that outputs the records from a TensorFlow Records file. +// +// Returns The handle to reference the Reader. +func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TFRecordReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapSizeAttr is an optional argument to OrderedMapSize. +type OrderedMapSizeAttr func(optionalAttr) + +// OrderedMapSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapSizeCapacity(value int64) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapSizeMemoryLimit(value int64) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapSizeContainer(value string) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapSizeSharedName(value string) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func OrderedMapSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// InfeedEnqueueAttr is an optional argument to InfeedEnqueue. +type InfeedEnqueueAttr func(optionalAttr) + +// InfeedEnqueueShape sets the optional shape attribute to value. +// +// value: The shape of the tensor. +// If not specified, defaults to <> +func InfeedEnqueueShape(value tf.Shape) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// InfeedEnqueueLayout sets the optional layout attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence. +// If a layout attribute is passed, but its values are all -1, the layout will +// be computed by the infeed operation. +// If not specified, defaults to <> +func InfeedEnqueueLayout(value []int64) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["layout"] = value + } +} + +// InfeedEnqueueDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func InfeedEnqueueDeviceOrdinal(value int64) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// An op which feeds a single Tensor value into the computation. +// +// Arguments: +// input: A tensor that will be provided using the infeed mechanism. +// +// Returns the created operation. +func InfeedEnqueue(scope *Scope, input tf.Output, optional ...InfeedEnqueueAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueue", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. +type SampleDistortedBoundingBoxAttr func(optionalAttr) + +// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to non-zero, the random number +// generator is seeded by the given `seed`. Otherwise, it is seeded by a random +// seed. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. +// +// value: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. +// If not specified, defaults to 0.1 +func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["min_object_covered"] = value + } +} + +// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. +// +// value: The cropped area of the image must have an aspect ratio = +// width / height within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value + } +} + +// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// +// value: The cropped area of the image must contain a fraction of the +// supplied image within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["area_range"] = value + } +} + +// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// +// value: Number of attempts at generating a cropped region of the image +// of the specified constraints. After `max_attempts` failures, return the entire +// image. +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["max_attempts"] = value + } +} + +// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// +// value: Controls behavior if no bounding boxes supplied. +// If true, assume an implicit bounding box covering the whole input. If false, +// raise an error. +// If not specified, defaults to false +func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. +// +// Bounding box annotations are often supplied in addition to ground-truth labels +// in image recognition or object localization tasks. A common technique for +// training such a system is to randomly distort an image while preserving +// its content, i.e. *data augmentation*. This Op outputs a randomly distorted +// localization of an object, i.e. bounding box, given an `image_size`, +// `bounding_boxes` and a series of constraints. +// +// The output of this Op is a single bounding box that may be used to crop the +// original image. The output is returned as 3 tensors: `begin`, `size` and +// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the +// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize +// what the bounding box looks like. +// +// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, +// +// ```python +// # Generate a single distorted bounding box. +// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( +// tf.shape(image), +// bounding_boxes=bounding_boxes) +// +// # Draw the bounding box in an image summary. +// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), +// bbox_for_draw) +// tf.summary.image('images_with_box', image_with_box) +// +// # Employ the bounding box to distort the image. +// distorted_image = tf.slice(image, begin, size) +// ``` +// +// Note that if no bounding box information is available, setting +// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit +// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is +// false and no bounding boxes are supplied, an error is raised. +// +// Arguments: +// image_size: 1-D, containing `[height, width, channels]`. +// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes +// associated with the image. +// +// Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to +// `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to +// `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. +// Provide as input to `tf.image.draw_bounding_boxes`. +func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SampleDistortedBoundingBox", + Input: []tf.Input{ + image_size, bounding_boxes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// AudioSummaryV2Attr is an optional argument to AudioSummaryV2. +type AudioSummaryV2Attr func(optionalAttr) + +// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value. +// +// value: Max number of batch elements to generate audio for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr { + return func(m optionalAttr) { + m["max_outputs"] = value + } +} + +// Outputs a `Summary` protocol buffer with audio. +// +// The summary has up to `max_outputs` summary values containing audio. The +// audio is built from `tensor` which must be 3-D with shape `[batch_size, +// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are +// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. +// * If `max_outputs` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. +// +// Arguments: +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 2-D of shape `[batch_size, frames]`. +// sample_rate: The sample rate of the signal in hertz. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSummaryV2", + Input: []tf.Input{ + tag, tensor, sample_rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdagradParametersGradAccumDebug. +type LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Adagrad embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the Adagrad optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdagradParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process and will never change. However, it is not suitable for cryptography. +// This function may be used when CPU time is scarce and inputs are trusted or +// unimportant. There is a risk of adversaries constructing inputs that all hash +// to the same bucket. To prevent this problem, use a strong hash function with +// `tf.string_to_hash_bucket_strong`. +// +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucketFast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A placeholder op for a value that will be fed into the computation. +// +// Arguments: +// dtype: The type of elements in the tensor. +// shape: The shape of the tensor. +// +// Returns A tensor that will be provided using the infeed mechanism. +func InfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "InfeedDequeue", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeProtoV2Attr is an optional argument to DecodeProtoV2. +type DecodeProtoV2Attr func(optionalAttr) + +// DecodeProtoV2DescriptorSource sets the optional descriptor_source attribute to value. +// +// value: Either the special value `local://` or a path to a file containing +// a serialized `FileDescriptorSet`. +// If not specified, defaults to "local://" +func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["descriptor_source"] = value + } +} + +// DecodeProtoV2MessageFormat sets the optional message_format attribute to value. +// +// value: Either `binary` or `text`. +// If not specified, defaults to "binary" +func DecodeProtoV2MessageFormat(value string) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["message_format"] = value + } +} + +// DecodeProtoV2Sanitize sets the optional sanitize attribute to value. +// +// value: Whether to sanitize the result or not. +// If not specified, defaults to false +func DecodeProtoV2Sanitize(value bool) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["sanitize"] = value + } +} + +// The op extracts fields from a serialized protocol buffers message into tensors. +// +// The `decode_proto` op extracts fields from a serialized protocol buffers +// message into tensors. The fields in `field_names` are decoded and converted +// to the corresponding `output_types` if possible. +// +// A `message_type` name must be provided to give context for the field +// names. The actual message descriptor can be looked up either in the +// linked-in descriptor pool or a filename provided by the caller using +// the `descriptor_source` attribute. +// +// Each output tensor is a dense tensor. This means that it is padded to +// hold the largest number of repeated elements seen in the input +// minibatch. (The shape is also padded by one to prevent zero-sized +// dimensions). The actual repeat counts for each example in the +// minibatch can be found in the `sizes` output. In many cases the output +// of `decode_proto` is fed immediately into tf.squeeze if missing values +// are not a concern. When using tf.squeeze, always pass the squeeze +// dimension explicitly to avoid surprises. +// +// For the most part, the mapping between Proto field types and +// TensorFlow dtypes is straightforward. However, there are a few +// special cases: +// +// - A proto field that contains a submessage or group can only be converted +// to `DT_STRING` (the serialized submessage). This is to reduce the +// complexity of the API. The resulting string can be used as input +// to another instance of the decode_proto op. +// +// - TensorFlow lacks support for unsigned integers. The ops represent uint64 +// types as a `DT_INT64` with the same twos-complement bit pattern +// (the obvious way). Unsigned int32 values can be represented exactly by +// specifying type `DT_INT64`, or using twos-complement if the caller +// specifies `DT_INT32` in the `output_types` attribute. +// +// The `descriptor_source` attribute selects a source of protocol +// descriptors to consult when looking up `message_type`. This may be a +// filename containing a serialized `FileDescriptorSet` message, +// or the special value `local://`, in which case only descriptors linked +// into the code will be searched; the filename can be on any filesystem +// accessible to TensorFlow. +// +// You can build a `descriptor_source` file using the `--descriptor_set_out` +// and `--include_imports` options to the protocol compiler `protoc`. +// +// The `local://` database only covers descriptors linked into the +// code via C++ libraries, not Python imports. You can link in a proto descriptor +// by creating a cc_library target with alwayslink=1. +// +// Both binary and text proto serializations are supported, and can be +// chosen using the `format` attribute. +// +// Arguments: +// bytes: Tensor of serialized protos with shape `batch_shape`. +// message_type: Name of the proto message type to decode. +// field_names: List of strings containing proto field names. An extension field can be decoded +// by using its full name, e.g. EXT_PACKAGE.EXT_FIELD_NAME. +// output_types: List of TF types to use for the respective field in field_names. +// +// Returns Tensor of int32 with shape `[batch_shape, len(field_names)]`. +// Each entry is the number of values found for the corresponding field. +// Optional fields may have 0 or 1 values.List of tensors containing values for the corresponding field. +// `values[i]` has datatype `output_types[i]` +// and shape `[batch_shape, max(sizes[...,i])]`. +func DecodeProtoV2(scope *Scope, bytes tf.Output, message_type string, field_names []string, output_types []tf.DataType, optional ...DecodeProtoV2Attr) (sizes tf.Output, values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"message_type": message_type, "field_names": field_names, "output_types": output_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeProtoV2", + Input: []tf.Input{ + bytes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + sizes = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("DecodeProtoV2", err) + return + } + return sizes, values +} + +// Enqueue multiple Tensor values on the computation outfeed. +// +// Arguments: +// inputs: A list of tensors that will be inserted into the outfeed queue as an +// XLA tuple. +// +// Returns the created operation. +func OutfeedEnqueueTuple(scope *Scope, inputs []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OutfeedEnqueueTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient of `igamma(a, x)` wrt `a`. +func IgammaGradA(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IgammaGradA", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingRMSPropParameters. +type LoadTPUEmbeddingRMSPropParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingRMSPropParametersTableId(value int64) LoadTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersTableName(value string) LoadTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load RMSProp embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the RMSProp optimization algorithm. +// ms: Value of ms used in the RMSProp optimization algorithm. +// mom: Value of mom used in the RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingRMSPropParameters", + Input: []tf.Input{ + parameters, ms, mom, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Encode audio data using the WAV file format. +// +// This operation will generate a string suitable to be saved out to create a .wav +// audio file. It will be encoded in the 16-bit PCM format. It takes in float +// values in the range -1.0f to 1.0f, and any outside that value will be clamped to +// that range. +// +// `audio` is a 2-D float Tensor of shape `[length, channels]`. +// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). +// +// Arguments: +// audio: 2-D with shape `[length, channels]`. +// sample_rate: Scalar containing the sample frequency. +// +// Returns 0-D. WAV-encoded file contents. +func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EncodeWav", + Input: []tf.Input{ + audio, sample_rate, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // DecodeCompressedAttr is an optional argument to DecodeCompressed. type DecodeCompressedAttr func(optionalAttr) @@ -11723,72 +19326,289 @@ func DecodeCompressed(scope *Scope, bytes tf.Output, optional ...DecodeCompresse return op.Output(0) } -// Convert JSON-encoded Example records to binary protocol buffer strings. -// -// This op translates a tensor containing Example records, encoded using -// the [standard JSON -// mapping](https://developers.google.com/protocol-buffers/docs/proto3#json), -// into a tensor containing the same records encoded as binary protocol -// buffers. The resulting tensor can then be fed to any of the other -// Example-parsing ops. +// Creates a tree ensemble model and returns a handle to it. // // Arguments: -// json_examples: Each string is a JSON object serialized according to the JSON -// mapping of the Example proto. +// tree_ensemble_handle: Handle to the tree ensemble resource to be created. +// stamp_token: Token to use as the initial value of the resource stamp. +// tree_ensemble_serialized: Serialized proto of the tree ensemble. // -// Returns Each string is a binary Example protocol buffer corresponding -// to the respective element of `json_examples`. -func DecodeJSONExample(scope *Scope, json_examples tf.Output) (binary_examples tf.Output) { +// Returns the created operation. +func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "DecodeJSONExample", + Type: "BoostedTreesCreateEnsemble", Input: []tf.Input{ - json_examples, + tree_ensemble_handle, stamp_token, tree_ensemble_serialized, + }, + } + return scope.AddOperation(opspec) +} + +// Gets next element for the provided shard number. +// +// Arguments: +// multi_device_iterator: A MultiDeviceIterator resource. +// shard_num: Integer representing which shard to fetch data for. +// incarnation_id: Which incarnation of the MultiDeviceIterator is running. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +// +// Returns Result of the get_next on the dataset. +func MultiDeviceIteratorGetNextFromShard(scope *Scope, multi_device_iterator tf.Output, shard_num tf.Output, incarnation_id tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorGetNextFromShard", + Input: []tf.Input{ + multi_device_iterator, shard_num, incarnation_id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("MultiDeviceIteratorGetNextFromShard", err) + return + } + return components +} + +// Computes softplus gradients for a softplus operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding softplus operation. +// features: The features passed as input to the corresponding softplus operation. +// +// Returns The gradients: `gradients / (1 + exp(-features))`. +func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SoftplusGrad", + Input: []tf.Input{ + gradients, features, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// DecodePaddedRawAttr is an optional argument to DecodePaddedRaw. -type DecodePaddedRawAttr func(optionalAttr) - -// DecodePaddedRawLittleEndian sets the optional little_endian attribute to value. -// -// value: Whether the input `input_bytes` is in little-endian order. Ignored for -// `out_type` values that are stored in a single byte, like `uint8` -// If not specified, defaults to true -func DecodePaddedRawLittleEndian(value bool) DecodePaddedRawAttr { - return func(m optionalAttr) { - m["little_endian"] = value - } -} - -// Reinterpret the bytes of a string as a vector of numbers. +// Serializes the tree ensemble to a proto. // // Arguments: -// input_bytes: Tensor of string to be decoded. -// fixed_length: Length in bytes for each element of the decoded output. Must be a multiple -// of the size of the output type. +// tree_ensemble_handle: Handle to the tree ensemble. // -// -// Returns A Tensor with one more dimension than the input `bytes`. The added dimension -// will have size equal to the length of the elements of `bytes` divided by the -// number of bytes to represent `out_type`. -func DecodePaddedRaw(scope *Scope, input_bytes tf.Output, fixed_length tf.Output, out_type tf.DataType, optional ...DecodePaddedRawAttr) (output tf.Output) { +// Returns Stamp token of the tree ensemble resource.Serialized proto of the ensemble. +func BoostedTreesSerializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, tree_ensemble_serialized tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "BoostedTreesSerializeEnsemble", + Input: []tf.Input{ + tree_ensemble_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Assigns sparse updates to the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] = updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] +// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterUpdate", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// A TPU core selector Op. +// +// This Op produces a set of TPU cores (for warm-up) or a single TPU core +// (for regular inference) to execute the TPU program on. The output is +// consumed by TPUPartitionedCall. +// +// Returns A vector 1 or more TPU cores. +func TPUOrdinalSelector(scope *Scope) (device_ordinals tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TPUOrdinalSelector", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Connects N inputs to an N-way replicated TPU computation. +func TPUReplicatedInput(scope *Scope, inputs []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TPUReplicatedInput", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp. +type ResourceApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the centered RMSProp algorithm. +// +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. +// +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) +// +// mg <- rho * mg_{t-1} + (1-rho) * grad +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "DecodePaddedRaw", + Type: "ResourceApplyCenteredRMSProp", Input: []tf.Input{ - input_bytes, fixed_length, + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SqueezeAttr is an optional argument to Squeeze. +type SqueezeAttr func(optionalAttr) + +// SqueezeAxis sets the optional axis attribute to value. +// +// value: If specified, only squeezes the dimensions listed. The dimension +// index starts at 0. It is an error to squeeze a dimension that is not 1. Must +// be in the range `[-rank(input), rank(input))`. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func SqueezeAxis(value []int64) SqueezeAttr { + return func(m optionalAttr) { + m["squeeze_dims"] = value + } +} + +// Removes dimensions of size 1 from the shape of a tensor. +// +// Given a tensor `input`, this operation returns a tensor of the same type with +// all dimensions of size 1 removed. If you don't want to remove all size 1 +// dimensions, you can remove specific size 1 dimensions by specifying +// `axis`. +// +// For example: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t)) ==> [2, 3] +// ``` +// +// Or, to remove specific size 1 dimensions: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] +// ``` +// +// Arguments: +// input: The `input` to squeeze. +// +// Returns Contains the same data as `input`, but has one or more dimensions of +// size 1 removed. +func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Squeeze", + Input: []tf.Input{ + input, }, Attrs: attrs, } @@ -11796,6 +19616,2137 @@ func DecodePaddedRaw(scope *Scope, input_bytes tf.Output, fixed_length tf.Output return op.Output(0) } +// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. +type ResourceScatterNdUpdateAttr func(optionalAttr) + +// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse `updates` to individual values or slices within a given +// +// variable according to `indices`. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. +// ``` +// +// For example, say we want to update 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that update would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1] ,[7]]) +// updates = tf.constant([9, 10, 11, 12]) +// update = tf.scatter_nd_update(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(update) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 11, 3, 10, 9, 6, 7, 12] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of updated +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdUpdate", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedDepthwiseConv2DAttr is an optional argument to QuantizedDepthwiseConv2D. +type QuantizedDepthwiseConv2DAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2DAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes quantized depthwise Conv2D. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// strides: List of stride values. +// +// +// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2D", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// LoadTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingADAMParametersGradAccumDebug. +type LoadTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load ADAM embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the ADAM optimization algorithm. +// momenta: Value of momenta used in the ADAM optimization algorithm. +// velocities: Value of velocities used in the ADAM optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the ADAM optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingADAMParametersGradAccumDebug", + Input: []tf.Input{ + parameters, momenta, velocities, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the absolute value of a tensor. +// +// Given a tensor `x`, this operation returns a tensor containing the absolute +// value of each element in `x`. For example, if x is an input element and y is +// an output element, this operation computes \\(y = |x|\\). +func Abs(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Abs", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringJoinAttr is an optional argument to StringJoin. +type StringJoinAttr func(optionalAttr) + +// StringJoinSeparator sets the optional separator attribute to value. +// +// value: string, an optional join separator. +// If not specified, defaults to "" +func StringJoinSeparator(value string) StringJoinAttr { + return func(m optionalAttr) { + m["separator"] = value + } +} + +// Joins the strings in the given list of string tensors into one tensor; +// +// with the given separator (default is an empty separator). +// +// Arguments: +// inputs: A list of string tensors. The tensors must all have the same shape, +// or be scalars. Scalars may be mixed in; these will be broadcast to the shape +// of non-scalar inputs. +func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringJoin", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TopKV2Attr is an optional argument to TopKV2. +type TopKV2Attr func(optionalAttr) + +// TopKV2Sorted sets the optional sorted attribute to value. +// +// value: If true the resulting `k` elements will be sorted by the values in +// descending order. +// If not specified, defaults to true +func TopKV2Sorted(value bool) TopKV2Attr { + return func(m optionalAttr) { + m["sorted"] = value + } +} + +// Finds values and indices of the `k` largest elements for the last dimension. +// +// If the input is a vector (rank-1), finds the `k` largest entries in the vector +// and outputs their values and indices as vectors. Thus `values[j]` is the +// `j`-th largest entry in `input`, and its index is `indices[j]`. +// +// For matrices (resp. higher rank input), computes the top `k` entries in each +// row (resp. vector along the last dimension). Thus, +// +// values.shape = indices.shape = input.shape[:-1] + [k] +// +// If two elements are equal, the lower-index element appears first. +// +// Arguments: +// input: 1-D or higher with last dimension at least `k`. +// k: 0-D. Number of top elements to look for along the last dimension (along each +// row for matrices). +// +// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. +func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TopKV2", + Input: []tf.Input{ + input, k, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. +type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) + +// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["min"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["max"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxArgs operation. +// +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. +// +// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: +// `gradients * (inputs >= min && inputs <= max)`. +func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxArgsGradient", + Input: []tf.Input{ + gradients, inputs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. +type ResourceApplyAdadeltaAttr func(optionalAttr) + +// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var, accum and update_accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the adadelta scheme. +// +// accum = rho() * accum + (1 - rho()) * grad.square(); +// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; +// update_accum = rho() * update_accum + (1 - rho()) * update.square(); +// var -= update; +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// accum_update: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdadelta", + Input: []tf.Input{ + var_, accum, accum_update, lr, rho, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// L2 Loss. +// +// Computes half the L2 norm of a tensor without the `sqrt`: +// +// output = sum(t ** 2) / 2 +// +// Arguments: +// t: Typically 2-D, but may have any dimensions. +// +// Returns 0-D. +func L2Loss(scope *Scope, t tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "L2Loss", + Input: []tf.Input{ + t, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Split a `SparseTensor` into `num_split` tensors along one dimension. +// +// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices +// `[0 : shape[split_dim] % num_split]` gets one extra dimension. +// For example, if `split_dim = 1` and `num_split = 2` and the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// output_tensor[0] = shape = [2, 4] +// [ a ] +// [b c ] +// +// output_tensor[1] = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// split_dim: 0-D. The dimension along which to split. Must be in the range +// `[0, rank(shape))`. +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// num_split: The number of ways to split. +// +// Returns A list of 1-D tensors represents the values of the output sparse +// tensors.A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_split": num_split} + opspec := tf.OpSpec{ + Type: "SparseSplit", + Input: []tf.Input{ + split_dim, indices, values, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + return output_indices, output_values, output_shape +} + +// ResourceApplyAddSignAttr is an optional argument to ResourceApplyAddSign. +type ResourceApplyAddSignAttr func(optionalAttr) + +// ResourceApplyAddSignUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAddSignUseLocking(value bool) ResourceApplyAddSignAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AddSign update. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- (alpha + sign_decay * sign(g) *sign(m)) * g +// variable <- variable - lr_t * update +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// alpha: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, alpha tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyAddSignAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAddSign", + Input: []tf.Input{ + var_, m, lr, alpha, sign_decay, beta, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to RetrieveTPUEmbeddingStochasticGradientDescentParameters. +type RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve SGD embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the stochastic gradient descent optimization algorithm. +func RetrieveTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr) (parameters tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingStochasticGradientDescentParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Bucketize each feature based on bucket boundaries. +// +// An op that returns a list of float tensors, where each tensor represents the +// bucketized values for a single feature. +// +// Arguments: +// float_values: float; List of Rank 1 Tensor each containing float values for a single feature. +// bucket_boundaries: float; List of Rank 1 Tensors each containing the bucket boundaries for a single +// feature. +// +// Returns int; List of Rank 1 Tensors each containing the bucketized values for a single feature. +func BoostedTreesBucketize(scope *Scope, float_values []tf.Output, bucket_boundaries []tf.Output) (buckets []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesBucketize", + Input: []tf.Input{ + tf.OutputList(float_values), tf.OutputList(bucket_boundaries), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if buckets, idx, err = makeOutputList(op, idx, "buckets"); err != nil { + scope.UpdateErr("BoostedTreesBucketize", err) + return + } + return buckets +} + +// EncodePngAttr is an optional argument to EncodePng. +type EncodePngAttr func(optionalAttr) + +// EncodePngCompression sets the optional compression attribute to value. +// +// value: Compression level. +// If not specified, defaults to -1 +func EncodePngCompression(value int64) EncodePngAttr { + return func(m optionalAttr) { + m["compression"] = value + } +} + +// PNG-encode an image. +// +// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]` +// where `channels` is: +// +// * 1: for grayscale. +// * 2: for grayscale + alpha. +// * 3: for RGB. +// * 4: for RGBA. +// +// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder +// default or a value from 0 to 9. 9 is the highest compression level, generating +// the smallest output, but is slower. +// +// Arguments: +// image: 3-D with shape `[height, width, channels]`. +// +// Returns 0-D. PNG-encoded image. +func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodePng", + Input: []tf.Input{ + image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnbatchAttr is an optional argument to Unbatch. +type UnbatchAttr func(optionalAttr) + +// UnbatchContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func UnbatchContainer(value string) UnbatchAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// UnbatchSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func UnbatchSharedName(value string) UnbatchAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Reverses the operation of Batch for a single output Tensor. +// +// An instance of Unbatch either receives an empty batched_tensor, in which case it +// asynchronously waits until the values become available from a concurrently +// running instance of Unbatch with the same container and shared_name, or receives +// a non-empty batched_tensor in which case it finalizes all other concurrently +// running instances and outputs its own element from the batch. +// +// batched_tensor: The possibly transformed output of Batch. The size of the first +// dimension should remain unchanged by the transformations for the operation to +// work. +// batch_index: The matching batch_index obtained from Batch. +// id: The id scalar emitted by Batch. +// unbatched_tensor: The Tensor corresponding to this execution. +// timeout_micros: Maximum amount of time (in microseconds) to wait to receive the +// batched input tensor associated with a given invocation of the op. +// container: Container to control resource sharing. +// shared_name: Instances of Unbatch with the same container and shared_name are +// assumed to possibly belong to the same batch. If left empty, the op name will +// be used as the shared name. +func Unbatch(scope *Scope, batched_tensor tf.Output, batch_index tf.Output, id tf.Output, timeout_micros int64, optional ...UnbatchAttr) (unbatched_tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"timeout_micros": timeout_micros} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unbatch", + Input: []tf.Input{ + batched_tensor, batch_index, id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) + +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. +// +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { + return func(m optionalAttr) { + m["iou_threshold"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppression", + Input: []tf.Input{ + boxes, scores, max_output_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. +type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the centered RMSProp algorithm. +// +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. +// +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. +// +// Returns the created operation. +func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyCenteredRMSProp", + Input: []tf.Input{ + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// A container for an iterator resource. +// +// Returns A handle to the iterator that can be passed to a "MakeIterator" or +// "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents +// resource sharing by name, and does not keep a reference to the resource +// container.A variant deleter that should be passed into the op that deletes the iterator. +func AnonymousIteratorV2(scope *Scope, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output, deleter tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "AnonymousIteratorV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MutexV2Attr is an optional argument to MutexV2. +type MutexV2Attr func(optionalAttr) + +// MutexV2Container sets the optional container attribute to value. +// +// value: If non-empty, this variable is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutexV2Container(value string) MutexV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutexV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this variable is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func MutexV2SharedName(value string) MutexV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a Mutex resource that can be locked by `MutexLock`. +// +// Returns The mutex resource. +func MutexV2(scope *Scope, optional ...MutexV2Attr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutexV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Pop the element at the top of the stack. +// +// Arguments: +// handle: The handle to a stack. +// elem_type: The type of the elem that is popped. +// +// Returns The tensor that is popped from the top of the stack. +func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"elem_type": elem_type} + opspec := tf.OpSpec{ + Type: "StackPopV2", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = min(ref[indices, ...], updates[...]) +// +// # Vector indices (for each i) +// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions are combined. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterMin", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyKerasMomentumAttr is an optional argument to ResourceSparseApplyKerasMomentum. +type ResourceSparseApplyKerasMomentumAttr func(optionalAttr) + +// ResourceSparseApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyKerasMomentumUseLocking(value bool) ResourceSparseApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var + momentum * accum, so in the end, the var you get is actually +// var + momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyKerasMomentumUseNesterov(value bool) ResourceSparseApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. +// +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// That is for rows we have grad for, we update var and accum as follows: +// +// accum = accum * momentum - lr * grad +// var += accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyKerasMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyKerasMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, indices, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical. +type CudnnRNNParamsToCanonicalAttr func(optionalAttr) + +// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Retrieves CudnnRNN params in canonical form. +// +// Retrieves a set of weights from the opaque params buffer that can be saved and +// restored in a way compatible with future runs. +// +// Note that the params buffer may not be compatible across different GPUs. So any +// save and restoration should be converted to and from the canonical weights and +// biases. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// num_params: number of parameter sets for all layers. +// Each layer may contain multiple parameter sets, with each set consisting of +// a weight matrix and a bias vector. +// weights: the canonical form of weights that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// biases: the canonical form of biases that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_params": num_params} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNParamsToCanonical", + Input: []tf.Input{ + num_layers, num_units, input_size, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonical", err) + return + } + if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonical", err) + return + } + return weights, biases +} + +// Records the latency of producing `input_dataset` elements in a StatsAggregator. +func ExperimentalLatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalLatencyStatsDataset", + Input: []tf.Input{ + input_dataset, tag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds sparse `updates` to an existing tensor according to `indices`. +// +// This operation creates a new tensor by adding sparse `updates` to the passed +// in `tensor`. +// This operation is very similar to `tf.scatter_nd_add`, except that the updates +// are added onto an existing tensor (as opposed to a variable). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of tensor_scatter_add is to add individual elements to a +// tensor by index. For example, say we want to add 4 elements in a rank-1 +// tensor with 8 elements. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// tensor = tf.ones([8], dtype=tf.int32) +// updated = tf.tensor_scatter_add(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [1, 12, 1, 11, 10, 1, 1, 13] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// tensor = tf.ones([4, 4, 4]) +// updated = tf.tensor_scatter_add(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], +// [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor copied from tensor and updates added according to the indices. +func TensorScatterAdd(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterAdd", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes exponential of x element-wise. \\(y = e^x\\). +// +// This function computes the exponential of every element in the input tensor. +// i.e. `exp(x)` or `e^(x)`, where `x` is the input tensor. +// `e` denotes Euler's number and is approximately equal to 2.718281. +// Output is positive for any real input. +// +// ```python +// x = tf.constant(2.0) +// tf.math.exp(x) ==> 7.389056 +// +// x = tf.constant([2.0, 8.0]) +// tf.math.exp(x) ==> array([7.389056, 2980.958], dtype=float32) +// ``` +// +// For complex numbers, the exponential value is calculated as follows: +// +// ``` +// e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y) +// ``` +// +// Let's consider complex number 1+1j as an example. +// e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j) +// +// ```python +// x = tf.constant(1 + 1j) +// tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j +// ``` +func Exp(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Exp", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes inverse hyperbolic sine of x element-wise. +func Asinh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Asinh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AddV2", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adjust the hue of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last dimension is +// interpretted as channels, and must be three. +// +// The input image is considered in the RGB colorspace. Conceptually, the RGB +// colors are first mapped into HSV. A delta is then applied all the hue values, +// and then remapped back to RGB colorspace. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// delta: A float delta to add to the hue. +// +// Returns The hue-adjusted image or images. +func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustHue", + Input: []tf.Input{ + images, delta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the derivative of a Gamma random sample w.r.t. `alpha`. +func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RandomGammaGrad", + Input: []tf.Input{ + alpha, sample, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MultiDeviceIteratorFromStringHandleAttr is an optional argument to MultiDeviceIteratorFromStringHandle. +type MultiDeviceIteratorFromStringHandleAttr func(optionalAttr) + +// MultiDeviceIteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. +// +// value: The type list for the return values. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func MultiDeviceIteratorFromStringHandleOutputTypes(value []tf.DataType) MultiDeviceIteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_types"] = value + } +} + +// MultiDeviceIteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. +// +// value: The list of shapes being produced. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func MultiDeviceIteratorFromStringHandleOutputShapes(value []tf.Shape) MultiDeviceIteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_shapes"] = value + } +} + +// Generates a MultiDeviceIterator resource from its provided string handle. +// +// Arguments: +// string_handle: String representing the resource. +// +// Returns A MultiDeviceIterator resource. +func MultiDeviceIteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...MultiDeviceIteratorFromStringHandleAttr) (multi_device_iterator tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorFromStringHandle", + Input: []tf.Input{ + string_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the value stored in an Optional variant or raises an error if none exists. +func OptionalGetValue(scope *Scope, optional tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "OptionalGetValue", + Input: []tf.Input{ + optional, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("OptionalGetValue", err) + return + } + return components +} + +// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2. +type PaddingFIFOQueueV2Attr func(optionalAttr) + +// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value. +// +// value: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. +// Shapes of fixed rank but variable size are allowed by setting +// any shape dimension to -1. In this case, the inputs' shape may vary along +// the given dimension, and DequeueMany will pad the given dimension with +// zeros up to the maximum shape of all elements in the given batch. +// If the length of this attr is 0, different queue elements may have +// different ranks and shapes, but only one element may be dequeued at a time. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["shapes"] = value + } +} + +// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// PaddingFIFOQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that produces elements in first-in first-out order. +// +// Variable-size shapes are allowed by setting the corresponding shape dimensions +// to 0 in the shape attr. In this case DequeueMany will pad up to the maximum +// size of any given element in the minibatch. See below for details. +// +// Arguments: +// component_types: The type of each component in a value. +// +// Returns The handle to the queue. +func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PaddingFIFOQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingFTRLParametersAttr is an optional argument to LoadTPUEmbeddingFTRLParameters. +type LoadTPUEmbeddingFTRLParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingFTRLParametersTableId(value int64) LoadTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingFTRLParametersTableName(value string) LoadTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load FTRL embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the FTRL optimization algorithm. +// accumulators: Value of accumulators used in the FTRL optimization algorithm. +// linears: Value of linears used in the FTRL optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingFTRLParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingFTRLParameters", + Input: []tf.Input{ + parameters, accumulators, linears, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes numerical negative value element-wise. +// +// I.e., \\(y = -x\\). +func Neg(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Neg", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OneHotAttr is an optional argument to OneHot. +type OneHotAttr func(optionalAttr) + +// OneHotAxis sets the optional axis attribute to value. +// +// value: The axis to fill (default: -1, a new inner-most axis). +// If not specified, defaults to -1 +func OneHotAxis(value int64) OneHotAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Returns a one-hot tensor. +// +// The locations represented by indices in `indices` take value `on_value`, +// while all other locations take value `off_value`. +// +// If the input `indices` is rank `N`, the output will have rank `N+1`, +// The new axis is created at dimension `axis` (default: the new axis is +// appended at the end). +// +// If `indices` is a scalar the output shape will be a vector of length `depth`. +// +// If `indices` is a vector of length `features`, the output shape will be: +// ``` +// features x depth if axis == -1 +// depth x features if axis == 0 +// ``` +// +// If `indices` is a matrix (batch) with shape `[batch, features]`, +// the output shape will be: +// ``` +// batch x features x depth if axis == -1 +// batch x depth x features if axis == 1 +// depth x batch x features if axis == 0 +// ``` +// +// +// Examples +// ========= +// +// Suppose that +// ``` +// indices = [0, 2, -1, 1] +// depth = 3 +// on_value = 5.0 +// off_value = 0.0 +// axis = -1 +// ``` +// +// Then output is `[4 x 3]`: +// ``` +// output = +// [5.0 0.0 0.0] // one_hot(0) +// [0.0 0.0 5.0] // one_hot(2) +// [0.0 0.0 0.0] // one_hot(-1) +// [0.0 5.0 0.0] // one_hot(1) +// ``` +// +// Suppose that +// ``` +// indices = [0, 2, -1, 1] +// depth = 3 +// on_value = 0.0 +// off_value = 3.0 +// axis = 0 +// ``` +// +// Then output is `[3 x 4]`: +// ``` +// output = +// [0.0 3.0 3.0 3.0] +// [3.0 3.0 3.0 0.0] +// [3.0 3.0 3.0 3.0] +// [3.0 0.0 3.0 3.0] +// // ^ one_hot(0) +// // ^ one_hot(2) +// // ^ one_hot(-1) +// // ^ one_hot(1) +// ``` +// +// Suppose that +// ``` +// indices = [[0, 2], [1, -1]] +// depth = 3 +// on_value = 1.0 +// off_value = 0.0 +// axis = -1 +// ``` +// +// Then output is `[2 x 2 x 3]`: +// ``` +// output = +// [ +// [1.0, 0.0, 0.0] // one_hot(0) +// [0.0, 0.0, 1.0] // one_hot(2) +// ][ +// [0.0, 1.0, 0.0] // one_hot(1) +// [0.0, 0.0, 0.0] // one_hot(-1) +// ] +// ``` +// +// Arguments: +// indices: A tensor of indices. +// depth: A scalar defining the depth of the one hot dimension. +// on_value: A scalar defining the value to fill in output when `indices[j] = i`. +// off_value: A scalar defining the value to fill in output when `indices[j] != i`. +// +// Returns The one-hot tensor. +func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OneHot", + Input: []tf.Input{ + indices, depth, on_value, off_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput. +type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr) + +// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of depthwise convolution with respect to the input. +// +// Arguments: +// input_sizes: An integer vector representing the shape of `input`, based +// on `data_format`. For example, if `data_format` is 'NHWC' then +// `input` is a 4-D `[batch, height, width, channels]` tensor. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, depthwise_multiplier]`. +// out_backprop: 4-D with shape based on `data_format`. +// For example, if `data_format` is 'NHWC' then +// out_backprop shape is `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape according to `data_format`. For example, if +// `data_format` is 'NHWC', output shape is `[batch, in_height, +// in_width, in_channels]`. Gradient w.r.t. the input of the +// convolution. +func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNativeBackpropInput", + Input: []tf.Input{ + input_sizes, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Check if the input matches the regex pattern. +// +// The input is a string tensor of any shape. The pattern is the +// regular expression to be matched with every element of the input tensor. +// The boolean values (True or False) of the output tensor indicate +// if the input matches the regex pattern provided. +// +// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: A string tensor of the text to be processed. +// pattern: The regular expression to match the input. +// +// Returns A bool tensor with the same shape as `input`. +func StaticRegexFullMatch(scope *Scope, input tf.Output, pattern string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pattern": pattern} + opspec := tf.OpSpec{ + Type: "StaticRegexFullMatch", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Performs a padding as a preprocess during a convolution. +// +// Similar to FusedResizeAndPadConv2d, this op allows for an optimized +// implementation where the spatial padding transformation stage is fused with the +// im2col lookup, but in this case without the bilinear filtering required for +// resizing. Fusing the padding prevents the need to write out the intermediate +// results as whole tensors, reducing memory pressure, and we can get some latency +// gains by merging the transformation calculations. +// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' +// order is used instead. +// Internally this op uses a single per-graph scratch buffer, which means that it +// will block if multiple versions are being run in parallel. This is because this +// operator is primarily an optimization to minimize memory usage. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. Must be in the same order as the dimension specified with format. +// padding: The type of padding algorithm to use. +func FusedPadConv2D(scope *Scope, input tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "FusedPadConv2D", + Input: []tf.Input{ + input, paddings, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. +type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) + +// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// prox_v = var +// prox_v -= lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. +type ResourceSparseApplyRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, ms, and mom tensors is protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the RMSProp algorithm. +// +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. +// +// Returns the created operation. +func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyRMSProp", + Input: []tf.Input{ + var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. +type SparseToSparseSetOperationAttr func(optionalAttr) + +// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of 2 `SparseTensor` inputs. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the +// order and range of `set1` and `set2` indices. +// +// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, +// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same +// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set1` +// and `set2` indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must +// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the +// max set size across `0...n-1` dimensions. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the +// max set size across `0...n-1` dimensions. +// +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToSparseSetOperation", + Input: []tf.Input{ + set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update '*var' according to the adagrad scheme. +// +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // LeakyReluGradAttr is an optional argument to LeakyReluGrad. type LeakyReluGradAttr func(optionalAttr) @@ -11834,83 +21785,40 @@ func LeakyReluGrad(scope *Scope, gradients tf.Output, features tf.Output, option return op.Output(0) } -// Encodes a `RaggedTensor` into a `variant` Tensor. -// -// -// Encodes the given `RaggedTensor` and returns a `variant` Tensor. If -// `batched_input` is True, then input `RaggedTensor` is unbatched along the -// zero-th dimension, each component `RaggedTensor` is encoded into a scalar -// `variant` Tensor, and these are stacked to return a 1-D `variant` Tensor. -// If `batched_input` is False, then the input `RaggedTensor` is encoded as is and -// a scalar `variant` Tensor is returned. A `RaggedTensor` is encoded by first -// creating a 1-D `variant` Tensor with `ragged_rank + 1` elements, containing the -// splits and values Tensors of the `RaggedTensor`. Then the 1-D `variant` Tensor -// is wrapped in a scalar `variant` Tensor. See `RaggedTensorFromVariant` for the -// corresponding decoding logic. -// -// -// Arguments: -// rt_nested_splits: A list of one or more Tensors representing the splits of the input -// `RaggedTensor`. -// rt_dense_values: A Tensor representing the values of the input `RaggedTensor`. -// batched_input: A `bool` denoting whether the input is a batched `RaggedTensor`. -// -// Returns A `variant` Tensor that containing encoded `RaggedTensor`. -func RaggedTensorToVariant(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output, batched_input bool) (encoded_ragged tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"batched_input": batched_input} - opspec := tf.OpSpec{ - Type: "RaggedTensorToVariant", - Input: []tf.Input{ - tf.OutputList(rt_nested_splits), rt_dense_values, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// ResourceApplyAdaMaxAttr is an optional argument to ResourceApplyAdaMax. +type ResourceApplyAdaMaxAttr func(optionalAttr) -// RaggedRangeAttr is an optional argument to RaggedRange. -type RaggedRangeAttr func(optionalAttr) - -// RaggedRangeTsplits sets the optional Tsplits attribute to value. -// If not specified, defaults to DT_INT64 -func RaggedRangeTsplits(value tf.DataType) RaggedRangeAttr { +// ResourceApplyAdaMaxUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdaMaxUseLocking(value bool) ResourceApplyAdaMaxAttr { return func(m optionalAttr) { - m["Tsplits"] = value + m["use_locking"] = value } } -// Returns a `RaggedTensor` containing the specified sequences of numbers. +// Update '*var' according to the AdaMax algorithm. // -// -// Returns a `RaggedTensor` `result` composed from `rt_dense_values` and -// `rt_nested_splits`, such that -// `result[i] = range(starts[i], limits[i], deltas[i])`. -// -// ```python -// >>> (rt_nested_splits, rt_dense_values) = gen_ragged_ops.ragged_range( -// ... starts=[2, 5, 8], limits=[3, 5, 12], deltas=1) -// >>> result = ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) -// >>> print result.eval().tolist() -// [[2], # result[0] = range(2, 3) -// [], # result[1] = range(5, 5) -// [8, 9, 10, 11]] # result[2] = range(8, 12) -// ``` -// -// The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors. -// The vector inputs must all have the same size. Scalar inputs are broadcast -// to match the size of the vector inputs. +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// v_t <- max(beta2 * v_{t-1}, abs(g)) +// variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) // // Arguments: -// starts: The starts of each range. -// limits: The limits of each range. -// deltas: The deltas of each range. +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// beta1_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. // -// Returns The `row_splits` for the returned `RaggedTensor`.The `flat_values` for the returned `RaggedTensor`. -func RaggedRange(scope *Scope, starts tf.Output, limits tf.Output, deltas tf.Output, optional ...RaggedRangeAttr) (rt_nested_splits tf.Output, rt_dense_values tf.Output) { +// Returns the created operation. +func ResourceApplyAdaMax(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdaMaxAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -11919,46 +21827,208 @@ func RaggedRange(scope *Scope, starts tf.Output, limits tf.Output, deltas tf.Out a(attrs) } opspec := tf.OpSpec{ - Type: "RaggedRange", + Type: "ResourceApplyAdaMax", Input: []tf.Input{ - starts, limits, deltas, + var_, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return scope.AddOperation(opspec) } -// RetrieveTPUEmbeddingAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingAdagradParameters. -type RetrieveTPUEmbeddingAdagradParametersAttr func(optionalAttr) +// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent. +type ResourceApplyGradientDescentAttr func(optionalAttr) -// RetrieveTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. +// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' by subtracting 'alpha' * 'delta' from it. +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// delta: The change. +// +// Returns the created operation. +func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyGradientDescent", + Input: []tf.Input{ + var_, alpha, delta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceApplyAdamWithAmsgradAttr is an optional argument to ResourceApplyAdamWithAmsgrad. +type ResourceApplyAdamWithAmsgradAttr func(optionalAttr) + +// ResourceApplyAdamWithAmsgradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdamWithAmsgradUseLocking(value bool) ResourceApplyAdamWithAmsgradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the Adam algorithm. +// +// $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ +// $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ +// $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ +// $$vhat_t := max{vhat_{t-1}, v_t}$$ +// $$variable := variable - lr_t * m_t / (\sqrt{vhat_t} + \epsilon)$$ +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// vhat: Should be from a Variable(). +// beta1_power: Must be a scalar. +// beta2_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdamWithAmsgrad(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, vhat tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamWithAmsgradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdamWithAmsgrad", + Input: []tf.Input{ + var_, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns a list list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`. +// +// tensor: The tensor to put on the list. +// input_handle: The old list. +// output_handle: A list with the elements of the old list followed by tensor. +// element_dtype: the type of elements in the list. +// element_shape: a shape compatible with that of elements in the list. +func TensorListPushBack(scope *Scope, input_handle tf.Output, tensor tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListPushBack", + Input: []tf.Input{ + input_handle, tensor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// InfeedEnqueuePrelinearizedBufferAttr is an optional argument to InfeedEnqueuePrelinearizedBuffer. +type InfeedEnqueuePrelinearizedBufferAttr func(optionalAttr) + +// InfeedEnqueuePrelinearizedBufferDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op is running on a TPU device +// and = 0 when the Op is running on the CPU device. +// If not specified, defaults to -1 +func InfeedEnqueuePrelinearizedBufferDeviceOrdinal(value int64) InfeedEnqueuePrelinearizedBufferAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// An op which enqueues prelinearized buffer into TPU infeed. +// +// Arguments: +// input: A variant tensor representing linearized output. +// +// Returns the created operation. +func InfeedEnqueuePrelinearizedBuffer(scope *Scope, input tf.Output, optional ...InfeedEnqueuePrelinearizedBufferAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueuePrelinearizedBuffer", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingFTRLParametersGradAccumDebug. +type LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. // If not specified, defaults to -1 // // REQUIRES: value >= -1 -func RetrieveTPUEmbeddingAdagradParametersTableId(value int64) RetrieveTPUEmbeddingAdagradParametersAttr { +func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { return func(m optionalAttr) { m["table_id"] = value } } -// RetrieveTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. +// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. // If not specified, defaults to "" -func RetrieveTPUEmbeddingAdagradParametersTableName(value string) RetrieveTPUEmbeddingAdagradParametersAttr { +func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { return func(m optionalAttr) { m["table_name"] = value } } -// Retrieve Adagrad embedding parameters. +// Load FTRL embedding parameters with debug support. // -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. // -// Returns Parameter parameters updated by the Adagrad optimization algorithm.Parameter accumulators updated by the Adagrad optimization algorithm. -func RetrieveTPUEmbeddingAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { +// Arguments: +// parameters: Value of parameters used in the FTRL optimization algorithm. +// accumulators: Value of accumulators used in the FTRL optimization algorithm. +// linears: Value of linears used in the FTRL optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the FTRL optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -11967,59 +22037,272 @@ func RetrieveTPUEmbeddingAdagradParameters(scope *Scope, num_shards int64, shard a(attrs) } opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingAdagradParameters", + Type: "LoadTPUEmbeddingFTRLParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, linears, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Compute the regularized incomplete beta integral \\(I_x(a, b)\\). +// +// The regularized incomplete beta integral is defined as: +// +// +// \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\) +// +// where +// +// +// \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\) +// +// +// is the incomplete beta function and \\(B(a, b)\\) is the *complete* +// beta function. +func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Betainc", + Input: []tf.Input{ + a, b, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along sparse segments of a tensor. +// +// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is +// misisng, the `output` tensor at that position will be zeroed. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/sparse#Segmentation) +// for an explanation of segments. +// +// For example: +// +// ```python +// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) +// +// tf.sparse_segment_sum_with_num_segments( +// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3) +// # => [[0 0 0 0] +// # [0 0 0 0] +// # [0 0 0 0]] +// +// tf.sparse_segment_sum_with_num_segments(c, +// tf.constant([0, 1]), +// tf.constant([0, 2], +// num_segments=4)) +// # => [[ 1 2 3 4] +// # [ 0 0 0 0] +// # [-1 -2 -3 -4] +// # [ 0 0 0 0]] +// ``` +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `num_segments`. +func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSumWithNumSegments", + Input: []tf.Input{ + data, indices, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AudioSpectrogramAttr is an optional argument to AudioSpectrogram. +type AudioSpectrogramAttr func(optionalAttr) + +// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value. +// +// value: Whether to return the squared magnitude or just the +// magnitude. Using squared magnitude can avoid extra calculations. +// If not specified, defaults to false +func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr { + return func(m optionalAttr) { + m["magnitude_squared"] = value + } +} + +// Produces a visualization of audio data over time. +// +// Spectrograms are a standard way of representing audio information as a series of +// slices of frequency information, one slice for each window of time. By joining +// these together into a sequence, they form a distinctive fingerprint of the sound +// over time. +// +// This op expects to receive audio data as an input, stored as floats in the range +// -1 to 1, together with a window width in samples, and a stride specifying how +// far to move the window between slices. From this it generates a three +// dimensional output. The first dimension is for the channels in the input, so a +// stereo audio input would have two here for example. The second dimension is time, +// with successive frequency slices. The third dimension has an amplitude value for +// each frequency during that time slice. +// +// This means the layout when converted and saved as an image is rotated 90 degrees +// clockwise from a typical spectrogram. Time is descending down the Y axis, and +// the frequency decreases from left to right. +// +// Each value in the result represents the square root of the sum of the real and +// imaginary parts of an FFT on the current window of samples. In this way, the +// lowest dimension represents the power of each frequency in the current window, +// and adjacent windows are concatenated in the next dimension. +// +// To get a more intuitive and visual look at what this operation does, you can run +// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the +// resulting spectrogram as a PNG image. +// +// Arguments: +// input: Float representation of audio data. +// window_size: How wide the input window is in samples. For the highest efficiency +// this should be a power of two, but other values are accepted. +// stride: How widely apart the center of adjacent sample windows should be. +// +// Returns 3D representation of the audio frequencies as an image. +func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"window_size": window_size, "stride": stride} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSpectrogram", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Component-wise divides a SparseTensor by a dense Tensor. +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseDiv", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An op that receives embedding activations on the TPU. +// +// The TPU system performs the embedding lookups and aggregations specified by +// the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The +// results of these aggregations are visible to the Tensorflow Graph as the +// outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing +// one Tensor of activations per table specified in the model. There can be at +// most one RecvTPUEmbeddingActivations op in the TPU graph. +// +// Arguments: +// num_outputs: The number of output activation tensors, equal to the number of +// embedding tables in the model. +// config: Serialized TPUEmbeddingConfiguration proto. +// +// Returns A TensorList of embedding activations containing one Tensor per +// embedding table in the model. +func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_outputs": num_outputs, "config": config} + opspec := tf.OpSpec{ + Type: "RecvTPUEmbeddingActivations", Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RecvTPUEmbeddingActivations", err) + return + } + return outputs } -// DecodeWavAttr is an optional argument to DecodeWav. -type DecodeWavAttr func(optionalAttr) +// RandomUniformIntAttr is an optional argument to RandomUniformInt. +type RandomUniformIntAttr func(optionalAttr) -// DecodeWavDesiredChannels sets the optional desired_channels attribute to value. +// RandomUniformIntSeed sets the optional seed attribute to value. // -// value: Number of sample channels wanted. -// If not specified, defaults to -1 -func DecodeWavDesiredChannels(value int64) DecodeWavAttr { +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomUniformIntSeed(value int64) RandomUniformIntAttr { return func(m optionalAttr) { - m["desired_channels"] = value + m["seed"] = value } } -// DecodeWavDesiredSamples sets the optional desired_samples attribute to value. +// RandomUniformIntSeed2 sets the optional seed2 attribute to value. // -// value: Length of audio requested. -// If not specified, defaults to -1 -func DecodeWavDesiredSamples(value int64) DecodeWavAttr { +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomUniformIntSeed2(value int64) RandomUniformIntAttr { return func(m optionalAttr) { - m["desired_samples"] = value + m["seed2"] = value } } -// Decode a 16-bit PCM WAV file to a float tensor. +// Outputs random integers from a uniform distribution. // -// The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float. +// The generated values are uniform integers in the range `[minval, maxval)`. +// The lower bound `minval` is included in the range, while the upper bound +// `maxval` is excluded. // -// When desired_channels is set, if the input contains fewer channels than this -// then the last channel will be duplicated to give the requested number, else if -// the input has more channels than requested then the additional channels will be -// ignored. -// -// If desired_samples is set, then the audio will be cropped or padded with zeroes -// to the requested length. -// -// The first output contains a Tensor with the content of the audio samples. The -// lowest dimension will be the number of channels, and the second will be the -// number of samples. For example, a ten-sample-long stereo WAV file should give an -// output shape of [10, 2]. +// The random integers are slightly biased unless `maxval - minval` is an exact +// power of two. The bias is small for values of `maxval - minval` significantly +// smaller than the range of the output (either `2^32` or `2^64`). // // Arguments: -// contents: The WAV-encoded audio, usually from a file. +// shape: The shape of the output tensor. +// minval: 0-D. Inclusive lower bound on the generated integers. +// maxval: 0-D. Exclusive upper bound on the generated integers. // -// Returns 2-D with shape `[length, channels]`.Scalar holding the sample rate found in the WAV header. -func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (audio tf.Output, sample_rate tf.Output) { +// Returns A tensor of the specified shape filled with uniform random integers. +func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -12028,9 +22311,1035 @@ func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (aud a(attrs) } opspec := tf.OpSpec{ - Type: "DecodeWav", + Type: "RandomUniformInt", Input: []tf.Input{ - contents, + shape, minval, maxval, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Copy a tensor setting everything outside a central band in each innermost matrix +// +// to zero. +// +// The `band` part is computed as follows: +// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a +// tensor with the same shape where +// +// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. +// +// The indicator function +// +// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && +// (num_upper < 0 || (n-m) <= num_upper)`. +// +// For example: +// +// ``` +// # if 'input' is [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [-2, -1, 0, 1] +// [-3, -2, -1, 0]], +// +// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [ 0, -1, 0, 1] +// [ 0, 0, -1, 0]], +// +// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] +// [-1, 0, 1, 0] +// [-2, -1, 0, 1] +// [ 0, -2, -1, 0]] +// ``` +// +// Useful special cases: +// +// ``` +// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. +// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. +// tf.matrix_band_part(input, 0, 0) ==> Diagonal. +// ``` +// +// Arguments: +// input: Rank `k` tensor. +// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire +// lower triangle. +// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep +// entire upper triangle. +// +// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor. +func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixBandPart", + Input: []tf.Input{ + input, num_lower, num_upper, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SdcaOptimizerAttr is an optional argument to SdcaOptimizer. +type SdcaOptimizerAttr func(optionalAttr) + +// SdcaOptimizerAdaptative sets the optional adaptative attribute to value. +// +// value: Whether to use Adaptive SDCA for the inner loop. +// If not specified, defaults to true +func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr { + return func(m optionalAttr) { + m["adaptative"] = value + } +} + +// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for +// +// linear models with L1 + L2 regularization. As global optimization objective is +// strongly-convex, the optimizer optimizes the dual objective at each step. The +// optimizer applies each update one example at a time. Examples are sampled +// uniformly, and the optimizer is learning rate free and enjoys linear convergence +// rate. +// +// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
+// Shai Shalev-Shwartz, Tong Zhang. 2012 +// +// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ +// +// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, +// Peter Richtarik, Martin Takac. 2015 +// +// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 +// +// Arguments: +// sparse_example_indices: a list of vectors which contain example indices. +// sparse_feature_indices: a list of vectors which contain feature indices. +// sparse_feature_values: a list of vectors which contains feature value +// associated with each feature group. +// dense_features: a list of matrices which contains the dense feature values. +// example_weights: a vector which contains the weight associated with each +// example. +// example_labels: a vector which contains the label/target associated with each +// example. +// sparse_indices: a list of vectors where each value is the indices which has +// corresponding weights in sparse_weights. This field maybe omitted for the +// dense approach. +// sparse_weights: a list of vectors where each value is the weight associated with +// a sparse feature group. +// dense_weights: a list of vectors where the values are the weights associated +// with a dense feature group. +// example_state_data: a list of vectors containing the example state data. +// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, +// squared and hinge losses. +// l1: Symmetric l1 regularization strength. +// l2: Symmetric l2 regularization strength. +// num_loss_partitions: Number of partitions of the global loss function. +// num_inner_iterations: Number of iterations per mini-batch. +// +// Returns a list of vectors containing the updated example state +// data.a list of vectors where each value is the delta +// weights associated with a sparse feature group.a list of vectors where the values are the delta +// weights associated with a dense feature group. +func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SdcaOptimizer", + Input: []tf.Input{ + tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + out_example_state_data = op.Output(idx) + if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return + } + if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return + } + return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights +} + +// Eagerly executes a python function to compute func(input)->output. The +// +// semantics of the input, output, and attributes are the same as those for +// PyFunc. +func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"token": token, "Tout": Tout} + opspec := tf.OpSpec{ + Type: "EagerPyFunc", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("EagerPyFunc", err) + return + } + return output +} + +// UnicodeTranscodeAttr is an optional argument to UnicodeTranscode. +type UnicodeTranscodeAttr func(optionalAttr) + +// UnicodeTranscodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeTranscodeErrors(value string) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeTranscodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD or U+65533.) +// +// Note that for UTF-8, passing a replacement character expressible in 1 byte, such +// as ' ', will preserve string alignment to the source since invalid bytes will be +// replaced with a 1-byte replacement. For UTF-16-BE and UTF-16-LE, any 1 or 2 byte +// replacement character will preserve byte alignment to the source. +// If not specified, defaults to 65533 +func UnicodeTranscodeReplacementChar(value int64) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// UnicodeTranscodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. +// +// value: Whether to replace the C0 control characters (00-1F) with the +// `replacement_char`. Default is false. +// If not specified, defaults to false +func UnicodeTranscodeReplaceControlCharacters(value bool) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["replace_control_characters"] = value + } +} + +// Transcode the input text from a source encoding to a destination encoding. +// +// The input is a string tensor of any shape. The output is a string tensor of +// the same shape containing the transcoded strings. Output strings are always +// valid unicode. If the input contains invalid encoding positions, the +// `errors` attribute sets the policy for how to deal with them. If the default +// error-handling policy is used, invalid formatting will be substituted in the +// output by the `replacement_char`. If the errors policy is to `ignore`, any +// invalid encoding positions in the input are skipped and not included in the +// output. If it set to `strict` then any invalid formatting will result in an +// InvalidArgument error. +// +// This operation can be used with `output_encoding = input_encoding` to enforce +// correct formatting for inputs even if they are already in the desired encoding. +// +// If the input is prefixed by a Byte Order Mark needed to determine encoding +// (e.g. if the encoding is UTF-16 and the BOM indicates big-endian), then that +// BOM will be consumed and not emitted into the output. If the input encoding +// is marked with an explicit endianness (e.g. UTF-16-BE), then the BOM is +// interpreted as a non-breaking-space and is preserved in the output (including +// always for UTF-8). +// +// The end result is that if the input is marked as an explicit endianness the +// transcoding is faithful to all codepoints in the source. If it is not marked +// with an explicit endianness, the BOM is not considered part of the string itself +// but as metadata, and so is not preserved in the output. +// +// Arguments: +// input: The text to be processed. Can have any shape. +// input_encoding: Text encoding of the input strings. This is any of the encodings supported +// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. +// output_encoding: The unicode encoding to use in the output. Must be one of +// `"UTF-8", "UTF-16-BE", "UTF-32-BE"`. Multi-byte encodings will be big-endian. +// +// Returns A string tensor containing unicode text encoded using `output_encoding`. +func UnicodeTranscode(scope *Scope, input tf.Output, input_encoding string, output_encoding string, optional ...UnicodeTranscodeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_encoding": input_encoding, "output_encoding": output_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeTranscode", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad. +type MaxPoolGradGradAttr func(optionalAttr) + +// MaxPoolGradGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes rectified linear 6: `min(max(features, 0), 6)`. +func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu6", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayCloseV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayCloseV3 +// +// Returns the created operation. +func TensorArrayCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayCloseV2", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// Creates a dataset that skips `count` elements from the `input_dataset`. +// +// Arguments: +// +// count: A scalar representing the number of elements from the `input_dataset` +// that should be skipped. If count is -1, skips everything. +// +// +func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SkipDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. +type QuantizedRelu6Attr func(optionalAttr) + +// QuantizedRelu6OutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` +// +// Arguments: +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. +func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedRelu6", + Input: []tf.Input{ + features, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Strip leading and trailing whitespaces from the Tensor. +// +// Arguments: +// input: A string `Tensor` of any shape. +// +// Returns A string `Tensor` of the same shape as the input. +func StringStrip(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StringStrip", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingSparseTensorBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseTensorBatch. +type EnqueueTPUEmbeddingSparseTensorBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// EnqueueTPUEmbeddingSparseTensorBatchCombiners sets the optional combiners attribute to value. +// +// value: A list of string scalars, one for each embedding table that specify +// how to normalize the embedding activations after weighted summation. +// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have +// the sum of the weights be 0 for 'mean' or the sum of the squared weights be +// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for +// all tables. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseTensorBatchCombiners(value []string) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["combiners"] = value + } +} + +// EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths sets the optional max_sequence_lengths attribute to value. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths(value []int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["max_sequence_lengths"] = value + } +} + +// Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). +// +// sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond +// to the ith feature. table_ids[i] indicates which embedding table to look up ith +// feature. +// +// The tensors at corresponding positions in the three input lists (sample_indices, +// embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 +// with dim_size() equal to the total number of lookups into the table described by +// the corresponding feature. +// +// Arguments: +// sample_indices: A list of rank 1 Tensors specifying the training example to +// which the corresponding embedding_indices and aggregation_weights values +// belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse(). +// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. +// It corresponds to sp_ids.values in embedding_lookup_sparse(). +// aggregation_weights: A list of rank 1 Tensors containing per training example +// aggregation weights. It corresponds to sp_weights.values in +// embedding_lookup_sparse(). +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// table_ids: A list of integers specifying the identifier of the embedding table +// (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the +// corresponding input. The ith input is looked up using table_ids[i]. The size +// of the table_ids list must be equal to that of sample_indices, +// embedding_indices and aggregation_weights. +// +// Returns the created operation. +func EnqueueTPUEmbeddingSparseTensorBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, table_ids []int64, optional ...EnqueueTPUEmbeddingSparseTensorBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"table_ids": table_ids} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingSparseTensorBatch", + Input: []tf.Input{ + tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Output a fact about factorials. +func Fact(scope *Scope) (fact tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Fact", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that splits a SparseTensor into elements row-wise. +func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorSliceDataset", + Input: []tf.Input{ + indices, values, dense_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TextLineReaderV2Attr is an optional argument to TextLineReaderV2. +type TextLineReaderV2Attr func(optionalAttr) + +// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value. +// +// value: Number of lines to skip from the beginning of every file. +// If not specified, defaults to 0 +func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["skip_header_lines"] = value + } +} + +// TextLineReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func TextLineReaderV2Container(value string) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TextLineReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A Reader that outputs the lines of a file delimited by '\n'. +// +// Returns The handle to reference the Reader. +func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TextLineReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Return a tensor with the same shape and contents as the input tensor or value. +func Identity(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Identity", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softmax cross entropy cost and gradients to backpropagate. +// +// Inputs are the logits, not probabilities. +// +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size x num_classes matrix +// The caller must ensure that each batch of labels represents a valid +// probability distribution. +// +// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). +func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D. +type FusedResizeAndPadConv2DAttr func(optionalAttr) + +// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr { + return func(m optionalAttr) { + m["resize_align_corners"] = value + } +} + +// Performs a resize and padding as a preprocess during a convolution. +// +// It's often possible to do spatial transformations more efficiently as part of +// the packing stage of a convolution, so this op allows for an optimized +// implementation where these stages are fused together. This prevents the need to +// write out the intermediate results as whole tensors, reducing memory pressure, +// and we can get some latency gains by merging the transformation calculations. +// The data_format attribute for Conv2D isn't supported by this op, and defaults to +// 'NHWC' order. +// Internally this op uses a single per-graph scratch buffer, which means that it +// will block if multiple versions are being run in parallel. This is because this +// operator is primarily an optimization to minimize memory usage. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. Must be in the same order as the dimension specified with format. +// padding: The type of padding algorithm to use. +func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedResizeAndPadConv2D", + Input: []tf.Input{ + input, size, paddings, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Splits a tensor into a list. +// +// list[i] corresponds to lengths[i] tensors from the input tensor. +// The tensor must have rank at least 1 and contain exactly sum(lengths) elements. +// +// tensor: The input tensor. +// element_shape: A shape compatible with that of elements in the tensor. +// lengths: Vector of sizes of the 0th dimension of tensors in the list. +// output_handle: The list. +func TensorListSplit(scope *Scope, tensor tf.Output, element_shape tf.Output, lengths tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListSplit", + Input: []tf.Input{ + tensor, element_shape, lengths, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gather ragged slices from `params` axis `0` according to `indices`. +// +// Outputs a `RaggedTensor` output composed from `output_dense_values` and +// `output_nested_splits`, such that: +// +// ```python +// output.shape = indices.shape + params.shape[1:] +// output.ragged_rank = indices.shape.ndims + params.ragged_rank +// output[i...j, d0...dn] = params[indices[i...j], d0...dn] +// ``` +// +// where +// +// * `params = +// ragged.from_nested_row_splits(params_dense_values, params_nested_splits)` +// provides the values that should be gathered. +// * `indices` ia a dense tensor with dtype `int32` or `int64`, indicating which +// values should be gathered. +// * `output = +// ragged.from_nested_row_splits(output_dense_values, output_nested_splits)` +// is the output tensor. +// +// (Note: This c++ op is used to implement the higher-level python +// `tf.ragged.gather` op, which also supports ragged indices.) +// +// +// Arguments: +// params_nested_splits: The `nested_row_splits` tensors that define the row-partitioning for the +// `params` RaggedTensor input. +// params_dense_values: The `flat_values` for the `params` RaggedTensor. There was a terminology change +// at the python level from dense_values to flat_values, so dense_values is the +// deprecated name. +// indices: Indices in the outermost dimension of `params` of the values that should be +// gathered. +// OUTPUT_RAGGED_RANK: The ragged rank of the output RaggedTensor. `output_nested_splits` will contain +// this number of `row_splits` tensors. This value should equal +// `indices.shape.ndims + params.ragged_rank - 1`. +// +// Returns The `nested_row_splits` tensors that define the row-partitioning for the +// returned RaggedTensor.The `flat_values` for the returned RaggedTensor. +func RaggedGather(scope *Scope, params_nested_splits []tf.Output, params_dense_values tf.Output, indices tf.Output, OUTPUT_RAGGED_RANK int64) (output_nested_splits []tf.Output, output_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"OUTPUT_RAGGED_RANK": OUTPUT_RAGGED_RANK} + opspec := tf.OpSpec{ + Type: "RaggedGather", + Input: []tf.Input{ + tf.OutputList(params_nested_splits), params_dense_values, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { + scope.UpdateErr("RaggedGather", err) + return + } + output_dense_values = op.Output(idx) + return output_nested_splits, output_dense_values +} + +// Creates a dataset that overrides the maximum intra-op parallelism. +// +// Arguments: +// +// max_intra_op_parallelism: Identifies the maximum intra-op parallelism to use. +// +// +func ExperimentalMaxIntraOpParallelismDataset(scope *Scope, input_dataset tf.Output, max_intra_op_parallelism tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalMaxIntraOpParallelismDataset", + Input: []tf.Input{ + input_dataset, max_intra_op_parallelism, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdadeltaParametersGradAccumDebug. +type LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Adadelta parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adadelta optimization algorithm. +// accumulators: Value of accumulators used in the Adadelta optimization algorithm. +// updates: Value of updates used in the Adadelta optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Adadelta optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, updates, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Reorders a SparseTensor into the canonical, row-major ordering. +// +// Note that by convention, all sparse ops preserve the canonical ordering along +// increasing dimension number. The only time ordering can be violated is during +// manual manipulation of the indices and values vectors to add entries. +// +// Reordering does not affect the shape of the SparseTensor. +// +// If the tensor has rank `R` and `N` non-empty values, `input_indices` has +// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// +// Returns 2-D. `N x R` matrix with the same indices as input_indices, but +// in canonical row-major ordering.1-D. `N` non-empty values corresponding to `output_indices`. +func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseReorder", + Input: []tf.Input{ + input_indices, input_values, input_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. +// If not specified, defaults to DT_INT64 +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["Targmax"] = value + } +} + +// MaxPoolWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. +// +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolWithArgmaxIncludeBatchInIndex(value bool) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["include_batch_in_index"] = value + } +} + +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index: +// `(y * width + x) * channels + c` if `include_batch_in_index` is False; +// `((b * height + y) * width + x) * channels + c` if `include_batch_in_index` is True. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolWithArgmax", + Input: []tf.Input{ + input, }, Attrs: attrs, } @@ -12038,6 +23347,1135 @@ func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (aud return op.Output(0), op.Output(1) } +// 3D real-valued fast Fourier transform. +// +// Computes the 3-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 3 dimensions of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. +// +// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the their 3D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfftn with 3 dimensions. +// @end_compatibility +func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RFFT3D", + Input: []tf.Input{ + input, fft_length, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolV2Attr is an optional argument to MaxPoolV2. +type MaxPoolV2Attr func(optionalAttr) + +// MaxPoolV2DataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolV2DataFormat(value string) MaxPoolV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPoolV2(scope *Scope, input tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolV2", + Input: []tf.Input{ + input, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset containing elements of first component of `input_dataset` having true in the last component. +func FilterByLastComponentDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "FilterByLastComponentDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. +type ResourceApplyProximalAdagradAttr func(optionalAttr) + +// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. +// +// accum += grad * grad +// prox_v = var - lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes sigmoid of `x` element-wise. +// +// Specifically, `y = 1 / (1 + exp(-x))`. +func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sigmoid", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010. +// +// Flip each bit of supported types. For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101. +// This operation is performed on each element of the tensor argument `x`. +func Invert(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Invert", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Checks whether a resource handle-based variable has been initialized. +// +// Arguments: +// resource: the input resource handle. +// +// Returns a scalar boolean which is true if the variable has been +// initialized. +func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "VarIsInitializedOp", + Input: []tf.Input{ + resource, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalParseExampleDatasetAttr is an optional argument to ExperimentalParseExampleDataset. +type ExperimentalParseExampleDatasetAttr func(optionalAttr) + +// ExperimentalParseExampleDatasetSloppy sets the optional sloppy attribute to value. +// If not specified, defaults to false +func ExperimentalParseExampleDatasetSloppy(value bool) ExperimentalParseExampleDatasetAttr { + return func(m optionalAttr) { + m["sloppy"] = value + } +} + +// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. +// +// Arguments: +// +// +// dense_defaults: A dict mapping string keys to `Tensor`s. +// The keys of the dict must match the dense_keys of the feature. +// sparse_keys: A list of string keys in the examples features. +// The results for these keys will be returned as `SparseTensor` objects. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples features associated with dense values. +// sparse_types: A list of `DTypes` of the same length as `sparse_keys`. +// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), +// and `tf.string` (`BytesList`) are supported. +// dense_shapes: List of tuples with the same length as `dense_keys`. +// The shape of the data for each dense feature referenced by `dense_keys`. +// Required for any input tensors identified by `dense_keys`. Must be +// either fully defined, or may contain an unknown first dimension. +// An unknown first dimension means the feature is treated as having +// a variable number of blocks, and the output shape along this dimension +// is considered unknown at graph build time. Padding is applied for +// minibatch elements smaller than the maximum number of blocks for the +// given feature along this dimension. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +func ExperimentalParseExampleDataset(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ExperimentalParseExampleDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalParseExampleDataset", + Input: []tf.Input{ + input_dataset, num_parallel_calls, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve. +type MatrixTriangularSolveAttr func(optionalAttr) + +// MatrixTriangularSolveLower sets the optional lower attribute to value. +// +// value: Boolean indicating whether the innermost matrices in `matrix` are +// lower or upper triangular. +// If not specified, defaults to true +func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { + return func(m optionalAttr) { + m["lower"] = value + } +} + +// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value. +// +// value: Boolean indicating whether to solve with `matrix` or its (block-wise) +// adjoint. +// +// @compatibility(numpy) +// Equivalent to scipy.linalg.solve_triangular +// @end_compatibility +// If not specified, defaults to false +func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. +// +// +// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form +// square matrices. If `lower` is `True` then the strictly upper triangular part +// of each inner-most matrix is assumed to be zero and not accessed. +// If `lower` is False then the strictly lower triangular part of each inner-most +// matrix is assumed to be zero and not accessed. +// `rhs` is a tensor of shape `[..., M, K]`. +// +// The output is a tensor of shape `[..., M, K]`. If `adjoint` is +// `True` then the innermost matrices in `output` satisfy matrix equations +// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. +// If `adjoint` is `False` then the strictly then the innermost matrices in +// `output` satisfy matrix equations +// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. +// +// Example: +// ```python +// +// a = tf.constant([[3, 0, 0, 0], +// [2, 1, 0, 0], +// [1, 0, 1, 0], +// [1, 1, 1, 1]], dtype=tf.float32) +// +// b = tf.constant([[4], +// [2], +// [4], +// [2]], dtype=tf.float32) +// +// x = tf.linalg.triangular_solve(a, b, lower=True) +// x +// # +// +// # in python3 one can use `a@x` +// tf.matmul(a, x) +// # +// ``` +// +// Arguments: +// matrix: Shape is `[..., M, M]`. +// rhs: Shape is `[..., M, K]`. +// +// Returns Shape is `[..., M, K]`. +func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixTriangularSolve", + Input: []tf.Input{ + matrix, rhs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts a `RaggedTensor` into a `SparseTensor` with the same values. +// +// input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) +// output=SparseTensor(indices=sparse_indices, values=sparse_values, +// dense_shape=sparse_dense_shape) +// +// Arguments: +// rt_nested_splits: The `row_splits` for the `RaggedTensor`. +// rt_dense_values: The `flat_values` for the `RaggedTensor`. +// +// Returns The indices for the `SparseTensor`.The values of the `SparseTensor`.`sparse_dense_shape` is a tight bounding box of the input `RaggedTensor`. +func RaggedTensorToSparse(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output) (sparse_indices tf.Output, sparse_values tf.Output, sparse_dense_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RaggedTensorToSparse", + Input: []tf.Input{ + tf.OutputList(rt_nested_splits), rt_dense_values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the index of a data point that should be added to the seed set. +// +// Entries in distances are assumed to be squared distances of candidate points to +// the already sampled centers in the seed set. The op constructs one Markov chain +// of the k-MC^2 algorithm and returns the index of one candidate point to be added +// as an additional cluster center. +// +// Arguments: +// distances: Vector with squared distances to the closest previously sampled cluster center +// for each candidate point. +// seed: Scalar. Seed for initializing the random number generator. +// +// Returns Scalar with the index of the sampled point. +func KMC2ChainInitialization(scope *Scope, distances tf.Output, seed tf.Output) (index tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "KMC2ChainInitialization", + Input: []tf.Input{ + distances, seed, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AudioSummaryAttr is an optional argument to AudioSummary. +type AudioSummaryAttr func(optionalAttr) + +// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value. +// +// value: Max number of batch elements to generate audio for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr { + return func(m optionalAttr) { + m["max_outputs"] = value + } +} + +// Outputs a `Summary` protocol buffer with audio. +// +// DEPRECATED at GraphDef version 15: Use AudioSummaryV2. +// +// The summary has up to `max_outputs` summary values containing audio. The +// audio is built from `tensor` which must be 3-D with shape `[batch_size, +// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are +// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. +// * If `max_outputs` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. +// +// Arguments: +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 2-D of shape `[batch_size, frames]`. +// sample_rate: The sample rate of the signal in hertz. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sample_rate": sample_rate} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSummary", + Input: []tf.Input{ + tag, tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug. +type RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve proximal Adagrad embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the proximal Adagrad optimization algorithm.Parameter accumulators updated by the proximal Adagrad optimization algorithm.Parameter gradient_accumulators updated by the proximal Adagrad optimization algorithm. +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ShardDatasetAttr is an optional argument to ShardDataset. +type ShardDatasetAttr func(optionalAttr) + +// ShardDatasetRequireNonEmpty sets the optional require_non_empty attribute to value. +// If not specified, defaults to false +func ShardDatasetRequireNonEmpty(value bool) ShardDatasetAttr { + return func(m optionalAttr) { + m["require_non_empty"] = value + } +} + +// Creates a `Dataset` that includes only 1/`num_shards` of this dataset. +// +// Arguments: +// +// num_shards: An integer representing the number of shards operating in parallel. +// index: An integer representing the current worker index. +// +// +func ShardDataset(scope *Scope, input_dataset tf.Output, num_shards tf.Output, index tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShardDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShardDataset", + Input: []tf.Input{ + input_dataset, num_shards, index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize. +type QuantizeAndDequantizeAttr func(optionalAttr) + +// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value. +// If not specified, defaults to true +func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["signed_input"] = value + } +} + +// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value. +// If not specified, defaults to false +func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["range_given"] = value + } +} + +// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value. +// If not specified, defaults to 0 +func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["input_min"] = value + } +} + +// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value. +// If not specified, defaults to 0 +func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["input_max"] = value + } +} + +// Use QuantizeAndDequantizeV2 instead. +// +// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2 +func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeAndDequantize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts sparse updates from the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] -= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] -= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions add. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterSub", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingFTRLParametersGradAccumDebug. +type RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve FTRL embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the FTRL optimization algorithm.Parameter accumulators updated by the FTRL optimization algorithm.Parameter linears updated by the FTRL optimization algorithm.Parameter gradient_accumulators updated by the FTRL optimization algorithm. +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Reduces `input` from `num_devices` using `reduction` to a single device. +// +// Reduces `input` from `num_devices` using `reduction` to a single device. +// +// The graph should be constructed so that all inputs have a valid device +// assignment, and the op itself is assigned one of these devices. +// +// input: The input to the reduction. +// data: the value of the reduction across all `num_devices` devices. +// reduction: the reduction operation to perform. +func NcclReduce(scope *Scope, input []tf.Output, reduction string) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"reduction": reduction} + opspec := tf.OpSpec{ + Type: "NcclReduce", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceScatterNdAddAttr is an optional argument to ResourceScatterNdAdd. +type ResourceScatterNdAddAttr func(optionalAttr) + +// ResourceScatterNdAddUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdAddUseLocking(value bool) ResourceScatterNdAddAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse addition to individual values or slices in a Variable. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] +// ``` +// +// For example, say we want to add 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that addition would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// add = tf.scatter_nd_add(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(add) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 13, 3, 14, 14, 6, 7, 20] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdAdd(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdAddAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdAdd", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// PrelinearizeTupleAttr is an optional argument to PrelinearizeTuple. +type PrelinearizeTupleAttr func(optionalAttr) + +// PrelinearizeTupleLayouts sets the optional layouts attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence for all the +// tuple shapes in the order the shapes appear in the "shapes" input. The layout +// elements for a sub-shape can be set to -1 in which case the corresponding layout +// will be computed by the infeed operation. +// If not specified, defaults to <> +func PrelinearizeTupleLayouts(value []int64) PrelinearizeTupleAttr { + return func(m optionalAttr) { + m["layouts"] = value + } +} + +// An op which linearizes multiple Tensor values to an opaque variant tensor. +// +// Arguments: +// inputs: A list of tensors that will be provided using the infeed mechanism. +// shapes: The shapes of each tensor in `inputs`. +func PrelinearizeTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...PrelinearizeTupleAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PrelinearizeTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdagradParametersAttr is an optional argument to LoadTPUEmbeddingAdagradParameters. +type LoadTPUEmbeddingAdagradParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingAdagradParametersTableId(value int64) LoadTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersTableName(value string) LoadTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Adagrad embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdagradParameters", + Input: []tf.Input{ + parameters, accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2. +type ResourceSparseApplyFtrlV2Attr func(optionalAttr) + +// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. +// +// That is for rows we have grad for, we update var, accum and linear as follows: +// grad_with_shrinkage = grad + 2 * l2_shrinkage * var +// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage +// linear += grad_with_shrinkage + +// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 shrinkage regulariation. Must be a scalar. +// +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrlV2", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedMatMulWithBiasAndReluAttr is an optional argument to QuantizedMatMulWithBiasAndRelu. +type QuantizedMatMulWithBiasAndReluAttr func(optionalAttr) + +// QuantizedMatMulWithBiasAndReluToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMatMulWithBiasAndReluToutput(value tf.DataType) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// QuantizedMatMulWithBiasAndReluTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluTransposeA(value bool) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulWithBiasAndReluTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluTransposeB(value bool) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulWithBiasAndReluInputQuantMode sets the optional input_quant_mode attribute to value. +// +// value: Input data quantization mode. Either MIN_FIRST(default) or SCALED. +// If not specified, defaults to "MIN_FIRST" +func QuantizedMatMulWithBiasAndReluInputQuantMode(value string) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["input_quant_mode"] = value + } +} + +// Perform a quantized matrix multiplication of `a` by the matrix `b` with bias +// add and relu fusion. +// +// The inputs must be two-dimensional matrices and 1D bias vector. And the inner +// dimension of `a` (after being transposed if `transpose_a` is non-zero) must +// match the outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). Then do broadcast add operation with bias values on the matrix +// mulplication result. The bias size must match inner dimension of `b`. Then do +// relu activation to get non-negative result. +// +// Arguments: +// a: A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. +// b: A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. +// bias: A 1D bias tensor with size matching with inner dimension of `b` (after being +// transposed if `transposed_b` is non-zero). +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedMatMulWithBiasAndRelu(scope *Scope, a tf.Output, b tf.Output, bias tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulWithBiasAndReluAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMatMulWithBiasAndRelu", + Input: []tf.Input{ + a, b, bias, min_a, max_a, min_b, max_b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// OutfeedDequeueTupleAttr is an optional argument to OutfeedDequeueTuple. +type OutfeedDequeueTupleAttr func(optionalAttr) + +// OutfeedDequeueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func OutfeedDequeueTupleDeviceOrdinal(value int64) OutfeedDequeueTupleAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Retrieve multiple values from the computation outfeed. +// +// This operation will block indefinitely until data is available. Output `i` +// corresponds to XLA tuple element `i`. +// +// Arguments: +// dtypes: The element types of each element in `outputs`. +// shapes: The shapes of each tensor in `outputs`. +// +// Returns A list of tensors that will be read from the outfeed. +func OutfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape, optional ...OutfeedDequeueTupleAttr) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OutfeedDequeueTuple", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("OutfeedDequeueTuple", err) + return + } + return outputs +} + +// Add all input tensors element wise. +// +// Arguments: +// inputs: Must all be the same size and shape. +func AddN(scope *Scope, inputs []tf.Output) (sum tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AddN", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces a string handle for the given MultiDeviceIterator. +// +// Arguments: +// multi_device_iterator: A MultiDeviceIterator resource. +// +// Returns A string representing the resource. +func MultiDeviceIteratorToStringHandle(scope *Scope, multi_device_iterator tf.Output) (string_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorToStringHandle", + Input: []tf.Input{ + multi_device_iterator, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // RetrieveTPUEmbeddingMomentumParametersAttr is an optional argument to RetrieveTPUEmbeddingMomentumParameters. type RetrieveTPUEmbeddingMomentumParametersAttr func(optionalAttr) @@ -12084,46 +24522,1384 @@ func RetrieveTPUEmbeddingMomentumParameters(scope *Scope, num_shards int64, shar return op.Output(0), op.Output(1) } -// Computes the derivative of a Gamma random sample w.r.t. `alpha`. -func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) { +// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad. +type FractionalAvgPoolGradAttr func(optionalAttr) + +// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [41/3, 26/3] for fractional avg pooling. +// If not specified, defaults to false +func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// Computes gradient of the FractionalAvgPool function. +// +// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for +// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of +// out_backprop to those indices that form the same pooling cell. Therefore, we +// just need to know the shape of original input tensor, instead of the whole +// tensor. +// +// Arguments: +// orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool` +// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients +// w.r.t. the output of `fractional_avg_pool`. +// row_pooling_sequence: row pooling sequence, form pooling region with +// col_pooling_sequence. +// col_pooling_sequence: column pooling sequence, form pooling region with +// row_pooling sequence. +// +// Returns 4-D. Gradients w.r.t. the input of `fractional_avg_pool`. +func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalAvgPoolGrad", + Input: []tf.Input{ + orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Assigns a new value to a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to return +// this value or a subsequent newer value of the variable. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value to set the new tensor to use. +// +// Returns the created operation. +func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "RandomGammaGrad", + Type: "AssignVariableOp", Input: []tf.Input{ - alpha, sample, + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// MapStageAttr is an optional argument to MapStage. +type MapStageAttr func(optionalAttr) + +// MapStageCapacity sets the optional capacity attribute to value. +// +// value: Maximum number of elements in the Staging Area. If > 0, inserts +// on the container will block when the capacity is reached. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapStageCapacity(value int64) MapStageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapStageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapStageMemoryLimit(value int64) MapStageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapStageContainer sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. Otherwise, +// a default container is used. +// If not specified, defaults to "" +func MapStageContainer(value string) MapStageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapStageSharedName sets the optional shared_name attribute to value. +// +// value: It is necessary to match this name to the matching Unstage Op. +// If not specified, defaults to "" +func MapStageSharedName(value string) MapStageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Stage (key, values) in the underlying container which behaves like a hashtable. +// +// Arguments: +// key: int64 +// +// values: a list of tensors +// dtypes A list of data types that inserted values should adhere to. +// +// +// Returns the created operation. +func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...MapStageAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapStage", + Input: []tf.Input{ + key, indices, tf.OutputList(values), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingMomentumParametersAttr is an optional argument to LoadTPUEmbeddingMomentumParameters. +type LoadTPUEmbeddingMomentumParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingMomentumParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingMomentumParametersTableId(value int64) LoadTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersTableName(value string) LoadTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Momentum embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Momentum optimization algorithm. +// momenta: Value of momenta used in the Momentum optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMomentumParameters(scope *Scope, parameters tf.Output, momenta tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMomentumParameters", + Input: []tf.Input{ + parameters, momenta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns a batched diagonal tensor with a given batched diagonal values. +// +// Given a `diagonal`, this operation returns a tensor with the `diagonal` and +// everything else padded with zeros. The diagonal is computed as follows: +// +// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a +// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: +// +// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`. +// +// For example: +// +// ``` +// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] +// +// and diagonal.shape = (2, 4) +// +// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]], +// [[5, 0, 0, 0] +// [0, 6, 0, 0] +// [0, 0, 7, 0] +// [0, 0, 0, 8]]] +// +// which has shape (2, 4, 4) +// ``` +// +// Arguments: +// diagonal: Rank `k`, where `k >= 1`. +// +// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`. +func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiag", + Input: []tf.Input{ + diagonal, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// TruncatedNormalAttr is an optional argument to TruncatedNormal. -type TruncatedNormalAttr func(optionalAttr) +// Store the input tensor in the state of the current session. +// +// Arguments: +// value: The tensor to be stored. +// +// Returns The handle for the tensor stored in the session state, represented +// as a string. +func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GetSessionHandle", + Input: []tf.Input{ + value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// TruncatedNormalSeed sets the optional seed attribute to value. +// This op is used as a placeholder in If branch functions. It doesn't provide a +// valid output when run, so must either be removed (e.g. replaced with a +// function input) or guaranteed not to be used (e.g. if mirroring an +// intermediate output needed for the gradient computation of the other branch). +// +// Arguments: +// dtype: The type of the output. +// shape: The purported shape of the output. This is only used for shape inference; +// the output will not necessarily have this shape. Can be a partial shape. +// +// Returns \"Fake\" output value. This should not be consumed by another op. +func FakeParam(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "FakeParam", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomShuffleAttr is an optional argument to RandomShuffle. +type RandomShuffleAttr func(optionalAttr) + +// RandomShuffleSeed sets the optional seed attribute to value. // // value: If either `seed` or `seed2` are set to be non-zero, the random number // generator is seeded by the given seed. Otherwise, it is seeded by a // random seed. // If not specified, defaults to 0 -func TruncatedNormalSeed(value int64) TruncatedNormalAttr { +func RandomShuffleSeed(value int64) RandomShuffleAttr { return func(m optionalAttr) { m["seed"] = value } } -// TruncatedNormalSeed2 sets the optional seed2 attribute to value. +// RandomShuffleSeed2 sets the optional seed2 attribute to value. // // value: A second seed to avoid seed collision. // If not specified, defaults to 0 -func TruncatedNormalSeed2(value int64) TruncatedNormalAttr { +func RandomShuffleSeed2(value int64) RandomShuffleAttr { return func(m optionalAttr) { m["seed2"] = value } } +// Randomly shuffles a tensor along its first dimension. +// +// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped +// to one and only one `output[i]`. For example, a mapping that might occur for a +// 3x2 tensor is: +// +// ``` +// [[1, 2], [[5, 6], +// [3, 4], ==> [1, 2], +// [5, 6]] [3, 4]] +// ``` +// +// Arguments: +// value: The tensor to be shuffled. +// +// Returns A tensor of same shape and type as `value`, shuffled along its first +// dimension. +func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomShuffle", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative. +type DepthwiseConv2dNativeAttr func(optionalAttr) + +// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors. +// +// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` +// and a filter / kernel tensor of shape +// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing +// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies +// a different filter to each input channel (expanding from 1 channel to +// `channel_multiplier` channels for each), then concatenates the results +// together. Thus, the output has `in_channels * channel_multiplier` channels. +// +// ``` +// for k in 0..in_channels-1 +// for q in 0..channel_multiplier-1 +// output[b, i, j, k * channel_multiplier + q] = +// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * +// filter[di, dj, k, q] +// ``` +// +// Must have `strides[0] = strides[3] = 1`. For the most common case of the same +// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. +// +// Arguments: +// +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. +// padding: The type of padding algorithm to use. +func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNative", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionV3(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV3", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Shuts down a running distributed TPU system. +// +// The op returns an error if no system is running. +// +// Returns the created operation. +func ShutdownDistributedTPU(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ShutdownDistributedTPU", + } + return scope.AddOperation(opspec) +} + +// Creates a dataset that changes the batch size. +// +// Creates a dataset that changes the batch size of the dataset to current batch +// size // num_workers. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// num_workers: A scalar representing the number of workers to distribute this batch across. As +// a result of this transformation the current batch size would end up being +// divided by this parameter. +// +// +func ExperimentalRebatchDataset(scope *Scope, input_dataset tf.Output, num_workers tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalRebatchDataset", + Input: []tf.Input{ + input_dataset, num_workers, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse real-valued fast Fourier transform. +// +// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most dimension of `input`. +// +// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the +// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If +// `fft_length` is not provided, it is computed from the size of the inner-most +// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to +// compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller +// than the corresponding dimension of `input`, the dimension is cropped. If it is +// larger, the dimension is padded with zeros. +// +// Arguments: +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [1]. The FFT length. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most +// dimension of `input` is replaced with the `fft_length` samples of its inverse +// 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.irfft +// @end_compatibility +func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IRFFT", + Input: []tf.Input{ + input, fft_length, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a Tensor into a serialized TensorProto proto. +// +// Arguments: +// tensor: A Tensor of type `T`. +// +// Returns A serialized TensorProto proto of the input tensor. +func SerializeTensor(scope *Scope, tensor tf.Output) (serialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SerializeTensor", + Input: []tf.Input{ + tensor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs all keys and values in the table. +// +// Arguments: +// table_handle: Handle to the table. +// +// +// +// Returns Vector of all keys present in the table.Tensor of all values in the table. Indexed in parallel with `keys`. +func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType, Tvalues tf.DataType) (keys tf.Output, values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Tkeys": Tkeys, "Tvalues": Tvalues} + opspec := tf.OpSpec{ + Type: "LookupTableExportV2", + Input: []tf.Input{ + table_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// TPUReplicateMetadataAttr is an optional argument to TPUReplicateMetadata. +type TPUReplicateMetadataAttr func(optionalAttr) + +// TPUReplicateMetadataNumCoresPerReplica sets the optional num_cores_per_replica attribute to value. +// +// value: Number of cores per replica. Used for model parallelism. +// If not specified, defaults to 1 +func TPUReplicateMetadataNumCoresPerReplica(value int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["num_cores_per_replica"] = value + } +} + +// TPUReplicateMetadataTopology sets the optional topology attribute to value. +// +// value: TopologyProto indicating the topology of the TPU pod slice. +// If not specified, defaults to "" +func TPUReplicateMetadataTopology(value string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["topology"] = value + } +} + +// TPUReplicateMetadataUseTpu sets the optional use_tpu attribute to value. +// +// value: Whether to place the computation on the TPU. +// If not specified, defaults to true +func TPUReplicateMetadataUseTpu(value bool) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["use_tpu"] = value + } +} + +// TPUReplicateMetadataDeviceAssignment sets the optional device_assignment attribute to value. +// +// value: The assignment of devices for the computation. +// If not specified, defaults to <> +func TPUReplicateMetadataDeviceAssignment(value []int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["device_assignment"] = value + } +} + +// TPUReplicateMetadataComputationShape sets the optional computation_shape attribute to value. +// +// value: DEPRECATED. Use num_cores_per_replica instead. +// If not specified, defaults to <> +func TPUReplicateMetadataComputationShape(value []int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["computation_shape"] = value + } +} + +// TPUReplicateMetadataHostComputeCore sets the optional host_compute_core attribute to value. +// If not specified, defaults to <> +func TPUReplicateMetadataHostComputeCore(value []string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["host_compute_core"] = value + } +} + +// TPUReplicateMetadataPaddingMap sets the optional padding_map attribute to value. +// If not specified, defaults to <> +func TPUReplicateMetadataPaddingMap(value []string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["padding_map"] = value + } +} + +// TPUReplicateMetadataStepMarkerLocation sets the optional step_marker_location attribute to value. +// If not specified, defaults to "STEP_MARK_AT_ENTRY" +func TPUReplicateMetadataStepMarkerLocation(value string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["step_marker_location"] = value + } +} + +// Metadata indicaitng how the TPU computation should be replicated. +// +// Arguments: +// num_replicas: Number of replicas of the computation +// +// Returns the created operation. +func TPUReplicateMetadata(scope *Scope, num_replicas int64, optional ...TPUReplicateMetadataAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_replicas": num_replicas} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TPUReplicateMetadata", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Conv2DAttr is an optional argument to Conv2D. +type Conv2DAttr func(optionalAttr) + +// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DExplicitPaddings sets the optional explicit_paddings attribute to value. +// +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DExplicitPaddings(value []int64) Conv2DAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// Conv2DDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func Conv2DDataFormat(value string) Conv2DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv2DDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv2DDilations(value []int64) Conv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2-D convolution given 4-D `input` and `filter` tensors. +// +// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` +// and a filter / kernel tensor of shape +// `[filter_height, filter_width, in_channels, out_channels]`, this op +// performs the following: +// +// 1. Flattens the filter to a 2-D matrix with shape +// `[filter_height * filter_width * in_channels, output_channels]`. +// 2. Extracts image patches from the input tensor to form a *virtual* +// tensor of shape `[batch, out_height, out_width, +// filter_height * filter_width * in_channels]`. +// 3. For each patch, right-multiplies the filter matrix and the image patch +// vector. +// +// In detail, with the default NHWC format, +// +// output[b, i, j, k] = +// sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * +// filter[di, dj, q, k] +// +// Must have `strides[0] = strides[3] = 1`. For the most common case of the same +// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. +// +// Arguments: +// input: A 4-D tensor. The dimension order is interpreted according to the value +// of `data_format`, see below for details. +// filter: A 4-D tensor of shape +// `[filter_height, filter_width, in_channels, out_channels]` +// strides: 1-D tensor of length 4. The stride of the sliding window for each +// dimension of `input`. The dimension order is determined by the value of +// `data_format`, see below for details. +// padding: The type of padding algorithm to use. +// +// Returns A 4-D tensor. The dimension order is determined by the value of +// `data_format`, see below for details. +func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv2D", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchMatMulAttr is an optional argument to BatchMatMul. +type BatchMatMulAttr func(optionalAttr) + +// BatchMatMulAdjX sets the optional adj_x attribute to value. +// +// value: If `True`, adjoint the slices of `x`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulAdjX(value bool) BatchMatMulAttr { + return func(m optionalAttr) { + m["adj_x"] = value + } +} + +// BatchMatMulAdjY sets the optional adj_y attribute to value. +// +// value: If `True`, adjoint the slices of `y`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulAdjY(value bool) BatchMatMulAttr { + return func(m optionalAttr) { + m["adj_y"] = value + } +} + +// Multiplies slices of two tensors in batches. +// +// Multiplies all slices of `Tensor` `x` and `y` (each slice can be +// viewed as an element of a batch), and arranges the individual results +// in a single output tensor of the same batch size. Each of the +// individual slices can optionally be adjointed (to adjoint a matrix +// means to transpose and conjugate it) before multiplication by setting +// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. +// +// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` +// and `[..., r_y, c_y]`. +// +// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: +// +// r_o = c_x if adj_x else r_x +// c_o = r_y if adj_y else c_y +// +// It is computed as: +// +// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) +// +// Arguments: +// x: 2-D or higher with shape `[..., r_x, c_x]`. +// y: 2-D or higher with shape `[..., r_y, c_y]`. +// +// Returns 3-D or higher with shape `[..., r_o, c_o]` +func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchMatMul", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingMomentumParametersGradAccumDebug. +type LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load Momentum embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Momentum optimization algorithm. +// momenta: Value of momenta used in the Momentum optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Momentum optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMomentumParametersGradAccumDebug", + Input: []tf.Input{ + parameters, momenta, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns a batched matrix tensor with new batched diagonal values. +// +// Given `input` and `diagonal`, this operation returns a tensor with the +// same shape and values as `input`, except for the main diagonal of the +// innermost matrices. These will be overwritten by the values in `diagonal`. +// +// The output is computed as follows: +// +// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has +// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a +// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where: +// +// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`. +// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`. +// +// Arguments: +// input: Rank `k+1`, where `k >= 1`. +// diagonal: Rank `k`, where `k >= 1`. +// +// Returns Rank `k+1`, with `output.shape = input.shape`. +func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixSetDiag", + Input: []tf.Input{ + input, diagonal, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) + +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. +// +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. +type MapUnstageNoKeyAttr func(optionalAttr) + +// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapUnstageNoKeyContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns a random (key, value) +// +// from the underlying container. If the underlying container +// does not contain elements, the op will block until it does. +func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapUnstageNoKey", + Input: []tf.Input{ + indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + key = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapUnstageNoKey", err) + return + } + return key, values +} + +// RetrieveTPUEmbeddingAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingAdagradParameters. +type RetrieveTPUEmbeddingAdagradParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingAdagradParametersTableId(value int64) RetrieveTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersTableName(value string) RetrieveTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve Adagrad embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the Adagrad optimization algorithm.Parameter accumulators updated by the Adagrad optimization algorithm. +func RetrieveTPUEmbeddingAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdagradParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes the sum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \sum_j data_j\\) where sum is over `j` such +// that `segment_ids[j] == i`. +// +// If the sum is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_sum(c, tf.constant([0, 0, 1])) +// # ==> [[5, 5, 5, 5], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentSum", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2. +type ResourceApplyFtrlV2Attr func(optionalAttr) + +// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the Ftrl-proximal scheme. +// +// grad_with_shrinkage = grad + 2 * l2_shrinkage * var +// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage +// linear += grad_with_shrinkage + +// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regulariation. Must be a scalar. +// l2: L2 shrinkage regulariation. Must be a scalar. +// +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyFtrlV2", + Input: []tf.Input{ + var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Says whether the targets are in the top `K` predictions. +// +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed precision at `k` as a `bool Tensor`. +func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InTopKV2", + Input: []tf.Input{ + predictions, targets, k, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of NOT x element-wise. +func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogicalNot", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) + +// MultinomialSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// MultinomialSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Multinomial", + Input: []tf.Input{ + logits, num_samples, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatefulTruncatedNormalAttr is an optional argument to StatefulTruncatedNormal. +type StatefulTruncatedNormalAttr func(optionalAttr) + +// StatefulTruncatedNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulTruncatedNormalDtype(value tf.DataType) StatefulTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + // Outputs random values from a truncated normal distribution. // // The generated values follow a normal distribution with mean 0 and standard @@ -12131,23 +25907,23 @@ func TruncatedNormalSeed2(value int64) TruncatedNormalAttr { // deviations from the mean are dropped and re-picked. // // Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. // shape: The shape of the output tensor. -// dtype: The type of the output. // -// Returns A tensor of the specified shape filled with random truncated normal -// values. -func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) { +// Returns Random values with specified shape. +func StatefulTruncatedNormal(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulTruncatedNormalAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TruncatedNormal", + Type: "StatefulTruncatedNormal", Input: []tf.Input{ - shape, + resource, algorithm, shape, }, Attrs: attrs, } @@ -12155,110 +25931,64 @@ func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional return op.Output(0) } -// Merges summaries. +// Computes natural logarithm of x element-wise. // -// This op creates a -// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) -// protocol buffer that contains the union of all the values in the input -// summaries. -// -// When the Op is run, it reports an `InvalidArgument` error if multiple values -// in the summaries to merge use the same tag. -// -// Arguments: -// inputs: Can be of any shape. Each must contain serialized `Summary` protocol -// buffers. -// -// Returns Scalar. Serialized `Summary` protocol buffer. -func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) { +// I.e., \\(y = \log_e x\\). +func Log(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "MergeSummary", + Type: "Log", Input: []tf.Input{ - tf.OutputList(inputs), + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes the reverse mode backpropagated gradient of the Cholesky algorithm. +// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad. +type AvgPool3DGradAttr func(optionalAttr) + +// AvgPool3DGradDataFormat sets the optional data_format attribute to value. // -// For an explanation see "Differentiation of the Cholesky algorithm" by -// Iain Murray http://arxiv.org/abs/1602.07527. +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of average pooling function. // // Arguments: -// l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`. -// Algorithm depends only on lower triangular part of the innermost matrices of -// this tensor. -// grad: df/dl where f is some scalar function. Shape is `[..., M, M]`. -// Algorithm depends only on lower triangular part of the innermost matrices of -// this tensor. +// orig_input_shape: The original input dimensions. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. // -// Returns Symmetrized version of df/dA . Shape is `[..., M, M]` -func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) { +// Returns The backprop for input. +func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "CholeskyGrad", - Input: []tf.Input{ - l, grad, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RandomUniformAttr is an optional argument to RandomUniform. -type RandomUniformAttr func(optionalAttr) - -// RandomUniformSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomUniformSeed(value int64) RandomUniformAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomUniformSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomUniformSeed2(value int64) RandomUniformAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Outputs random values from a uniform distribution. -// -// The generated values follow a uniform distribution in the range `[0, 1)`. The -// lower bound 0 is included in the range, while the upper bound 1 is excluded. -// -// Arguments: -// shape: The shape of the output tensor. -// dtype: The type of the output. -// -// Returns A tensor of the specified shape filled with uniform random values. -func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "RandomUniform", + Type: "AvgPool3DGrad", Input: []tf.Input{ - shape, + orig_input_shape, grad, }, Attrs: attrs, } @@ -12266,110 +25996,595 @@ func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional .. return op.Output(0) } -// Return a tensor with the same shape and contents as the input tensor or value. -func Identity(scope *Scope, input tf.Output) (output tf.Output) { +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Identity", + Type: "Add", Input: []tf.Input{ - input, + x, y, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Get the current size of the TensorArray. +// Computes the inverse permutation of a tensor. +// +// This operation computes the inverse of an index permutation. It takes a 1-D +// integer tensor `x`, which represents the indices of a zero-based array, and +// swaps each value with its index position. In other words, for an output tensor +// `y` and an input tensor `x`, this operation computes the following: +// +// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]` +// +// The values must include 0. There can be no duplicate values or negative values. +// +// For example: +// +// ``` +// # tensor `x` is [3, 4, 0, 2, 1] +// invert_permutation(x) ==> [2, 4, 3, 0, 1] +// ``` // // Arguments: -// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). -// flow_in: A float scalar that enforces proper chaining of operations. +// x: 1-D. // -// Returns The current size of the TensorArray. -func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) { +// Returns 1-D. +func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "TensorArraySizeV3", + Type: "InvertPermutation", Input: []tf.Input{ - handle, flow_in, + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// A dataset that creates window datasets from the input dataset. +// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. +type ResourceApplyMomentumAttr func(optionalAttr) + +// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the momentum scheme. Set use_nesterov = True if you +// +// want to use Nesterov momentum. +// +// accum = accum * momentum + grad +// var -= lr * accum // // Arguments: -// -// size: A scalar representing the number of elements to accumulate in a window. -// shift: A scalar representing the steps moving the sliding window forward in one -// iteration. It must be positive. -// stride: A scalar representing the stride of the input elements of the sliding window. -// It must be positive. -// drop_remainder: A scalar representing whether a window should be dropped in case its size is -// smaller than desired. -// -// -func WindowDataset(scope *Scope, input_dataset tf.Output, size tf.Output, shift tf.Output, stride tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "WindowDataset", - Input: []tf.Input{ - input_dataset, size, shift, stride, drop_remainder, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Worker heartbeat op. -// -// Heartbeats may be sent periodically to indicate the coordinator is still active, -// to retrieve the current worker status and to expedite shutdown when necessary. -// -// Arguments: -// request: A string tensor containing a serialized WorkerHeartbeatRequest -// -// Returns A string tensor containing a serialized WorkerHeartbeatResponse -func WorkerHeartbeat(scope *Scope, request tf.Output) (response tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "WorkerHeartbeat", - Input: []tf.Input{ - request, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Set a summary_writer_interface to record statistics using given stats_aggregator. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// momentum: Momentum. Must be a scalar. // // Returns the created operation. -func StatsAggregatorSetSummaryWriter(scope *Scope, stats_aggregator tf.Output, summary tf.Output) (o *tf.Operation) { +func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SetSizeAttr is an optional argument to SetSize. +type SetSizeAttr func(optionalAttr) + +// SetSizeValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SetSizeValidateIndices(value bool) SetSizeAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Number of unique elements along last dimension of input `set`. +// +// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, +// and `set_shape`. The last dimension contains values in a set, duplicates are +// allowed but ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set` +// indices. +// +// Arguments: +// set_indices: 2D `Tensor`, indices of a `SparseTensor`. +// set_values: 1D `Tensor`, values of a `SparseTensor`. +// set_shape: 1D `Tensor`, shape of a `SparseTensor`. +// +// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st +// `n-1` dimensions as `set`. Each value is the number of unique elements in +// the corresponding `[0...n-1]` dimension of `set`. +func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SetSize", + Input: []tf.Input{ + set_indices, set_values, set_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns up to `num_records` (key, value) pairs produced by a Reader. +// +// Will dequeue from the input queue if necessary (e.g. when the +// Reader needs to start reading from a new file since it has finished +// with the previous file). +// It may return less than `num_records` even before the last batch. +// +// Arguments: +// reader_handle: Handle to a `Reader`. +// queue_handle: Handle to a `Queue`, with string work items. +// num_records: number of records to read from `Reader`. +// +// Returns A 1-D tensor.A 1-D tensor. +func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "StatsAggregatorSetSummaryWriter", + Type: "ReaderReadUpToV2", Input: []tf.Input{ - stats_aggregator, summary, + reader_handle, queue_handle, num_records, }, } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Receives a tensor value broadcast from another device. +func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + opspec := tf.OpSpec{ + Type: "CollectiveBcastRecv", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces the max pool of the input tensor for quantized types. +// +// Arguments: +// input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// ksize: The size of the window for each dimension of the input tensor. +// The length must be 4 to match the number of dimensions of the input. +// strides: The stride of the sliding window for each dimension of the input +// tensor. The length must be 4 to match the number of dimensions of the input. +// padding: The type of padding algorithm to use. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "QuantizedMaxPool", + Input: []tf.Input{ + input, min_input, max_input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MaxPoolGradGradWithArgmaxAttr is an optional argument to MaxPoolGradGradWithArgmax. +type MaxPoolGradGradWithArgmaxAttr func(optionalAttr) + +// MaxPoolGradGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. +// +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolGradGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradGradWithArgmaxAttr { + return func(m optionalAttr) { + m["include_batch_in_index"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// input: The original input. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the +// input of `max_pool`. +// argmax: The indices of the maximum values chosen for each output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input of `max_pool`. +func MaxPoolGradGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradWithArgmaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGradWithArgmax", + Input: []tf.Input{ + input, grad, argmax, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyAdadeltaAttr is an optional argument to ResourceSparseApplyAdadelta. +type ResourceSparseApplyAdadeltaAttr func(optionalAttr) + +// ResourceSparseApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdadeltaUseLocking(value bool) ResourceSparseApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// var: Should be from a Variable(). +// +// Arguments: +// +// accum: Should be from a Variable(). +// accum_update: : Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdadeltaAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdadelta", + Input: []tf.Input{ + var_, accum, accum_update, lr, rho, epsilon, grad, indices, + }, + Attrs: attrs, + } return scope.AddOperation(opspec) } +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) + +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { + return func(m optionalAttr) { + m["preferred_shard"] = value + } +} + +// Restores a tensor from checkpoint files. +// +// This is like `Restore` except that restored tensor can be listed as filling +// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the +// larger tensor and the slice that the restored tensor covers. +// +// The `shape_and_slice` input has the same format as the +// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// +// Arguments: +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// shape_and_slice: Scalar. The shapes and slice specifications to use when +// restoring a tensors. +// dt: The type of the tensor to be restored. +// +// Returns The restored tensor. +func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dt": dt} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RestoreSlice", + Input: []tf.Input{ + file_pattern, tensor_name, shape_and_slice, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug. +type RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve Adadelta embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the Adadelta optimization algorithm.Parameter accumulators updated by the Adadelta optimization algorithm.Parameter updates updated by the Adadelta optimization algorithm.Parameter gradient_accumulators updated by the Adadelta optimization algorithm. +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. +// +// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the +// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each +// input channel is processed independently of the others with its own structuring +// function. The `output` tensor has shape +// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output +// tensor depend on the `padding` algorithm. We currently only support the default +// "NHWC" `data_format`. +// +// In detail, the grayscale morphological 2-D dilation is the max-sum correlation +// (for consistency with `conv2d`, we use unmirrored filters): +// +// output[b, y, x, c] = +// max_{dy, dx} input[b, +// strides[1] * y + rates[1] * dy, +// strides[2] * x + rates[2] * dx, +// c] + +// filter[dy, dx, c] +// +// Max-pooling is a special case when the filter has size equal to the pooling +// kernel size and contains all zeros. +// +// Note on duality: The dilation of `input` by the `filter` is equal to the +// negation of the erosion of `-input` by the reflected `filter`. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// strides: The stride of the sliding window for each dimension of the input +// tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: The input stride for atrous morphological dilation. Must be: +// `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, out_height, out_width, depth]`. +func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2D", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. +type StatelessRandomNormalAttr func(optionalAttr) + +// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingCenteredRMSPropParameters. +type RetrieveTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingCenteredRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingCenteredRMSPropParametersTableName(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve centered RMSProp embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the centered RMSProp optimization algorithm.Parameter ms updated by the centered RMSProp optimization algorithm.Parameter mom updated by the centered RMSProp optimization algorithm.Parameter mg updated by the centered RMSProp optimization algorithm. +func RetrieveTPUEmbeddingCenteredRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingCenteredRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingCenteredRMSPropParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Adjust the saturation of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last dimension is +// interpretted as channels, and must be three. +// +// The input image is considered in the RGB colorspace. Conceptually, the RGB +// colors are first mapped into HSV. A scale is then applied all the saturation +// values, and then remapped back to RGB colorspace. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// scale: A float scale to add to the saturation. +// +// Returns The hue-adjusted image or images. +func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustSaturation", + Input: []tf.Input{ + images, scale, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // StatefulStandardNormalAttr is an optional argument to StatefulStandardNormal. type StatefulStandardNormalAttr func(optionalAttr) @@ -12413,24 +26628,666 @@ func StatefulStandardNormal(scope *Scope, resource tf.Output, shape tf.Output, o return op.Output(0) } -// A substitute for `InterleaveDataset` on a fixed list of `N` datasets. +// RetrieveTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to RetrieveTPUEmbeddingMDLAdagradLightParameters. +type RetrieveTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 // -// Arguments: -// selector_input_dataset: A dataset of scalar `DT_INT64` elements that determines which of the -// `N` data inputs should produce the next output element. -// data_input_datasets: `N` datasets with the same type that will be interleaved according to -// the values of `selector_input_dataset`. +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingMDLAdagradLightParametersTableId(value int64) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMDLAdagradLightParametersTableName(value string) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve MDL Adagrad Light embedding parameters. // +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. // -func ExperimentalDirectedInterleaveDataset(scope *Scope, selector_input_dataset tf.Output, data_input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Returns Parameter parameters updated by the MDL Adagrad Light optimization algorithm.Parameter accumulators updated by the MDL Adagrad Light optimization algorithm.Parameter weights updated by the MDL Adagrad Light optimization algorithm.Parameter benefits updated by the MDL Adagrad Light optimization algorithm. +func RetrieveTPUEmbeddingMDLAdagradLightParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMDLAdagradLightParametersAttr) (parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ExperimentalDirectedInterleaveDataset", + Type: "RetrieveTPUEmbeddingMDLAdagradLightParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Creates a TensorList by indexing into a Tensor. +// +// Each member of the TensorList corresponds to one row of the input tensor, +// specified by the given index (see `tf.gather`). +// +// tensor: The input tensor. +// indices: The indices used to index into the list. +// element_shape: The shape of the elements in the list (can be less specified than +// the shape of the tensor). +// num_elements: The size of the output list. Must be large enough to accommodate +// the largest index in indices. If -1, the list is just large enough to include +// the largest index in indices. +// output_handle: The TensorList. +func TensorListScatterV2(scope *Scope, tensor tf.Output, indices tf.Output, element_shape tf.Output, num_elements tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListScatterV2", Input: []tf.Input{ - selector_input_dataset, tf.OutputList(data_input_datasets), + tensor, indices, element_shape, num_elements, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SdcaOptimizerV2Attr is an optional argument to SdcaOptimizerV2. +type SdcaOptimizerV2Attr func(optionalAttr) + +// SdcaOptimizerV2Adaptive sets the optional adaptive attribute to value. +// +// value: Whether to use Adaptive SDCA for the inner loop. +// If not specified, defaults to true +func SdcaOptimizerV2Adaptive(value bool) SdcaOptimizerV2Attr { + return func(m optionalAttr) { + m["adaptive"] = value + } +} + +// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for +// +// linear models with L1 + L2 regularization. As global optimization objective is +// strongly-convex, the optimizer optimizes the dual objective at each step. The +// optimizer applies each update one example at a time. Examples are sampled +// uniformly, and the optimizer is learning rate free and enjoys linear convergence +// rate. +// +// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
+// Shai Shalev-Shwartz, Tong Zhang. 2012 +// +// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ +// +// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, +// Peter Richtarik, Martin Takac. 2015 +// +// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 +// +// Arguments: +// sparse_example_indices: a list of vectors which contain example indices. +// sparse_feature_indices: a list of vectors which contain feature indices. +// sparse_feature_values: a list of vectors which contains feature value +// associated with each feature group. +// dense_features: a list of matrices which contains the dense feature values. +// example_weights: a vector which contains the weight associated with each +// example. +// example_labels: a vector which contains the label/target associated with each +// example. +// sparse_indices: a list of vectors where each value is the indices which has +// corresponding weights in sparse_weights. This field maybe omitted for the +// dense approach. +// sparse_weights: a list of vectors where each value is the weight associated with +// a sparse feature group. +// dense_weights: a list of vectors where the values are the weights associated +// with a dense feature group. +// example_state_data: a list of vectors containing the example state data. +// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, +// squared and hinge losses. +// l1: Symmetric l1 regularization strength. +// l2: Symmetric l2 regularization strength. +// num_loss_partitions: Number of partitions of the global loss function. +// num_inner_iterations: Number of iterations per mini-batch. +// +// Returns a list of vectors containing the updated example state +// data.a list of vectors where each value is the delta +// weights associated with a sparse feature group.a list of vectors where the values are the delta +// weights associated with a dense feature group. +func SdcaOptimizerV2(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerV2Attr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SdcaOptimizerV2", + Input: []tf.Input{ + tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + out_example_state_data = op.Output(idx) + if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { + scope.UpdateErr("SdcaOptimizerV2", err) + return + } + if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { + scope.UpdateErr("SdcaOptimizerV2", err) + return + } + return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights +} + +// TopKAttr is an optional argument to TopK. +type TopKAttr func(optionalAttr) + +// TopKSorted sets the optional sorted attribute to value. +// +// value: If true the resulting `k` elements will be sorted by the values in +// descending order. +// If not specified, defaults to true +func TopKSorted(value bool) TopKAttr { + return func(m optionalAttr) { + m["sorted"] = value + } +} + +// Finds values and indices of the `k` largest elements for the last dimension. +// +// DEPRECATED at GraphDef version 7: Use TopKV2 instead +// +// If the input is a vector (rank-1), finds the `k` largest entries in the vector +// and outputs their values and indices as vectors. Thus `values[j]` is the +// `j`-th largest entry in `input`, and its index is `indices[j]`. +// +// For matrices (resp. higher rank input), computes the top `k` entries in each +// row (resp. vector along the last dimension). Thus, +// +// values.shape = indices.shape = input.shape[:-1] + [k] +// +// If two elements are equal, the lower-index element appears first. +// +// If `k` varies dynamically, use `TopKV2` below. +// +// Arguments: +// input: 1-D or higher with last dimension at least `k`. +// k: Number of top elements to look for along the last dimension (along each +// row for matrices). +// +// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. +func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"k": k} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TopK", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// PriorityQueueV2Attr is an optional argument to PriorityQueueV2. +type PriorityQueueV2Attr func(optionalAttr) + +// PriorityQueueV2ComponentTypes sets the optional component_types attribute to value. +// +// value: The type of each component in a value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func PriorityQueueV2ComponentTypes(value []tf.DataType) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["component_types"] = value + } +} + +// PriorityQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func PriorityQueueV2Capacity(value int64) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// PriorityQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func PriorityQueueV2Container(value string) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// PriorityQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func PriorityQueueV2SharedName(value string) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that produces elements sorted by the first component value. +// +// Note that the PriorityQueue requires the first component of any element +// to be a scalar int64, in addition to the other elements declared by +// component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue +// and DequeueMany) on a PriorityQueue will all require (resp. output) one extra +// entry in their input (resp. output) lists. +// +// Arguments: +// shapes: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. If the length of +// this attr is 0, the shapes of queue elements are not constrained, and +// only one element may be dequeued at a time. +// +// Returns The handle to the queue. +func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PriorityQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the Hurwitz zeta function \\(\zeta(x, q)\\). +// +// The Hurwitz zeta function is defined as: +// +// +// \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\) +func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Zeta", + Input: []tf.Input{ + x, q, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2. +type SelfAdjointEigV2Attr func(optionalAttr) + +// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value. +// +// value: If `True` then eigenvectors will be computed and returned in `v`. +// Otherwise, only the eigenvalues will be computed. +// If not specified, defaults to true +func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr { + return func(m optionalAttr) { + m["compute_v"] = value + } +} + +// Computes the eigen decomposition of one or more square self-adjoint matrices. +// +// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in +// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues +// are sorted in non-decreasing order. +// +// ```python +// # a is a tensor. +// # e is a tensor of eigenvalues. +// # v is a tensor of eigenvectors. +// e, v = self_adjoint_eig(a) +// e = self_adjoint_eig(a, compute_v=False) +// ``` +// +// Arguments: +// input: `Tensor` input of shape `[N, N]`. +// +// Returns Eigenvalues. Shape is `[N]`.Eigenvectors. Shape is `[N, N]`. +func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SelfAdjointEigV2", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. +type AllCandidateSamplerAttr func(optionalAttr) + +// AllCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to produce. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AllCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes log softmax activations. +// +// For each batch `i` and class `j` we have +// +// logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i]))) +// +// Arguments: +// logits: 2-D with shape `[batch_size, num_classes]`. +// +// Returns Same shape as `logits`. +func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogSoftmax", + Input: []tf.Input{ + logits, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts sparse `updates` from an existing tensor according to `indices`. +// +// This operation creates a new tensor by subtracting sparse `updates` from the +// passed in `tensor`. +// This operation is very similar to `tf.scatter_nd_sub`, except that the updates +// are subtracted from an existing tensor (as opposed to a variable). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of tensor_scatter_sub is to subtract individual elements +// from a tensor by index. For example, say we want to insert 4 scattered elements +// in a rank-1 tensor with 8 elements. +// +// In Python, this scatter subtract operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// tensor = tf.ones([8], dtype=tf.int32) +// updated = tf.tensor_scatter_sub(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [1, -10, 1, -9, -8, 1, 1, -11] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// tensor = tf.ones([4, 4, 4]) +// updated = tf.tensor_scatter_sub(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], +// [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor copied from tensor and updates subtracted according to the indices. +func TensorScatterSub(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterSub", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. +type ResourceApplyPowerSignAttr func(optionalAttr) + +// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AddSign update. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g +// variable <- variable - lr_t * update +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// logbase: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyPowerSign", + Input: []tf.Input{ + var_, m, lr, logbase, sign_decay, beta, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// StringFormatAttr is an optional argument to StringFormat. +type StringFormatAttr func(optionalAttr) + +// StringFormatTemplate sets the optional template attribute to value. +// +// value: A string, the template to format tensor summaries into. +// If not specified, defaults to "%s" +func StringFormatTemplate(value string) StringFormatAttr { + return func(m optionalAttr) { + m["template"] = value + } +} + +// StringFormatPlaceholder sets the optional placeholder attribute to value. +// +// value: A string, at each placeholder in the template a subsequent tensor summary will be inserted. +// If not specified, defaults to "%s" +func StringFormatPlaceholder(value string) StringFormatAttr { + return func(m optionalAttr) { + m["placeholder"] = value + } +} + +// StringFormatSummarize sets the optional summarize attribute to value. +// +// value: When formatting the tensor summaries print the first and last summarize entries of each tensor dimension. +// If not specified, defaults to 3 +func StringFormatSummarize(value int64) StringFormatAttr { + return func(m optionalAttr) { + m["summarize"] = value + } +} + +// Formats a string template using a list of tensors. +// +// Formats a string template using a list of tensors, pretty-printing tensor summaries. +// +// Arguments: +// inputs: The list of tensors to format into the placeholder string. +// +// Returns = The resulting string scalar. +func StringFormat(scope *Scope, inputs []tf.Output, optional ...StringFormatAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringFormat", + Input: []tf.Input{ + tf.OutputList(inputs), }, Attrs: attrs, } @@ -12438,6 +27295,1028 @@ func ExperimentalDirectedInterleaveDataset(scope *Scope, selector_input_dataset return op.Output(0) } +// DecodeBmpAttr is an optional argument to DecodeBmp. +type DecodeBmpAttr func(optionalAttr) + +// DecodeBmpChannels sets the optional channels attribute to value. +// If not specified, defaults to 0 +func DecodeBmpChannels(value int64) DecodeBmpAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// Decode the first frame of a BMP-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the BMP-encoded image. +// * 3: output an RGB image. +// * 4: output an RGBA image. +// +// Arguments: +// contents: 0-D. The BMP-encoded image. +// +// Returns 3-D with shape `[height, width, channels]`. RGB order +func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeBmp", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NonMaxSuppressionV4Attr is an optional argument to NonMaxSuppressionV4. +type NonMaxSuppressionV4Attr func(optionalAttr) + +// NonMaxSuppressionV4PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. +// +// value: If true, the output `selected_indices` is padded to be of length +// `max_output_size`. Defaults to false. +// If not specified, defaults to false +func NonMaxSuppressionV4PadToMaxOutputSize(value bool) NonMaxSuppressionV4Attr { + return func(m optionalAttr) { + m["pad_to_max_output_size"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`.A 0-D integer tensor representing the number of valid elements in +// `selected_indices`, with the valid elements appearing first. +func NonMaxSuppressionV4(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...NonMaxSuppressionV4Attr) (selected_indices tf.Output, valid_outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV4", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) + +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessTruncatedNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SumAttr is an optional argument to Sum. +type SumAttr func(optionalAttr) + +// SumKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SumKeepDims(value bool) SumAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Sum", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates sparse cross from a list of sparse and dense tensors. +// +// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each +// representing features of one feature column. It outputs a 2D `SparseTensor` with +// the batchwise crosses of these features. +// +// For example, if the inputs are +// +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" +// +// inputs[2]: Tensor [["f"], ["g"]] +// +// then the output will be +// +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// hashed_output: If true, returns the hash of the cross instead of the string. +// This will allow us avoiding string manipulations. +// num_buckets: It is used if hashed_output is true. +// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. +// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` +// function to combine the crosses fingerprints. +// +// +// +// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. +func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} + opspec := tf.OpSpec{ + Type: "SparseCross", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Inverse 3D fast Fourier transform. +// +// Computes the inverse 3-dimensional discrete Fourier transform over the +// inner-most 3 dimensions of `input`. +// +// Arguments: +// input: A complex64 tensor. +// +// Returns A complex64 tensor of the same shape as `input`. The inner-most 3 +// dimensions of `input` are replaced with their inverse 3D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifftn with 3 dimensions. +// @end_compatibility +func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT3D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingRMSPropParametersGradAccumDebug. +type LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load RMSProp embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the RMSProp optimization algorithm. +// ms: Value of ms used in the RMSProp optimization algorithm. +// mom: Value of mom used in the RMSProp optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingRMSPropParametersGradAccumDebug", + Input: []tf.Input{ + parameters, ms, mom, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// AsStringAttr is an optional argument to AsString. +type AsStringAttr func(optionalAttr) + +// AsStringPrecision sets the optional precision attribute to value. +// +// value: The post-decimal precision to use for floating point numbers. +// Only used if precision > -1. +// If not specified, defaults to -1 +func AsStringPrecision(value int64) AsStringAttr { + return func(m optionalAttr) { + m["precision"] = value + } +} + +// AsStringScientific sets the optional scientific attribute to value. +// +// value: Use scientific notation for floating point numbers. +// If not specified, defaults to false +func AsStringScientific(value bool) AsStringAttr { + return func(m optionalAttr) { + m["scientific"] = value + } +} + +// AsStringShortest sets the optional shortest attribute to value. +// +// value: Use shortest representation (either scientific or standard) for +// floating point numbers. +// If not specified, defaults to false +func AsStringShortest(value bool) AsStringAttr { + return func(m optionalAttr) { + m["shortest"] = value + } +} + +// AsStringWidth sets the optional width attribute to value. +// +// value: Pad pre-decimal numbers to this width. +// Applies to both floating point and integer numbers. +// Only used if width > -1. +// If not specified, defaults to -1 +func AsStringWidth(value int64) AsStringAttr { + return func(m optionalAttr) { + m["width"] = value + } +} + +// AsStringFill sets the optional fill attribute to value. +// +// value: The value to pad if width > -1. If empty, pads with spaces. +// Another typical value is '0'. String cannot be longer than 1 character. +// If not specified, defaults to "" +func AsStringFill(value string) AsStringAttr { + return func(m optionalAttr) { + m["fill"] = value + } +} + +// Converts each entry in the given tensor to strings. Supports many numeric +// +// types and boolean. +func AsString(scope *Scope, input tf.Output, optional ...AsStringAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AsString", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringSplitAttr is an optional argument to StringSplit. +type StringSplitAttr func(optionalAttr) + +// StringSplitSkipEmpty sets the optional skip_empty attribute to value. +// +// value: A `bool`. If `True`, skip the empty strings from the result. +// If not specified, defaults to true +func StringSplitSkipEmpty(value bool) StringSplitAttr { + return func(m optionalAttr) { + m["skip_empty"] = value + } +} + +// Split elements of `input` based on `delimiter` into a `SparseTensor`. +// +// Let N be the size of source (typically N will be the batch size). Split each +// element of `input` based on `delimiter` and return a `SparseTensor` +// containing the splitted tokens. Empty tokens are ignored. +// +// `delimiter` can be empty, or a string of split characters. If `delimiter` is an +// empty string, each element of `input` is split into individual single-byte +// character strings, including splitting of UTF-8 multibyte sequences. Otherwise +// every character of `delimiter` is a potential split point. +// +// For example: +// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output +// will be +// +// indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// shape = [2, 3] +// values = ['hello', 'world', 'a', 'b', 'c'] +// +// Arguments: +// input: 1-D. Strings to split. +// delimiter: 0-D. Delimiter characters (bytes), or empty string. +// +// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse +// tensor, where the first value is N and the second value is the maximum number +// of tokens in a single input entry. +func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringSplit", + Input: []tf.Input{ + input, delimiter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor. +// +// This operation folds the padded areas of `input` by `MirrorPad` according to the +// `paddings` you specify. `paddings` must be the same as `paddings` argument +// given to the corresponding `MirrorPad` op. +// +// The folded size of each dimension D of the output is: +// +// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. +// # 'paddings' is [[0, 1]], [0, 1]]. +// # 'mode' is SYMMETRIC. +// # rank of 't' is 2. +// pad(t, paddings) ==> [[ 1, 5] +// [11, 28]] +// ``` +// +// Arguments: +// input: The input tensor to be folded. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// mode: The mode used in the `MirrorPad` op. +// +// Returns The folded tensor. +func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode} + opspec := tf.OpSpec{ + Type: "MirrorPadGrad", + Input: []tf.Input{ + input, paddings, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to LoadTPUEmbeddingStochasticGradientDescentParameters. +type LoadTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func LoadTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingStochasticGradientDescentParametersTableName(value string) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Load SGD embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the stochastic gradient descent optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, parameters tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingStochasticGradientDescentParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingStochasticGradientDescentParameters", + Input: []tf.Input{ + parameters, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedConv2DAttr is an optional argument to QuantizedConv2D. +type QuantizedConv2DAttr func(optionalAttr) + +// QuantizedConv2DOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedConv2DDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2D convolution given quantized 4D input and filter tensors. +// +// The inputs are quantized tensors where the lowest value represents the real +// number of the associated minimum, and the highest represents the maximum. +// This means that you can only interpret the quantized output in the same way, by +// taking the returned minimum and maximum values into account. +// +// Arguments: +// +// filter: filter's input_depth dimension must match input's depth dimensions. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// min_filter: The float value that the lowest quantized filter value represents. +// max_filter: The float value that the highest quantized filter value represents. +// strides: The stride of the sliding window for each dimension of the input +// tensor. +// padding: The type of padding algorithm to use. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedConv2D", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// RandomStandardNormalAttr is an optional argument to RandomStandardNormal. +type RandomStandardNormalAttr func(optionalAttr) + +// RandomStandardNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomStandardNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with random normal values. +func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomStandardNormal", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Decodes a `variant` Tensor into a `RaggedTensor`. +// +// Decodes the given `variant` Tensor and returns a `RaggedTensor`. The input +// could be a scalar, meaning it encodes a single `RaggedTensor` with ragged_rank +// `output_ragged_rank`. It could also have an arbitrary rank, in which case each +// element is decoded into a `RaggedTensor` with ragged_rank `input_ragged_rank` +// and these are then stacked according to the input shape to output a single +// `RaggedTensor` with ragged_rank `output_ragged_rank`. Each `variant` element in +// the input Tensor is decoded by retrieving from the element a 1-D `variant` +// Tensor with `input_ragged_rank + 1` Tensors, corresponding to the splits and +// values of the decoded `RaggedTensor`. If `input_ragged_rank` is -1, then it is +// inferred as `output_ragged_rank` - `rank(encoded_ragged)`. See +// `RaggedTensorToVariant` for the corresponding encoding logic. +// +// +// Arguments: +// encoded_ragged: A `variant` Tensor containing encoded `RaggedTensor`s. +// input_ragged_rank: The ragged rank of each encoded `RaggedTensor` component in the input. If set to +// -1, this is inferred as `output_ragged_rank` - `rank(encoded_ragged)` +// output_ragged_rank: The expected ragged rank of the output `RaggedTensor`. The following must hold: +// `output_ragged_rank = rank(encoded_ragged) + input_ragged_rank`. +// +// +// +// Returns A list of one or more Tensors representing the splits of the output +// `RaggedTensor`.A Tensor representing the values of the output `RaggedTensor`. +func RaggedTensorFromVariant(scope *Scope, encoded_ragged tf.Output, input_ragged_rank int64, output_ragged_rank int64, Tvalues tf.DataType, Tsplits tf.DataType) (output_nested_splits []tf.Output, output_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_ragged_rank": input_ragged_rank, "output_ragged_rank": output_ragged_rank, "Tvalues": Tvalues, "Tsplits": Tsplits} + opspec := tf.OpSpec{ + Type: "RaggedTensorFromVariant", + Input: []tf.Input{ + encoded_ragged, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { + scope.UpdateErr("RaggedTensorFromVariant", err) + return + } + output_dense_values = op.Output(idx) + return output_nested_splits, output_dense_values +} + +// CollectiveReduceAttr is an optional argument to CollectiveReduce. +type CollectiveReduceAttr func(optionalAttr) + +// CollectiveReduceWaitFor sets the optional wait_for attribute to value. +// If not specified, defaults to <> +func CollectiveReduceWaitFor(value []int64) CollectiveReduceAttr { + return func(m optionalAttr) { + m["wait_for"] = value + } +} + +// Mutually reduces multiple tensors of identical type and shape. +func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64, optional ...CollectiveReduceAttr) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CollectiveReduce", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingSparseBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseBatch. +type EnqueueTPUEmbeddingSparseBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingSparseBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingSparseBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// EnqueueTPUEmbeddingSparseBatchCombiners sets the optional combiners attribute to value. +// +// value: A list of string scalars, one for each embedding table that specify +// how to normalize the embedding activations after weighted summation. +// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have +// the sum of the weights be 0 for 'mean' or the sum of the squared weights be +// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for +// all tables. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseBatchCombiners(value []string) EnqueueTPUEmbeddingSparseBatchAttr { + return func(m optionalAttr) { + m["combiners"] = value + } +} + +// An op that enqueues TPUEmbedding input indices from a SparseTensor. +// +// This Op eases the porting of code that uses embedding_lookup_sparse(), +// although some Python preprocessing of the SparseTensor arguments to +// embedding_lookup_sparse() is required to produce the arguments to this Op, +// since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training +// step. +// +// The tensors at corresponding positions in the three input lists +// must have the same shape, i.e. rank 1 with dim_size() equal to the total +// number of lookups into the table described by the corresponding table_id. +// +// Arguments: +// sample_indices: A list of rank 1 Tensors specifying the training example and +// feature to which the corresponding embedding_indices and aggregation_weights +// values belong. sample_indices[i] must equal b * nf + f, where nf is the +// number of features from the corresponding table, f is in [0, nf), and +// b is in [0, batch size). +// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. +// aggregation_weights: A list of rank 1 Tensors containing per sample -- i.e. per +// (training example, feature) -- aggregation weights. +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// +// Returns the created operation. +func EnqueueTPUEmbeddingSparseBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingSparseBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingSparseBatch", + Input: []tf.Input{ + tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Decode web-safe base64-encoded strings. +// +// Input may or may not have padding at the end. See EncodeBase64 for padding. +// Web-safe means that input must use - and _ instead of + and /. +// +// Arguments: +// input: Base64 strings to decode. +// +// Returns Decoded strings. +func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DecodeBase64", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceScatterNdSubAttr is an optional argument to ResourceScatterNdSub. +type ResourceScatterNdSubAttr func(optionalAttr) + +// ResourceScatterNdSubUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdSubUseLocking(value bool) ResourceScatterNdSubAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse subtraction to individual values or slices in a Variable. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] +// ``` +// +// For example, say we want to subtract 4 scattered elements from a rank-1 tensor +// with 8 elements. In Python, that subtraction would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// sub = tf.scatter_nd_sub(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(sub) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, -9, 3, -6, -4, 6, 7, -4] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdSub(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdSubAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdSub", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse. +type SparseReduceSumSparseAttr func(optionalAttr) + +// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a +// SparseTensor. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceSumSparse", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + // CudnnRNNBackpropV2Attr is an optional argument to CudnnRNNBackpropV2. type CudnnRNNBackpropV2Attr func(optionalAttr) @@ -12611,2969 +28490,17 @@ func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...Ran return op.Output(0) } -// Concatenates a list of `SparseTensor` along the specified dimension. -// -// Concatenation is with respect to the dense versions of these sparse tensors. -// It is assumed that each input is a `SparseTensor` whose elements are ordered -// along increasing dimension number. -// -// All inputs' shapes must match, except for the concat dimension. The -// `indices`, `values`, and `shapes` lists must have the same length. -// -// The output shape is identical to the inputs', except along the concat -// dimension, where it is the sum of the inputs' sizes along that dimension. -// -// The output elements will be resorted to preserve the sort order along -// increasing dimension number. -// -// This op runs in `O(M log M)` time, where `M` is the total number of non-empty -// values across all inputs. This is due to the need for an internal sort in -// order to concatenate efficiently across an arbitrary dimension. -// -// For example, if `concat_dim = 1` and the inputs are -// -// sp_inputs[0]: shape = [2, 3] -// [0, 2]: "a" -// [1, 0]: "b" -// [1, 1]: "c" -// -// sp_inputs[1]: shape = [2, 4] -// [0, 1]: "d" -// [0, 2]: "e" -// -// then the output will be -// -// shape = [2, 7] -// [0, 2]: "a" -// [0, 4]: "d" -// [0, 5]: "e" -// [1, 0]: "b" -// [1, 1]: "c" -// -// Graphically this is equivalent to doing -// -// [ a] concat [ d e ] = [ a d e ] -// [b c ] [ ] [b c ] -// -// Arguments: -// indices: 2-D. Indices of each input `SparseTensor`. -// values: 1-D. Non-empty values of each `SparseTensor`. -// shapes: 1-D. Shapes of each `SparseTensor`. -// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), -// where rank is the number of dimensions in each input `SparseTensor`. -// -// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. -func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"concat_dim": concat_dim} - opspec := tf.OpSpec{ - Type: "SparseConcat", - Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Split a `SparseTensor` into `num_split` tensors along one dimension. -// -// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices -// `[0 : shape[split_dim] % num_split]` gets one extra dimension. -// For example, if `split_dim = 1` and `num_split = 2` and the input is -// -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] -// -// Graphically the output tensors are: -// -// output_tensor[0] = shape = [2, 4] -// [ a ] -// [b c ] -// -// output_tensor[1] = shape = [2, 3] -// [ d e ] -// [ ] -// -// Arguments: -// split_dim: 0-D. The dimension along which to split. Must be in the range -// `[0, rank(shape))`. -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. -// num_split: The number of ways to split. -// -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_split": num_split} - opspec := tf.OpSpec{ - Type: "SparseSplit", - Input: []tf.Input{ - split_dim, indices, values, shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil { - scope.UpdateErr("SparseSplit", err) - return - } - if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil { - scope.UpdateErr("SparseSplit", err) - return - } - if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil { - scope.UpdateErr("SparseSplit", err) - return - } - return output_indices, output_values, output_shape -} - -// Outputs random integers from a uniform distribution. -// -// The generated values are uniform integers in the range `[minval, maxval)`. -// The lower bound `minval` is included in the range, while the upper bound -// `maxval` is excluded. -// -// The random integers are slightly biased unless `maxval - minval` is an exact -// power of two. The bias is small for values of `maxval - minval` significantly -// smaller than the range of the output (either `2^32` or `2^64`). -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// shape: The shape of the output tensor. -// minval: Minimum value (inclusive, scalar). -// maxval: Maximum value (exclusive, scalar). -// -// Returns Random values with specified shape. -func StatefulUniformInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "StatefulUniformInt", - Input: []tf.Input{ - resource, algorithm, shape, minval, maxval, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that emits the records from one or more binary files. -// -// Arguments: -// filenames: A scalar or a vector containing the name(s) of the file(s) to be -// read. -// header_bytes: A scalar representing the number of bytes to skip at the -// beginning of a file. -// record_bytes: A scalar representing the number of bytes in each record. -// footer_bytes: A scalar representing the number of bytes to skip at the end -// of a file. -// buffer_size: A scalar representing the number of bytes to buffer. Must be > 0. -func FixedLengthRecordDataset(scope *Scope, filenames tf.Output, header_bytes tf.Output, record_bytes tf.Output, footer_bytes tf.Output, buffer_size tf.Output) (handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FixedLengthRecordDataset", - Input: []tf.Input{ - filenames, header_bytes, record_bytes, footer_bytes, buffer_size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatefulTruncatedNormalAttr is an optional argument to StatefulTruncatedNormal. -type StatefulTruncatedNormalAttr func(optionalAttr) - -// StatefulTruncatedNormalDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatefulTruncatedNormalDtype(value tf.DataType) StatefulTruncatedNormalAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random values from a truncated normal distribution. -// -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// shape: The shape of the output tensor. -// -// Returns Random values with specified shape. -func StatefulTruncatedNormal(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulTruncatedNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatefulTruncatedNormal", - Input: []tf.Input{ - resource, algorithm, shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StackV2Attr is an optional argument to StackV2. -type StackV2Attr func(optionalAttr) - -// StackV2StackName sets the optional stack_name attribute to value. -// -// value: Overrides the name used for the temporary stack resource. Default -// value is the name of the 'Stack' op (which is guaranteed unique). -// If not specified, defaults to "" -func StackV2StackName(value string) StackV2Attr { - return func(m optionalAttr) { - m["stack_name"] = value - } -} - -// A stack that produces elements in first-in last-out order. -// -// Arguments: -// max_size: The maximum size of the stack if non-negative. If negative, the stack -// size is unlimited. -// elem_type: The type of the elements on the stack. -// -// Returns The handle to the stack. -func StackV2(scope *Scope, max_size tf.Output, elem_type tf.DataType, optional ...StackV2Attr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"elem_type": elem_type} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StackV2", - Input: []tf.Input{ - max_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatefulUniformFullIntAttr is an optional argument to StatefulUniformFullInt. -type StatefulUniformFullIntAttr func(optionalAttr) - -// StatefulUniformFullIntDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_UINT64 -func StatefulUniformFullIntDtype(value tf.DataType) StatefulUniformFullIntAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random integers from a uniform distribution. -// -// The generated values are uniform integers covering the whole range of `dtype`. -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// shape: The shape of the output tensor. -// -// Returns Random values with specified shape. -func StatefulUniformFullInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformFullIntAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatefulUniformFullInt", - Input: []tf.Input{ - resource, algorithm, shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Execute a sub graph on a remote processor. -// -// The graph specifications(such as graph itself, input tensors and output names) -// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo -// as serialized_remote_fused_graph_execute_info. -// The specifications will be passed to a dedicated registered -// remote fused graph executor. The executor will send the graph specifications -// to a remote processor and execute that graph. The execution results -// will be passed to consumer nodes as outputs of this node. -// -// Arguments: -// inputs: Arbitrary number of tensors with arbitrary data types -// -// serialized_remote_fused_graph_execute_info: Serialized protocol buffer -// of RemoteFusedGraphExecuteInfo which contains graph specifications. -// -// Returns Arbitrary number of tensors with arbitrary data types -func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} - opspec := tf.OpSpec{ - Type: "RemoteFusedGraphExecute", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("RemoteFusedGraphExecute", err) - return - } - return outputs -} - -// Returns an element-wise indication of the sign of a number. -// -// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`. -// -// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`. -func Sign(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Sign", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that contains the elements of `input_dataset` ignoring errors. -func ExperimentalIgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalIgnoreErrorsDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse real-valued fast Fourier transform. -// -// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most dimension of `input`. -// -// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the -// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If -// `fft_length` is not provided, it is computed from the size of the inner-most -// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to -// compute `input` is odd, it should be provided since it cannot be inferred -// properly. -// -// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller -// than the corresponding dimension of `input`, the dimension is cropped. If it is -// larger, the dimension is padded with zeros. -// -// Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [1]. The FFT length. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most -// dimension of `input` is replaced with the `fft_length` samples of its inverse -// 1D Fourier transform. +// Returns which elements of x are Inf. // // @compatibility(numpy) -// Equivalent to np.fft.irfft +// Equivalent to np.isinf // @end_compatibility -func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "IRFFT", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] = min(ref[indices, ...], updates[...]) -// -// # Vector indices (for each i) -// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions are combined. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. -// -// Returns the created operation. -func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ResourceScatterMin", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp. -type ResourceApplyRMSPropAttr func(optionalAttr) - -// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, ms, and mom tensors is protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the RMSProp algorithm. -// -// Note that in dense implementation of this algorithm, ms and mom will -// update even if the grad is zero, but in this sparse implementation, ms -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) -// -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom -// -// Arguments: -// var_: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyRMSProp", - Input: []tf.Input{ - var_, ms, mom, lr, rho, momentum, epsilon, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Computes the gradient for the inverse of `x` wrt its input. -// -// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` -// is the corresponding input gradient. -func InvGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "InvGrad", - Input: []tf.Input{ - y, dy, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Divides sparse updates into the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] /= updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] /= updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions multiply. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. -// -// Returns the created operation. -func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ResourceScatterDiv", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// Subtracts a value from the current value of a variable. -// -// Any ReadVariableOp with a control dependency on this op is guaranteed to -// see the decremented value or a subsequent newer one. -// -// Arguments: -// resource: handle to the resource in which to store the variable. -// value: the value by which the variable will be incremented. -// -// Returns the created operation. -func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AssignSubVariableOp", - Input: []tf.Input{ - resource, value, - }, - } - return scope.AddOperation(opspec) -} - -// ParameterizedTruncatedNormalAttr is an optional argument to ParameterizedTruncatedNormal. -type ParameterizedTruncatedNormalAttr func(optionalAttr) - -// ParameterizedTruncatedNormalSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func ParameterizedTruncatedNormalSeed(value int64) ParameterizedTruncatedNormalAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// ParameterizedTruncatedNormalSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func ParameterizedTruncatedNormalSeed2(value int64) ParameterizedTruncatedNormalAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Outputs random values from a normal distribution. The parameters may each be a -// -// scalar which applies to the entire output, or a vector of length shape[0] which -// stores the parameters for each batch. -// -// Arguments: -// shape: The shape of the output tensor. Batches are indexed by the 0th dimension. -// means: The mean parameter of each batch. -// stdevs: The standard deviation parameter of each batch. Must be greater than 0. -// minvals: The minimum cutoff. May be -infinity. -// maxvals: The maximum cutoff. May be +infinity, and must be more than the minval -// for each batch. -// -// Returns A matrix of shape num_batches x samples_per_batch, filled with random -// truncated normal values using the parameters for each row. -func ParameterizedTruncatedNormal(scope *Scope, shape tf.Output, means tf.Output, stdevs tf.Output, minvals tf.Output, maxvals tf.Output, optional ...ParameterizedTruncatedNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ParameterizedTruncatedNormal", - Input: []tf.Input{ - shape, means, stdevs, minvals, maxvals, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Outputs a `Summary` protocol buffer with a histogram. -// -// The generated -// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) -// has one summary value containing a histogram for `values`. -// -// This op reports an `InvalidArgument` error if any value is not finite. -// -// Arguments: -// tag: Scalar. Tag to use for the `Summary.Value`. -// values: Any shape. Values to use to build the histogram. -// -// Returns Scalar. Serialized `Summary` protocol buffer. -func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "HistogramSummary", - Input: []tf.Input{ - tag, values, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Concatenates tensors along one dimension. -// -// Arguments: -// values: List of `N` Tensors to concatenate. Their ranks and types must match, -// and their sizes must match in all dimensions except `concat_dim`. -// axis: 0-D. The dimension along which to concatenate. Must be in the -// range [-rank(values), rank(values)). -// -// Returns A `Tensor` with the concatenation of values stacked along the -// `concat_dim` dimension. This tensor's shape matches that of `values` except -// in `concat_dim` where it has the sum of the sizes. -func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ConcatV2", - Input: []tf.Input{ - tf.OutputList(values), axis, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool. -type FractionalAvgPoolAttr func(optionalAttr) - -// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value. -// -// value: When set to True, generates the pooling sequence in a -// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin -// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for -// difference between pseudorandom and random. -// If not specified, defaults to false -func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr { - return func(m optionalAttr) { - m["pseudo_random"] = value - } -} - -// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value. -// -// value: When set to True, it means when pooling, the values at the boundary -// of adjacent pooling cells are used by both cells. For example: -// -// `index 0 1 2 3 4` -// -// `value 20 5 16 3 7` -// -// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. -// The result would be [41/3, 26/3] for fractional avg pooling. -// If not specified, defaults to false -func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr { - return func(m optionalAttr) { - m["overlapping"] = value - } -} - -// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value. -// -// value: When set to True, a fixed pooling region will be used when -// iterating over a FractionalAvgPool node in the computation graph. Mainly used -// in unit test to make FractionalAvgPool deterministic. -// If not specified, defaults to false -func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr { - return func(m optionalAttr) { - m["deterministic"] = value - } -} - -// FractionalAvgPoolSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Performs fractional average pooling on the input. -// -// Fractional average pooling is similar to Fractional max pooling in the pooling -// region generation step. The only difference is that after pooling regions are -// generated, a mean operation is performed instead of a max operation in each -// pooling region. -// -// Arguments: -// value: 4-D with shape `[batch, height, width, channels]`. -// pooling_ratio: Pooling ratio for each dimension of `value`, currently only -// supports row and col dimension and should be >= 1.0. For example, a valid -// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements -// must be 1.0 because we don't allow pooling on batch and channels -// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions -// respectively. -// -// Returns output tensor after fractional avg pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient. -func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FractionalAvgPool", - Input: []tf.Input{ - value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Adds a value to the current value of a variable. -// -// Any ReadVariableOp with a control dependency on this op is guaranteed to -// see the incremented value or a subsequent newer one. -// -// Arguments: -// resource: handle to the resource in which to store the variable. -// value: the value by which the variable will be incremented. -// -// Returns the created operation. -func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AssignAddVariableOp", - Input: []tf.Input{ - resource, value, - }, - } - return scope.AddOperation(opspec) -} - -// Multiplies sparse updates into the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] *= updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] *= updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions multiply. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. -// -// Returns the created operation. -func ResourceScatterMul(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ResourceScatterMul", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// RandomUniformIntAttr is an optional argument to RandomUniformInt. -type RandomUniformIntAttr func(optionalAttr) - -// RandomUniformIntSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomUniformIntSeed(value int64) RandomUniformIntAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomUniformIntSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomUniformIntSeed2(value int64) RandomUniformIntAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Outputs random integers from a uniform distribution. -// -// The generated values are uniform integers in the range `[minval, maxval)`. -// The lower bound `minval` is included in the range, while the upper bound -// `maxval` is excluded. -// -// The random integers are slightly biased unless `maxval - minval` is an exact -// power of two. The bias is small for values of `maxval - minval` significantly -// smaller than the range of the output (either `2^32` or `2^64`). -// -// Arguments: -// shape: The shape of the output tensor. -// minval: 0-D. Inclusive lower bound on the generated integers. -// maxval: 0-D. Exclusive upper bound on the generated integers. -// -// Returns A tensor of the specified shape filled with uniform random integers. -func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomUniformInt", - Input: []tf.Input{ - shape, minval, maxval, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that passes a sliding window over `input_dataset`. -// -// Arguments: -// -// window_size: A scalar representing the number of elements in the -// sliding window. -// window_shift: A scalar representing the steps moving the sliding window -// forward in one iteration. It must be positive. -// window_stride: A scalar representing the stride of the input elements of the sliding window. -// It must be positive. -// -// -func ExperimentalSlidingWindowDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, window_shift tf.Output, window_stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalSlidingWindowDataset", - Input: []tf.Input{ - input_dataset, window_size, window_shift, window_stride, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the minimum along segments of a tensor. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. -// -// Computes a tensor such that -// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such -// that `segment_ids[j] == i`. -// -// If the min is empty for a given segment ID `i`, `output[i] = 0`. -// -//
-// -//
-// -// For example: -// -// ``` -// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) -// tf.segment_min(c, tf.constant([0, 0, 1])) -// # ==> [[1, 2, 2, 1], -// # [5, 6, 7, 8]] -// ``` -// -// Arguments: -// -// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SegmentMin", - Input: []tf.Input{ - data, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// VarHandleOpAttr is an optional argument to VarHandleOp. -type VarHandleOpAttr func(optionalAttr) - -// VarHandleOpContainer sets the optional container attribute to value. -// -// value: the container this variable is placed in. -// If not specified, defaults to "" -func VarHandleOpContainer(value string) VarHandleOpAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// VarHandleOpSharedName sets the optional shared_name attribute to value. -// -// value: the name by which this variable is referred to. -// If not specified, defaults to "" -func VarHandleOpSharedName(value string) VarHandleOpAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Creates a handle to a Variable resource. -// -// Arguments: -// dtype: the type of this variable. Must agree with the dtypes -// of all ops using this variable. -// shape: The (possibly partially specified) shape of this variable. -func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype, "shape": shape} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "VarHandleOp", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SparseReduceSumAttr is an optional argument to SparseReduceSum. -type SparseReduceSumAttr func(optionalAttr) - -// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the sum of elements across dimensions of a SparseTensor. -// -// This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` -// instead of a sparse one. -// -// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained -// with length 1. -// -// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor -// with a single element is returned. Additionally, the axes can be negative, -// which are interpreted according to the indexing rules in Python. -// -// Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. -// -// Returns `R-K`-D. The reduced Tensor. -func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseReduceSum", - Input: []tf.Input{ - input_indices, input_values, input_shape, reduction_axes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. -type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) - -// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. -// -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// prox_v = var -// prox_v -= lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// -// Returns the created operation. -func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyProximalAdagrad", - Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, indices, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Aggregates the summary of accumulated stats for the batch. -// -// The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id. -// -// Arguments: -// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. -// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. -// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. -// feature_indices: int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]). -// Number of sparse entries across all instances from the batch. The first value is -// the index of the instance, the second is dimension of the feature. The second axis -// can only have 2 values, i.e., the input dense version of Tensor can only be matrix. -// feature_values: int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]). -// Number of sparse entries across all instances from the batch. The first value is -// the index of the instance, the second is dimension of the feature. -// feature_shape: int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]). -// The first axis can only have 2 values, [batch_size, feature_dimension]. -// max_splits: int; the maximum number of splits possible in the whole tree. -// num_buckets: int; equals to the maximum possible value of bucketized feature + 1. -// -// Returns int32; Rank 2 indices of summary sparse Tensors (shape=[number of non zero statistics, 4]) -// The second axis can only be 4 including node id, feature dimension, bucket id, and statistics_dimension. -// statistics_dimension = logits_dimension + hessian_dimension.output Rank 1 Tensor (shape=[number of non zero statistics])output Rank 1 Tensor (shape=[4]) -// The tensor has following 4 values: [max_splits, feature_dimension, num_buckets, statistics_dimension], -// where statistics_dimension = gradient_dimension + hessian_dimension. gradient_dimension -// is the same as label_dimension, i.e., the output space. hessian_dimension can be the same -// as logits dimension when diagonal hessian is used, or label_dimension^2 when full -// hessian is used. -func BoostedTreesSparseAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature_indices tf.Output, feature_values tf.Output, feature_shape tf.Output, max_splits int64, num_buckets int64) (stats_summary_indices tf.Output, stats_summary_values tf.Output, stats_summary_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "BoostedTreesSparseAggregateStats", - Input: []tf.Input{ - node_ids, gradients, hessians, feature_indices, feature_values, feature_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// LoadTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to LoadTPUEmbeddingMDLAdagradLightParameters. -type LoadTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingMDLAdagradLightParametersTableId(value int64) LoadTPUEmbeddingMDLAdagradLightParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingMDLAdagradLightParametersTableName(value string) LoadTPUEmbeddingMDLAdagradLightParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load MDL Adagrad Light embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the MDL Adagrad Light optimization algorithm. -// accumulators: Value of accumulators used in the MDL Adagrad Light optimization algorithm. -// weights: Value of weights used in the MDL Adagrad Light optimization algorithm. -// benefits: Value of benefits used in the MDL Adagrad Light optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingMDLAdagradLightParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMDLAdagradLightParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingMDLAdagradLightParameters", - Input: []tf.Input{ - parameters, accumulators, weights, benefits, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Eagerly executes a python function to compute func(input)->output. The -// -// semantics of the input, output, and attributes are the same as those for -// PyFunc. -func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"token": token, "Tout": Tout} - opspec := tf.OpSpec{ - Type: "EagerPyFunc", - Input: []tf.Input{ - tf.OutputList(input), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("EagerPyFunc", err) - return - } - return output -} - -// RpcAttr is an optional argument to Rpc. -type RpcAttr func(optionalAttr) - -// RpcProtocol sets the optional protocol attribute to value. -// -// value: RPC protocol to use. Empty string means use the default protocol. -// Options include 'grpc'. -// If not specified, defaults to "" -func RpcProtocol(value string) RpcAttr { - return func(m optionalAttr) { - m["protocol"] = value - } -} - -// RpcFailFast sets the optional fail_fast attribute to value. -// -// value: `boolean`. If `true` (default), then failures to connect -// (i.e., the server does not immediately respond) cause an RPC failure. -// If not specified, defaults to true -func RpcFailFast(value bool) RpcAttr { - return func(m optionalAttr) { - m["fail_fast"] = value - } -} - -// RpcTimeoutInMs sets the optional timeout_in_ms attribute to value. -// -// value: `int`. If `0` (default), then the kernel will run the RPC -// request and only time out if the RPC deadline passes or the session times out. -// If this value is greater than `0`, then the op will raise an exception if -// the RPC takes longer than `timeout_in_ms`. -// If not specified, defaults to 0 -func RpcTimeoutInMs(value int64) RpcAttr { - return func(m optionalAttr) { - m["timeout_in_ms"] = value - } -} - -// Perform batches of RPC requests. -// -// This op asynchronously performs either a single RPC request, or a batch -// of requests. RPC requests are defined by three main parameters: -// -// - `address` (the host+port or BNS address of the request) -// - `method` (the RPC method name for the request) -// - `request` (the serialized proto string, or vector of strings, -// of the RPC request argument). -// -// For example, if you have an RPC service running on port localhost:2345, -// and its interface is configured with the following proto declaration: -// -// ``` -// service MyService { -// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { -// } -// }; -// ``` -// -// then call this op with arguments: -// -// ``` -// address = "localhost:2345" -// method = "MyService/MyMethod" -// ``` -// -// The `request` tensor is a string tensor representing serialized `MyRequestProto` -// strings; and the output string tensor `response` will have the same shape -// and contain (upon successful completion) corresponding serialized -// `MyResponseProto` strings. -// -// For example, to send a single, empty, `MyRequestProto`, call -// this op with `request = ""`. To send 5 **parallel** empty requests, -// call this op with `request = ["", "", "", "", ""]`. -// -// More generally, one can create a batch of `MyRequestProto` serialized protos -// from regular batched tensors using the `encode_proto` op, and convert -// the response `MyResponseProto` serialized protos to batched tensors -// using the `decode_proto` op. -// -// **NOTE** Working with serialized proto strings is faster than instantiating -// actual proto objects in memory, so no performance degradation is expected -// compared to writing custom kernels for this workflow. -// -// If the connection fails or the remote worker returns an error -// status, the op reraises this exception locally. -// -// See the `TryRpc` op if you prefer to handle RPC failures manually in the graph. -// -// Arguments: -// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `method` and `request`. -// method: `0-D` or `1-D`. The method address on the RPC server. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `address` and `request`. -// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `address` and `method`. -// -// Returns Same shape as `request`. Serialized proto strings: the rpc responses. -func Rpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...RpcAttr) (response tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Rpc", - Input: []tf.Input{ - address, method, request, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes fingerprints of the input strings. -// -// Arguments: -// input: vector of strings to compute fingerprints on. -// -// Returns a (N,2) shaped matrix where N is the number of elements in the input -// vector. Each row contains the low and high parts of the fingerprint. -func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SdcaFprint", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. -type StatelessTruncatedNormalAttr func(optionalAttr) - -// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs deterministic pseudorandom values from a truncated normal distribution. -// -// The generated values follow a normal distribution with mean 0 and standard -// deviation 1, except that values whose magnitude is more than 2 standard -// deviations from the mean are dropped and re-picked. -// -// The outputs are a deterministic function of `shape` and `seed`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). -// -// Returns Random values with specified shape. -func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatelessTruncatedNormal", - Input: []tf.Input{ - shape, seed, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SdcaOptimizerAttr is an optional argument to SdcaOptimizer. -type SdcaOptimizerAttr func(optionalAttr) - -// SdcaOptimizerAdaptative sets the optional adaptative attribute to value. -// -// value: Whether to use Adaptive SDCA for the inner loop. -// If not specified, defaults to true -func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr { - return func(m optionalAttr) { - m["adaptative"] = value - } -} - -// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for -// -// linear models with L1 + L2 regularization. As global optimization objective is -// strongly-convex, the optimizer optimizes the dual objective at each step. The -// optimizer applies each update one example at a time. Examples are sampled -// uniformly, and the optimizer is learning rate free and enjoys linear convergence -// rate. -// -// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
-// Shai Shalev-Shwartz, Tong Zhang. 2012 -// -// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ -// -// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
-// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, -// Peter Richtarik, Martin Takac. 2015 -// -// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
-// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 -// -// Arguments: -// sparse_example_indices: a list of vectors which contain example indices. -// sparse_feature_indices: a list of vectors which contain feature indices. -// sparse_feature_values: a list of vectors which contains feature value -// associated with each feature group. -// dense_features: a list of matrices which contains the dense feature values. -// example_weights: a vector which contains the weight associated with each -// example. -// example_labels: a vector which contains the label/target associated with each -// example. -// sparse_indices: a list of vectors where each value is the indices which has -// corresponding weights in sparse_weights. This field maybe omitted for the -// dense approach. -// sparse_weights: a list of vectors where each value is the weight associated with -// a sparse feature group. -// dense_weights: a list of vectors where the values are the weights associated -// with a dense feature group. -// example_state_data: a list of vectors containing the example state data. -// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, -// squared and hinge losses. -// l1: Symmetric l1 regularization strength. -// l2: Symmetric l2 regularization strength. -// num_loss_partitions: Number of partitions of the global loss function. -// num_inner_iterations: Number of iterations per mini-batch. -// -// Returns a list of vectors containing the updated example state -// data.a list of vectors where each value is the delta -// weights associated with a sparse feature group.a list of vectors where the values are the delta -// weights associated with a dense feature group. -func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SdcaOptimizer", - Input: []tf.Input{ - tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - out_example_state_data = op.Output(idx) - if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { - scope.UpdateErr("SdcaOptimizer", err) - return - } - if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { - scope.UpdateErr("SdcaOptimizer", err) - return - } - return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights -} - -// Computes softplus gradients for a softplus operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding softplus operation. -// features: The features passed as input to the corresponding softplus operation. -// -// Returns The gradients: `gradients / (1 + exp(-features))`. -func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SoftplusGrad", - Input: []tf.Input{ - gradients, features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RecordInputAttr is an optional argument to RecordInput. -type RecordInputAttr func(optionalAttr) - -// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value. -// -// value: Random seeds used to produce randomized records. -// If not specified, defaults to 301 -func RecordInputFileRandomSeed(value int64) RecordInputAttr { - return func(m optionalAttr) { - m["file_random_seed"] = value - } -} - -// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value. -// -// value: Shifts the list of files after the list is randomly -// shuffled. -// If not specified, defaults to 0 -func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr { - return func(m optionalAttr) { - m["file_shuffle_shift_ratio"] = value - } -} - -// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value. -// -// value: The randomization shuffling buffer. -// If not specified, defaults to 10000 -func RecordInputFileBufferSize(value int64) RecordInputAttr { - return func(m optionalAttr) { - m["file_buffer_size"] = value - } -} - -// RecordInputFileParallelism sets the optional file_parallelism attribute to value. -// -// value: How many sstables are opened and concurrently iterated over. -// If not specified, defaults to 16 -func RecordInputFileParallelism(value int64) RecordInputAttr { - return func(m optionalAttr) { - m["file_parallelism"] = value - } -} - -// RecordInputBatchSize sets the optional batch_size attribute to value. -// -// value: The batch size. -// If not specified, defaults to 32 -func RecordInputBatchSize(value int64) RecordInputAttr { - return func(m optionalAttr) { - m["batch_size"] = value - } -} - -// RecordInputCompressionType sets the optional compression_type attribute to value. -// -// value: The type of compression for the file. Currently ZLIB and -// GZIP are supported. Defaults to none. -// If not specified, defaults to "" -func RecordInputCompressionType(value string) RecordInputAttr { - return func(m optionalAttr) { - m["compression_type"] = value - } -} - -// Emits randomized records. -// -// Arguments: -// file_pattern: Glob pattern for the data files. -// -// Returns A tensor of shape [batch_size]. -func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"file_pattern": file_pattern} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RecordInput", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Deprecated. Disallowed in GraphDef version >= 2. -// -// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead -func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AdjustContrast", - Input: []tf.Input{ - images, contrast_factor, min_value, max_value, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the element-wise min of two SparseTensors. -// -// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. -// -// Arguments: -// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, in the canonical lexicographic ordering. -// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. -// a_shape: 1-D. Shape of the input SparseTensor. -// b_indices: counterpart to `a_indices` for the other operand. -// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. -// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. -// -// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. -func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSparseMinimum", - Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// GatherV2Attr is an optional argument to GatherV2. -type GatherV2Attr func(optionalAttr) - -// GatherV2BatchDims sets the optional batch_dims attribute to value. -// If not specified, defaults to 0 -func GatherV2BatchDims(value int64) GatherV2Attr { - return func(m optionalAttr) { - m["batch_dims"] = value - } -} - -// Gather slices from `params` axis `axis` according to `indices`. -// -// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). -// Produces an output tensor with shape `params.shape[:axis] + indices.shape + -// params.shape[axis + 1:]` where: -// -// ```python -// # Scalar indices (output is rank(params) - 1). -// output[a_0, ..., a_n, b_0, ..., b_n] = -// params[a_0, ..., a_n, indices, b_0, ..., b_n] -// -// # Vector indices (output is rank(params)). -// output[a_0, ..., a_n, i, b_0, ..., b_n] = -// params[a_0, ..., a_n, indices[i], b_0, ..., b_n] -// -// # Higher rank indices (output is rank(params) + rank(indices) - 1). -// output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = -// params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] -// ``` -// -//
-// -//
-// -// Note that on CPU, if an out of bound index is found, an error is returned. -// On GPU, if an out of bound index is found, a 0 is stored in the -// corresponding output value. -// -// See also `tf.batch_gather` and `tf.gather_nd`. -// -// Arguments: -// params: The tensor from which to gather values. Must be at least rank -// `axis + 1`. -// indices: Index tensor. Must be in range `[0, params.shape[axis])`. -// axis: The axis in `params` to gather `indices` from. Defaults to the first -// dimension. Supports negative indexes. -// -// Returns Values from `params` gathered from indices given by `indices`, with -// shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`. -func GatherV2(scope *Scope, params tf.Output, indices tf.Output, axis tf.Output, optional ...GatherV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "GatherV2", - Input: []tf.Input{ - params, indices, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MutexV2Attr is an optional argument to MutexV2. -type MutexV2Attr func(optionalAttr) - -// MutexV2Container sets the optional container attribute to value. -// -// value: If non-empty, this variable is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutexV2Container(value string) MutexV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MutexV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this variable is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func MutexV2SharedName(value string) MutexV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Creates a Mutex resource that can be locked by `MutexLock`. -// -// Returns The mutex resource. -func MutexV2(scope *Scope, optional ...MutexV2Attr) (resource tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MutexV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Return the shape of s0 op s1 with broadcast. -// -// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the -// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. -func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BroadcastArgs", - Input: []tf.Input{ - s0, s1, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AvgPool3DAttr is an optional argument to AvgPool3D. -type AvgPool3DAttr func(optionalAttr) - -// AvgPool3DDataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func AvgPool3DDataFormat(value string) AvgPool3DAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs 3D average pooling on the input. -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -// -// Returns The average pooled output tensor. -func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AvgPool3D", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Decodes a `variant` Tensor into a `RaggedTensor`. -// -// Decodes the given `variant` Tensor and returns a `RaggedTensor`. The input -// could be a scalar, meaning it encodes a single `RaggedTensor` with ragged_rank -// `output_ragged_rank`. It could also have an arbitrary rank, in which case each -// element is decoded into a `RaggedTensor` with ragged_rank `input_ragged_rank` -// and these are then stacked according to the input shape to output a single -// `RaggedTensor` with ragged_rank `output_ragged_rank`. Each `variant` element in -// the input Tensor is decoded by retrieving from the element a 1-D `variant` -// Tensor with `input_ragged_rank + 1` Tensors, corresponding to the splits and -// values of the decoded `RaggedTensor`. If `input_ragged_rank` is -1, then it is -// inferred as `output_ragged_rank` - `rank(encoded_ragged)`. See -// `RaggedTensorToVariant` for the corresponding encoding logic. -// -// -// Arguments: -// encoded_ragged: A `variant` Tensor containing encoded `RaggedTensor`s. -// input_ragged_rank: The ragged rank of each encoded `RaggedTensor` component in the input. If set to -// -1, this is inferred as `output_ragged_rank` - `rank(encoded_ragged)` -// output_ragged_rank: The expected ragged rank of the output `RaggedTensor`. The following must hold: -// `output_ragged_rank = rank(encoded_ragged) + input_ragged_rank`. -// -// -// -// Returns A list of one or more Tensors representing the splits of the output -// `RaggedTensor`.A Tensor representing the values of the output `RaggedTensor`. -func RaggedTensorFromVariant(scope *Scope, encoded_ragged tf.Output, input_ragged_rank int64, output_ragged_rank int64, Tvalues tf.DataType, Tsplits tf.DataType) (output_nested_splits []tf.Output, output_dense_values tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"input_ragged_rank": input_ragged_rank, "output_ragged_rank": output_ragged_rank, "Tvalues": Tvalues, "Tsplits": Tsplits} - opspec := tf.OpSpec{ - Type: "RaggedTensorFromVariant", - Input: []tf.Input{ - encoded_ragged, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { - scope.UpdateErr("RaggedTensorFromVariant", err) - return - } - output_dense_values = op.Output(idx) - return output_nested_splits, output_dense_values -} - -// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. -type SparseToSparseSetOperationAttr func(optionalAttr) - -// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Applies set operation along last dimension of 2 `SparseTensor` inputs. -// -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. -// -// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the -// order and range of `set1` and `set2` indices. -// -// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, -// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same -// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. -// -// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, -// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same -// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. -// -// If `validate_indices` is `True`, this op validates the order and range of `set1` -// and `set2` indices. -// -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. -// -// Arguments: -// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must -// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the -// max set size across `0...n-1` dimensions. -// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must -// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the -// max set size across `0...n-1` dimensions. -// -// -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"set_operation": set_operation} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseToSparseSetOperation", - Input: []tf.Input{ - set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation. -type DenseToDenseSetOperationAttr func(optionalAttr) - -// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Applies set operation along last dimension of 2 `Tensor` inputs. -// -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. -// -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. -// -// Arguments: -// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. -// Dimension `n` contains values in a set, duplicates are allowed but ignored. -// set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`. -// Dimension `n` contains values in a set, duplicates are allowed but ignored. -// -// -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"set_operation": set_operation} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DenseToDenseSetOperation", - Input: []tf.Input{ - set1, set2, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. -type ResourceApplyProximalAdagradAttr func(optionalAttr) - -// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. -// -// accum += grad * grad -// prox_v = var - lr * grad * (1 / sqrt(accum)) -// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyProximalAdagrad", - Input: []tf.Input{ - var_, accum, lr, l1, l2, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Returns the number of records this Reader has produced. -// -// This is the same as the number of ReaderRead executions that have -// succeeded. -// -// Arguments: -// reader_handle: Handle to a Reader. -func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderNumRecordsProducedV2", - Input: []tf.Input{ - reader_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds sparse updates to the variable referenced by `resource`. -// -// This operation computes -// -// # Scalar indices -// ref[indices, ...] += updates[...] -// -// # Vector indices (for each i) -// ref[indices[i], ...] += updates[i, ...] -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions add. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
-// -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. -// -// Returns the created operation. -func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ResourceScatterAdd", - Input: []tf.Input{ - resource, indices, updates, - }, - } - return scope.AddOperation(opspec) -} - -// Deprecated. Use TensorArrayGradV3 -// -// DEPRECATED at GraphDef version 26: Use TensorArrayGradV3 -func TensorArrayGradV2(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"source": source} - opspec := tf.OpSpec{ - Type: "TensorArrayGradV2", - Input: []tf.Input{ - handle, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Greedily selects a subset of bounding boxes in descending order of score, -// -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system. Note that this -// algorithm is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// -// selected_indices = tf.image.non_max_suppression_v2( -// boxes, scores, max_output_size, iou_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) -// -// Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// iou_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too much with respect to IOU. -// -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppressionV2(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output) (selected_indices tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NonMaxSuppressionV2", - Input: []tf.Input{ - boxes, scores, max_output_size, iou_threshold, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Fills empty rows in the input 2-D `SparseTensor` with a default value. -// -// The input `SparseTensor` is represented via the tuple of inputs -// (`indices`, `values`, `dense_shape`). The output `SparseTensor` has the -// same `dense_shape` but with indices `output_indices` and values -// `output_values`. -// -// This op inserts a single entry for every row that doesn't have any values. -// The index is created as `[row, 0, ..., 0]` and the inserted value -// is `default_value`. -// -// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values: -// -// [0, 1]: a -// [0, 3]: b -// [2, 0]: c -// [3, 1]: d -// -// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values: -// -// [0, 1]: a -// [0, 3]: b -// [1, 0]: default_value -// [2, 0]: c -// [3, 1]: d -// [4, 0]: default_value -// -// The output `SparseTensor` will be in row-major order and will have the -// same shape as the input. -// -// This op also returns an indicator vector shaped `[dense_shape[0]]` such that -// -// empty_row_indicator[i] = True iff row i was an empty row. -// -// And a reverse index map vector shaped `[indices.shape[0]]` that is used during -// backpropagation, -// -// reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :] -// -// Arguments: -// indices: 2-D. the indices of the sparse tensor. -// values: 1-D. the values of the sparse tensor. -// dense_shape: 1-D. the shape of the sparse tensor. -// default_value: 0-D. default value to insert into location `[row, 0, ..., 0]` -// for rows missing from the input sparse tensor. -// output indices: 2-D. the indices of the filled sparse tensor. -// -// Returns 1-D. the values of the filled sparse tensor.1-D. whether the dense row was missing in the -// input sparse tensor.1-D. a map from the input indices to the output indices. -func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseFillEmptyRows", - Input: []tf.Input{ - indices, values, dense_shape, default_value, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// Saves the input tensors to disk. -// -// The size of `tensor_names` must match the number of tensors in `data`. `data[i]` -// is written to `filename` with name `tensor_names[i]`. -// -// See also `SaveSlices`. -// -// Arguments: -// filename: Must have a single element. The name of the file to which we write -// the tensor. -// tensor_names: Shape `[N]`. The names of the tensors to be saved. -// data: `N` tensors to save. -// -// Returns the created operation. -func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Save", - Input: []tf.Input{ - filename, tensor_names, tf.OutputList(data), - }, - } - return scope.AddOperation(opspec) -} - -// Gather slices from `params` into a Tensor with shape specified by `indices`. -// -// `indices` is an K-dimensional integer tensor, best thought of as a -// (K-1)-dimensional tensor of indices into `params`, where each element defines a -// slice of `params`: -// -// output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]] -// -// Whereas in `tf.gather` `indices` defines slices into the first -// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the -// first `N` dimensions of `params`, where `N = indices.shape[-1]`. -// -// The last dimension of `indices` can be at most the rank of -// `params`: -// -// indices.shape[-1] <= params.rank -// -// The last dimension of `indices` corresponds to elements -// (if `indices.shape[-1] == params.rank`) or slices -// (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]` -// of `params`. The output tensor has shape -// -// indices.shape[:-1] + params.shape[indices.shape[-1]:] -// -// Note that on CPU, if an out of bound index is found, an error is returned. -// On GPU, if an out of bound index is found, a 0 is stored in the -// corresponding output value. -// -// Some examples below. -// -// Simple indexing into a matrix: -// -// ```python -// indices = [[0, 0], [1, 1]] -// params = [['a', 'b'], ['c', 'd']] -// output = ['a', 'd'] -// ``` -// -// Slice indexing into a matrix: -// -// ```python -// indices = [[1], [0]] -// params = [['a', 'b'], ['c', 'd']] -// output = [['c', 'd'], ['a', 'b']] -// ``` -// -// Indexing into a 3-tensor: -// -// ```python -// indices = [[1]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = [[['a1', 'b1'], ['c1', 'd1']]] -// -// -// indices = [[0, 1], [1, 0]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = [['c0', 'd0'], ['a1', 'b1']] -// -// -// indices = [[0, 0, 1], [1, 0, 1]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = ['b0', 'b1'] -// ``` -// -// Batched indexing into a matrix: -// -// ```python -// indices = [[[0, 0]], [[0, 1]]] -// params = [['a', 'b'], ['c', 'd']] -// output = [['a'], ['b']] -// ``` -// -// Batched slice indexing into a matrix: -// -// ```python -// indices = [[[1]], [[0]]] -// params = [['a', 'b'], ['c', 'd']] -// output = [[['c', 'd']], [['a', 'b']]] -// ``` -// -// Batched indexing into a 3-tensor: -// -// ```python -// indices = [[[1]], [[0]]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = [[[['a1', 'b1'], ['c1', 'd1']]], -// [[['a0', 'b0'], ['c0', 'd0']]]] -// -// indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = [[['c0', 'd0'], ['a1', 'b1']], -// [['a0', 'b0'], ['c1', 'd1']]] -// -// -// indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]] -// params = [[['a0', 'b0'], ['c0', 'd0']], -// [['a1', 'b1'], ['c1', 'd1']]] -// output = [['b0', 'b1'], ['d0', 'c1']] -// ``` -// -// See also `tf.gather` and `tf.batch_gather`. -// -// Arguments: -// params: The tensor from which to gather values. -// indices: Index tensor. -// -// Returns Values from `params` gathered from indices given by `indices`, with -// shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`. -func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "GatherNd", - Input: []tf.Input{ - params, indices, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds up a SparseTensor and a dense Tensor, using these special rules: -// -// (1) Broadcasts the dense side to have the same shape as the sparse side, if -// eligible; -// (2) Then, only the dense values pointed to by the indices of the SparseTensor -// participate in the cwise addition. -// -// By these rules, the result is a logical SparseTensor with exactly the same -// indices and shape, but possibly with different non-zero values. The output of -// this Op is the resultant non-zero values. -// -// Arguments: -// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// dense: `R`-D. The dense Tensor operand. -// -// Returns 1-D. The `N` values that are operated on. -func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseDenseCwiseAdd", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, dense, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Shuffle dimensions of x according to a permutation and conjugate the result. -// -// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: -// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` -// `y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])` -func ConjugateTranspose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ConjugateTranspose", - Input: []tf.Input{ - x, perm, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Slice a `SparseTensor` based on the `start` and `size`. -// -// For example, if the input is -// -// input_tensor = shape = [2, 7] -// [ a d e ] -// [b c ] -// -// Graphically the output tensors are: -// -// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] -// [ a ] -// [b c ] -// -// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] -// [ d e ] -// [ ] -// -// Arguments: -// indices: 2-D tensor represents the indices of the sparse tensor. -// values: 1-D tensor represents the values of the sparse tensor. -// shape: 1-D. tensor represents the shape of the sparse tensor. -// start: 1-D. tensor represents the start of the slice. -// size: 1-D. tensor represents the size of the slice. -// output indices: A list of 1-D tensors represents the indices of the output -// sparse tensors. -// -// Returns A list of 1-D tensors represents the values of the output sparse -// tensors.A list of 1-D tensors represents the shape of the output sparse -// tensors. -func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSlice", - Input: []tf.Input{ - indices, values, shape, start, size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// The gradient operator for the SparseSlice op. -// -// This op takes in the upstream gradient w.r.t. non-empty values of -// the sliced `SparseTensor`, and outputs the gradients w.r.t. -// the non-empty values of input `SparseTensor`. -// -// Arguments: -// backprop_val_grad: 1-D. The gradient with respect to -// the non-empty values of the sliced `SparseTensor`. -// input_indices: 2-D. The `indices` of the input `SparseTensor`. -// input_start: 1-D. tensor represents the start of the slice. -// output_indices: 2-D. The `indices` of the sliced `SparseTensor`. -// -// Returns 1-D. The gradient with respect to the non-empty values of input `SparseTensor`. -func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf.Output, input_start tf.Output, output_indices tf.Output) (val_grad tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSliceGrad", - Input: []tf.Input{ - backprop_val_grad, input_indices, input_start, output_indices, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SparseToDenseAttr is an optional argument to SparseToDense. -type SparseToDenseAttr func(optionalAttr) - -// SparseToDenseValidateIndices sets the optional validate_indices attribute to value. -// -// value: If true, indices are checked to make sure they are sorted in -// lexicographic order and that there are no repeats. -// If not specified, defaults to true -func SparseToDenseValidateIndices(value bool) SparseToDenseAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Converts a sparse representation into a dense tensor. -// -// Builds an array `dense` with shape `output_shape` such that -// -// ``` -// # If sparse_indices is scalar -// dense[i] = (i == sparse_indices ? sparse_values : default_value) -// -// # If sparse_indices is a vector, then for each i -// dense[sparse_indices[i]] = sparse_values[i] -// -// # If sparse_indices is an n by d matrix, then for each i in [0, n) -// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] -// ``` -// -// All other values in `dense` are set to `default_value`. If `sparse_values` is a -// scalar, all sparse indices are set to this single value. -// -// Indices should be sorted in lexicographic order, and indices must not -// contain any repeats. If `validate_indices` is true, these properties -// are checked during execution. -// -// Arguments: -// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete -// index where `sparse_values[i]` will be placed. -// output_shape: 1-D. Shape of the dense output tensor. -// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, -// or a scalar value to be used for all sparse indices. -// default_value: Scalar value to set for indices not specified in -// `sparse_indices`. -// -// Returns Dense output tensor of shape `output_shape`. -func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseToDense", - Input: []tf.Input{ - sparse_indices, output_shape, sparse_values, default_value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PrefetchDatasetAttr is an optional argument to PrefetchDataset. -type PrefetchDatasetAttr func(optionalAttr) - -// PrefetchDatasetSlackPeriod sets the optional slack_period attribute to value. -// If not specified, defaults to 0 -func PrefetchDatasetSlackPeriod(value int64) PrefetchDatasetAttr { - return func(m optionalAttr) { - m["slack_period"] = value - } -} - -// Creates a dataset that asynchronously prefetches elements from `input_dataset`. -// -// Arguments: -// -// buffer_size: The maximum number of elements to buffer in an iterator over -// this dataset. -// -// -func PrefetchDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...PrefetchDatasetAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "PrefetchDataset", - Input: []tf.Input{ - input_dataset, buffer_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes rectified linear: `max(features, 0)`. -func Relu(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Relu", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedConv2DAttr is an optional argument to QuantizedConv2D. -type QuantizedConv2DAttr func(optionalAttr) - -// QuantizedConv2DOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QINT32 -func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// QuantizedConv2DDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each -// filter element on that dimension. The dimension order is determined by the -// value of `data_format`, see above for details. Dilations in the batch and -// depth dimensions must be 1. -// If not specified, defaults to -func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes a 2D convolution given quantized 4D input and filter tensors. -// -// The inputs are quantized tensors where the lowest value represents the real -// number of the associated minimum, and the highest represents the maximum. -// This means that you can only interpret the quantized output in the same way, by -// taking the returned minimum and maximum values into account. -// -// Arguments: -// -// filter: filter's input_depth dimension must match input's depth dimensions. -// min_input: The float value that the lowest quantized input value represents. -// max_input: The float value that the highest quantized input value represents. -// min_filter: The float value that the lowest quantized filter value represents. -// max_filter: The float value that the highest quantized filter value represents. -// strides: The stride of the sliding window for each dimension of the input -// tensor. -// padding: The type of padding algorithm to use. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedConv2D", - Input: []tf.Input{ - input, filter, min_input, max_input, min_filter, max_filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Deserialize and concatenate `SparseTensors` from a serialized minibatch. -// -// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where -// `N` is the minibatch size and the rows correspond to packed outputs of -// `SerializeSparse`. The ranks of the original `SparseTensor` objects -// must all match. When the final `SparseTensor` is created, it has rank one -// higher than the ranks of the incoming `SparseTensor` objects -// (they have been concatenated along a new row dimension). -// -// The output `SparseTensor` object's shape values for all dimensions but the -// first are the max across the input `SparseTensor` objects' shape values -// for the corresponding dimensions. Its first shape value is `N`, the minibatch -// size. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the serialized input is a `[2 x 3]` matrix representing two -// original `SparseTensor` objects: -// -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// -// and -// -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] -// -// then the final deserialized `SparseTensor` will be: -// -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] -// -// Arguments: -// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. -// Must have 3 columns. -// dtype: The `dtype` of the serialized `SparseTensor` objects. -func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - opspec := tf.OpSpec{ - Type: "DeserializeManySparse", - Input: []tf.Input{ - serialized_sparse, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Return a slice from 'input'. -// -// The output tensor is a tensor with dimensions described by 'size' -// whose values are extracted from 'input' starting at the offsets in -// 'begin'. -// -// *Requirements*: -// 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) -// -// Arguments: -// -// begin: begin[i] specifies the offset into the 'i'th dimension of -// 'input' to slice from. -// size: size[i] specifies the number of elements of the 'i'th dimension -// of 'input' to slice. If size[i] is -1, all remaining elements in dimension -// i are included in the slice (i.e. this is equivalent to setting -// size[i] = input.dim_size(i) - begin[i]). -func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Slice", - Input: []tf.Input{ - input, begin, size, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Deserialize `SparseTensor` objects. -// -// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where -// the last dimension stores serialized `SparseTensor` objects and the other N -// dimensions (N >= 0) correspond to a batch. The ranks of the original -// `SparseTensor` objects must all match. When the final `SparseTensor` is -// created, its rank is the rank of the incoming `SparseTensor` objects plus N; -// the sparse tensors have been concatenated along new dimensions, one for each -// batch. -// -// The output `SparseTensor` object's shape values for the original dimensions -// are the max across the input `SparseTensor` objects' shape values for the -// corresponding dimensions. The new dimensions match the size of the batch. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the serialized input is a `[2 x 3]` matrix representing two -// original `SparseTensor` objects: -// -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// -// and -// -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] -// -// then the final deserialized `SparseTensor` will be: -// -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] -// -// Arguments: -// serialized_sparse: The serialized `SparseTensor` objects. The last dimension -// must have 3 columns. -// dtype: The `dtype` of the serialized `SparseTensor` objects. -func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - opspec := tf.OpSpec{ - Type: "DeserializeSparse", - Input: []tf.Input{ - serialized_sparse, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Computes inverse hyperbolic cosine of x element-wise. -func Acosh(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Acosh", + Type: "IsInf", Input: []tf.Input{ x, }, @@ -15582,24 +28509,83 @@ func Acosh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Convert one or more images from HSV to RGB. +// Returns the truth value of (x > y) element-wise. // -// Outputs a tensor of the same shape as the `images` tensor, containing the RGB +// *NOTE*: `Greater` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Greater", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. The hash function is a keyed hash function, where attribute `key` +// defines the key of the hash function. `key` is an array of 2 elements. +// +// A strong hash is important when inputs may be malicious, e.g. URLs with +// additional components. Adversaries could try to make their inputs hash to the +// same bucket for a denial-of-service attack or to skew the results. A strong +// hash can be used to make it difficult to find inputs with a skewed hash value +// distribution over buckets. This requires that the hash function is +// seeded by a high-entropy (random) "key" unknown to the adversary. +// +// The additional robustness comes at a cost of roughly 4x higher compute +// time than `tf.string_to_hash_bucket_fast`. +// +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// key: The key used to seed the hash function, passed as a list of two uint64 +// elements. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} + opspec := tf.OpSpec{ + Type: "StringToHashBucketStrong", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts one or more images from RGB to HSV. +// +// Outputs a tensor of the same shape as the `images` tensor, containing the HSV // value of the pixels. The output is only well defined if the value in `images` // are in `[0,1]`. // -// See `rgb_to_hsv` for a description of the HSV encoding. +// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and +// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0 +// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. // // Arguments: -// images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3. +// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3. // -// Returns `images` converted to RGB. -func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) { +// Returns `images` converted to HSV. +func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "HSVToRGB", + Type: "RGBToHSV", Input: []tf.Input{ images, }, @@ -15608,5452 +28594,25 @@ func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) { return op.Output(0) } -// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul. -type SparseTensorDenseMatMulAttr func(optionalAttr) - -// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value. +// Deserializes a serialized tree ensemble config and replaces current tree // -// value: Use the adjoint of A in the matrix multiply. If A is complex, this -// is transpose(conj(A)). Otherwise it's transpose(A). -// If not specified, defaults to false -func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr { - return func(m optionalAttr) { - m["adjoint_a"] = value - } -} - -// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value. -// -// value: Use the adjoint of B in the matrix multiply. If B is complex, this -// is transpose(conj(B)). Otherwise it's transpose(B). -// If not specified, defaults to false -func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr { - return func(m optionalAttr) { - m["adjoint_b"] = value - } -} - -// Multiply SparseTensor (of rank 2) "A" by dense matrix "B". -// -// No validity checking is performed on the indices of A. However, the following -// input format is recommended for optimal behavior: -// -// if adjoint_a == false: -// A should be sorted in lexicographically increasing order. Use SparseReorder -// if you're not sure. -// if adjoint_a == true: -// A should be sorted in order of increasing dimension 1 (i.e., "column major" -// order instead of "row major" order). +// ensemble. // // Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. -// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. -// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. -// b: 2-D. A dense Matrix. -func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseMatMul", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds two `SparseTensor` objects to produce another `SparseTensor`. -// -// The input `SparseTensor` objects' indices are assumed ordered in standard -// lexicographic order. If this is not the case, before this step run -// `SparseReorder` to restore index ordering. -// -// By default, if two values sum to zero at some index, the output `SparseTensor` -// would still include that particular location in its index, storing a zero in the -// corresponding value slot. To override this, callers can specify `thresh`, -// indicating that if the sum has a magnitude strictly smaller than `thresh`, its -// corresponding value and index would then not be included. In particular, -// `thresh == 0` (default) means everything is kept and actual thresholding happens -// only for a positive value. -// -// In the following shapes, `nnz` is the count after taking `thresh` into account. -// -// Arguments: -// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. -// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. -// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. -// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. -// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. -// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. -// thresh: 0-D. The magnitude threshold that determines if an output value/index -// pair takes space. -func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// TensorArrayV3Attr is an optional argument to TensorArrayV3. -type TensorArrayV3Attr func(optionalAttr) - -// TensorArrayV3ElementShape sets the optional element_shape attribute to value. -// -// value: The expected shape of an element, if known. Used to -// validate the shapes of TensorArray elements. If this shape is not -// fully specified, gathering zero-size TensorArrays is an error. -// If not specified, defaults to -func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr { - return func(m optionalAttr) { - m["element_shape"] = value - } -} - -// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value. -// -// value: A boolean that determines whether writes to the TensorArray -// are allowed to grow the size. By default, this is not allowed. -// If not specified, defaults to false -func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { - return func(m optionalAttr) { - m["dynamic_size"] = value - } -} - -// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value. -// -// value: If true (default), Tensors in the TensorArray are cleared -// after being read. This disables multiple read semantics but allows early -// release of memory. -// If not specified, defaults to true -func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { - return func(m optionalAttr) { - m["clear_after_read"] = value - } -} - -// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. -// -// value: If true (default is false), then all -// elements in the TensorArray will be expected to have have identical shapes. -// This allows certain behaviors, like dynamically checking for -// consistent shapes on write, and being able to fill in properly -// shaped zero tensors on stack -- even if the element_shape attribute -// is not fully defined. -// If not specified, defaults to false -func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { - return func(m optionalAttr) { - m["identical_element_shapes"] = value - } -} - -// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. -// -// value: Overrides the name used for the temporary tensor_array -// resource. Default value is the name of the 'TensorArray' op (which -// is guaranteed unique). -// If not specified, defaults to "" -func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { - return func(m optionalAttr) { - m["tensor_array_name"] = value - } -} - -// An array of Tensors of given size. -// -// Write data via Write and read via Read or Pack. -// -// Arguments: -// size: The size of the array. -// dtype: The type of the elements on the tensor_array. -// -// Returns The handle to the TensorArray.A scalar used to control gradient flow. -func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorArrayV3", - Input: []tf.Input{ - size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// The gradient operator for the SparseAdd op. -// -// The SparseAdd op calculates A + B, where A, B, and the sum are all represented -// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. -// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty -// values of A and B. -// -// Arguments: -// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to -// the non-empty values of the sum. -// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. -// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. -// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size -// `[nnz(sum), ndims]`. -// -// Returns 1-D with shape `[nnz(A)]`. The gradient with respect to the -// non-empty values of A.1-D with shape `[nnz(B)]`. The gradient with respect to the -// non-empty values of B. -func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseAddGrad", - Input: []tf.Input{ - backprop_val_grad, a_indices, b_indices, sum_indices, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Computes softmax cross entropy cost and gradients to backpropagate. -// -// Inputs are the logits, not probabilities. -// -// Arguments: -// features: batch_size x num_classes matrix -// labels: batch_size x num_classes matrix -// The caller must ensure that each batch of labels represents a valid -// probability distribution. -// -// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). -func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SoftmaxCrossEntropyWithLogits", - Input: []tf.Input{ - features, labels, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Returns true if and only if the given Optional variant has a value. -func OptionalHasValue(scope *Scope, optional tf.Output) (has_value tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OptionalHasValue", - Input: []tf.Input{ - optional, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Splits a tensor into `num_split` tensors along one dimension. -// -// Arguments: -// value: The tensor to split. -// size_splits: list containing the sizes of each output tensor along the split -// dimension. Must sum to the dimension of value along split_dim. -// Can contain one -1 indicating that dimension is to be inferred. -// axis: 0-D. The dimension along which to split. Must be in the range -// `[-rank(value), rank(value))`. -// -// -// Returns Tensors whose shape matches that of `value` -// except along `axis`, where their sizes are -// `size_splits[i]`. -func SplitV(scope *Scope, value tf.Output, size_splits tf.Output, axis tf.Output, num_split int64) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_split": num_split} - opspec := tf.OpSpec{ - Type: "SplitV", - Input: []tf.Input{ - value, size_splits, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("SplitV", err) - return - } - return output -} - -// Scatter the data from the input value into specific TensorArray elements. -// -// `indices` must be a vector, its length must match the first dim of `value`. -// -// Arguments: -// handle: The handle to a TensorArray. -// indices: The locations at which to write the tensor elements. -// value: The concatenated tensor to write to the TensorArray. -// flow_in: A float scalar that enforces proper chaining of operations. -// -// Returns A float scalar that enforces proper chaining of operations. -func TensorArrayScatterV3(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorArrayScatterV3", - Input: []tf.Input{ - handle, indices, value, flow_in, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ImagAttr is an optional argument to Imag. -type ImagAttr func(optionalAttr) - -// ImagTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func ImagTout(value tf.DataType) ImagAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Returns the imaginary part of a complex number. -// -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// type `float` that is the imaginary part of each element in `input`. All -// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* -// is the real part and *b* is the imaginary part returned by this operation. -// -// For example: -// -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.imag(input) ==> [4.75, 5.75] -// ``` -func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Imag", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the inverse permutation of a tensor. -// -// This operation computes the inverse of an index permutation. It takes a 1-D -// integer tensor `x`, which represents the indices of a zero-based array, and -// swaps each value with its index position. In other words, for an output tensor -// `y` and an input tensor `x`, this operation computes the following: -// -// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]` -// -// The values must include 0. There can be no duplicate values or negative values. -// -// For example: -// -// ``` -// # tensor `x` is [3, 4, 0, 2, 1] -// invert_permutation(x) ==> [2, 4, 3, 0, 1] -// ``` -// -// Arguments: -// x: 1-D. -// -// Returns 1-D. -func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "InvertPermutation", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool. -type FractionalMaxPoolAttr func(optionalAttr) - -// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value. -// -// value: When set to True, generates the pooling sequence in a -// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin -// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for -// difference between pseudorandom and random. -// If not specified, defaults to false -func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr { - return func(m optionalAttr) { - m["pseudo_random"] = value - } -} - -// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value. -// -// value: When set to True, it means when pooling, the values at the boundary -// of adjacent pooling cells are used by both cells. For example: -// -// `index 0 1 2 3 4` -// -// `value 20 5 16 3 7` -// -// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. -// The result would be [20, 16] for fractional max pooling. -// If not specified, defaults to false -func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr { - return func(m optionalAttr) { - m["overlapping"] = value - } -} - -// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value. -// -// value: When set to True, a fixed pooling region will be used when -// iterating over a FractionalMaxPool node in the computation graph. Mainly used -// in unit test to make FractionalMaxPool deterministic. -// If not specified, defaults to false -func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr { - return func(m optionalAttr) { - m["deterministic"] = value - } -} - -// FractionalMaxPoolSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// FractionalMaxPoolSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Performs fractional max pooling on the input. -// -// Fractional max pooling is slightly different than regular max pooling. In -// regular max pooling, you downsize an input set by taking the maximum value of -// smaller N x N subsections of the set (often 2x2), and try to reduce the set by -// a factor of N, where N is an integer. Fractional max pooling, as you might -// expect from the word "fractional", means that the overall reduction ratio N -// does not have to be an integer. -// -// The sizes of the pooling regions are generated randomly but are fairly uniform. -// For example, let's look at the height dimension, and the constraints on the -// list of rows that will be pool boundaries. -// -// First we define the following: -// -// 1. input_row_length : the number of rows from the input set -// 2. output_row_length : which will be smaller than the input -// 3. alpha = input_row_length / output_row_length : our reduction ratio -// 4. K = floor(alpha) -// 5. row_pooling_sequence : this is the result list of pool boundary rows -// -// Then, row_pooling_sequence should satisfy: -// -// 1. a[0] = 0 : the first value of the sequence is 0 -// 2. a[end] = input_row_length : the last value of the sequence is the size -// 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size -// 4. length(row_pooling_sequence) = output_row_length+1 -// -// For more details on fractional max pooling, see this paper: -// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) -// -// Arguments: -// value: 4-D with shape `[batch, height, width, channels]`. -// pooling_ratio: Pooling ratio for each dimension of `value`, currently only -// supports row and col dimension and should be >= 1.0. For example, a valid -// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements -// must be 1.0 because we don't allow pooling on batch and channels -// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions -// respectively. -// -// Returns output tensor after fractional max pooling.row pooling sequence, needed to calculate gradient.column pooling sequence, needed to calculate gradient. -func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FractionalMaxPool", - Input: []tf.Input{ - value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Rolls the elements of a tensor along an axis. -// -// The elements are shifted positively (towards larger indices) by the offset of -// `shift` along the dimension of `axis`. Negative `shift` values will shift -// elements in the opposite direction. Elements that roll passed the last position -// will wrap around to the first and vice versa. Multiple shifts along multiple -// axes may be specified. -// -// For example: -// -// ``` -// # 't' is [0, 1, 2, 3, 4] -// roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2] -// -// # shifting along multiple dimensions -// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] -// roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] -// -// # shifting along the same axis multiple times -// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] -// roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] -// ``` -// -// Arguments: -// -// shift: Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which -// elements are shifted positively (towards larger indices) along the dimension -// specified by `axis[i]`. Negative shifts will roll the elements in the opposite -// direction. -// axis: Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift -// `shift[i]` should occur. If the same axis is referenced more than once, the -// total shift for that axis will be the sum of all the shifts that belong to that -// axis. -// -// Returns Has the same shape and size as the input. The elements are shifted -// positively (towards larger indices) by the offsets of `shift` along the -// dimensions of `axis`. -func Roll(scope *Scope, input tf.Output, shift tf.Output, axis tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Roll", - Input: []tf.Input{ - input, shift, axis, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative. -type DepthwiseConv2dNativeAttr func(optionalAttr) - -// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, height, width, channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, channels, height, width]. -// If not specified, defaults to "NHWC" -func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors. -// -// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` -// and a filter / kernel tensor of shape -// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing -// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies -// a different filter to each input channel (expanding from 1 channel to -// `channel_multiplier` channels for each), then concatenates the results -// together. Thus, the output has `in_channels * channel_multiplier` channels. -// -// ``` -// for k in 0..in_channels-1 -// for q in 0..channel_multiplier-1 -// output[b, i, j, k * channel_multiplier + q] = -// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * -// filter[di, dj, k, q] -// ``` -// -// Must have `strides[0] = strides[3] = 1`. For the most common case of the same -// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. -// -// Arguments: -// -// -// strides: 1-D of length 4. The stride of the sliding window for each dimension -// of `input`. -// padding: The type of padding algorithm to use. -func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DepthwiseConv2dNative", - Input: []tf.Input{ - input, filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SpaceToBatch for N-D tensors of type T. -// -// This operation divides "spatial" dimensions `[1, ..., M]` of the input into a -// grid of blocks of shape `block_shape`, and interleaves these blocks with the -// "batch" dimension (0) such that in the output, the spatial dimensions -// `[1, ..., M]` correspond to the position within the grid, and the batch -// dimension combines both the position within a spatial block and the original -// batch position. Prior to division into blocks, the spatial dimensions of the -// input are optionally zero padded according to `paddings`. See below for a -// precise description. -// -// Arguments: -// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, -// where spatial_shape has `M` dimensions. -// block_shape: 1-D with shape `[M]`, all values must be >= 1. -// paddings: 2-D with shape `[M, 2]`, all values must be >= 0. -// `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension -// `i + 1`, which corresponds to spatial dimension `i`. It is required that -// `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`. -// -// This operation is equivalent to the following steps: -// -// 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the -// input according to `paddings` to produce `padded` of shape `padded_shape`. -// -// 2. Reshape `padded` to `reshaped_padded` of shape: -// -// [batch] + -// [padded_shape[1] / block_shape[0], -// block_shape[0], -// ..., -// padded_shape[M] / block_shape[M-1], -// block_shape[M-1]] + -// remaining_shape -// -// 3. Permute dimensions of `reshaped_padded` to produce -// `permuted_reshaped_padded` of shape: -// -// block_shape + -// [batch] + -// [padded_shape[1] / block_shape[0], -// ..., -// padded_shape[M] / block_shape[M-1]] + -// remaining_shape -// -// 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch -// dimension, producing an output tensor of shape: -// -// [batch * prod(block_shape)] + -// [padded_shape[1] / block_shape[0], -// ..., -// padded_shape[M] / block_shape[M-1]] + -// remaining_shape -// -// Some examples: -// -// (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and -// `paddings = [[0, 0], [0, 0]]`: -// -// ``` -// x = [[[[1], [2]], [[3], [4]]]] -// ``` -// -// The output tensor has shape `[4, 1, 1, 1]` and value: -// -// ``` -// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] -// ``` -// -// (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and -// `paddings = [[0, 0], [0, 0]]`: -// -// ``` -// x = [[[[1, 2, 3], [4, 5, 6]], -// [[7, 8, 9], [10, 11, 12]]]] -// ``` -// -// The output tensor has shape `[4, 1, 1, 3]` and value: -// -// ``` -// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] -// ``` -// -// (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and -// `paddings = [[0, 0], [0, 0]]`: -// -// ``` -// x = [[[[1], [2], [3], [4]], -// [[5], [6], [7], [8]], -// [[9], [10], [11], [12]], -// [[13], [14], [15], [16]]]] -// ``` -// -// The output tensor has shape `[4, 2, 2, 1]` and value: -// -// ``` -// x = [[[[1], [3]], [[9], [11]]], -// [[[2], [4]], [[10], [12]]], -// [[[5], [7]], [[13], [15]]], -// [[[6], [8]], [[14], [16]]]] -// ``` -// -// (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and -// paddings = `[[0, 0], [2, 0]]`: -// -// ``` -// x = [[[[1], [2], [3], [4]], -// [[5], [6], [7], [8]]], -// [[[9], [10], [11], [12]], -// [[13], [14], [15], [16]]]] -// ``` -// -// The output tensor has shape `[8, 1, 3, 1]` and value: -// -// ``` -// x = [[[[0], [1], [3]]], [[[0], [9], [11]]], -// [[[0], [2], [4]]], [[[0], [10], [12]]], -// [[[0], [5], [7]]], [[[0], [13], [15]]], -// [[[0], [6], [8]]], [[[0], [14], [16]]]] -// ``` -// -// Among others, this operation is useful for reducing atrous convolution into -// regular convolution. -func SpaceToBatchND(scope *Scope, input tf.Output, block_shape tf.Output, paddings tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SpaceToBatchND", - Input: []tf.Input{ - input, block_shape, paddings, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. -type DestroyResourceOpAttr func(optionalAttr) - -// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. -// -// value: whether to ignore the error when the resource -// doesn't exist. -// If not specified, defaults to true -func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { - return func(m optionalAttr) { - m["ignore_lookup_error"] = value - } -} - -// Deletes the resource specified by the handle. -// -// All subsequent operations using the resource will result in a NotFound -// error status. -// -// Arguments: -// resource: handle to the resource to delete. +// tree_ensemble_handle: Handle to the tree ensemble. +// stamp_token: Token to use as the new value of the resource stamp. +// tree_ensemble_serialized: Serialized proto of the ensemble. // // Returns the created operation. -func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { +func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DestroyResourceOp", - Input: []tf.Input{ - resource, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Gradients for batch normalization. -// -// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() -// -// This op is deprecated. See `tf.nn.batch_normalization`. -// -// Arguments: -// t: A 4D input Tensor. -// m: A 1D mean Tensor with size matching the last dimension of t. -// This is the first output from tf.nn.moments, -// or a saved moving average thereof. -// v: A 1D variance Tensor with size matching the last dimension of t. -// This is the second output from tf.nn.moments, -// or a saved moving average thereof. -// gamma: A 1D gamma Tensor with size matching the last dimension of t. -// If "scale_after_normalization" is true, this Tensor will be multiplied -// with the normalized Tensor. -// backprop: 4D backprop Tensor. -// variance_epsilon: A small float number to avoid dividing by 0. -// scale_after_normalization: A bool indicating whether the resulted tensor -// needs to be multiplied with gamma. -// -// Returns 4D backprop tensor for input.1D backprop tensor for mean.1D backprop tensor for variance.1D backprop tensor for beta.1D backprop tensor for gamma. -func BatchNormWithGlobalNormalizationGrad(scope *Scope, t tf.Output, m tf.Output, v tf.Output, gamma tf.Output, backprop tf.Output, variance_epsilon float32, scale_after_normalization bool) (dx tf.Output, dm tf.Output, dv tf.Output, db tf.Output, dg tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} - opspec := tf.OpSpec{ - Type: "BatchNormWithGlobalNormalizationGrad", - Input: []tf.Input{ - t, m, v, gamma, backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) -} - -// Bucketizes 'input' based on 'boundaries'. -// -// For example, if the inputs are -// boundaries = [0, 10, 100] -// input = [[-5, 10000] -// [150, 10] -// [5, 100]] -// -// then the output will be -// output = [[0, 3] -// [3, 2] -// [1, 3]] -// -// Arguments: -// input: Any shape of Tensor contains with int or float type. -// boundaries: A sorted list of floats gives the boundary of the buckets. -// -// Returns Same shape with 'input', each value of input replaced with bucket index. -// -// @compatibility(numpy) -// Equivalent to np.digitize. -// @end_compatibility -func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"boundaries": boundaries} - opspec := tf.OpSpec{ - Type: "Bucketize", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. -// -// This Op does not require `a_indices` be sorted in standard lexicographic order. -// -// Arguments: -// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. -// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. -// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. -// b: `ndims`-D Tensor. With shape `a_shape`. -func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseTensorDenseAdd", - Input: []tf.Input{ - a_indices, a_values, a_shape, b, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingAdadeltaParametersAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParameters. -type RetrieveTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingAdadeltaParametersTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingAdadeltaParametersTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve Adadelta embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the Adadelta optimization algorithm.Parameter accumulators updated by the Adadelta optimization algorithm.Parameter updates updated by the Adadelta optimization algorithm. -func RetrieveTPUEmbeddingAdadeltaParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingAdadeltaParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// LoadTPUEmbeddingAdadeltaParametersAttr is an optional argument to LoadTPUEmbeddingAdadeltaParameters. -type LoadTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingAdadeltaParametersTableId(value int64) LoadTPUEmbeddingAdadeltaParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingAdadeltaParametersTableName(value string) LoadTPUEmbeddingAdadeltaParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Adadelta embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the Adadelta optimization algorithm. -// accumulators: Value of accumulators used in the Adadelta optimization algorithm. -// updates: Value of updates used in the Adadelta optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingAdadeltaParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingAdadeltaParameters", - Input: []tf.Input{ - parameters, accumulators, updates, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Inserts a dimension of 1 into a tensor's shape. -// -// Given a tensor `input`, this operation inserts a dimension of 1 at the -// dimension index `axis` of `input`'s shape. The dimension index `axis` starts at -// zero; if you specify a negative number for `axis` it is counted backward from -// the end. -// -// This operation is useful if you want to add a batch dimension to a single -// element. For example, if you have a single image of shape `[height, width, -// channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, -// which will make the shape `[1, height, width, channels]`. -// -// Other examples: -// -// ``` -// # 't' is a tensor of shape [2] -// shape(expand_dims(t, 0)) ==> [1, 2] -// shape(expand_dims(t, 1)) ==> [2, 1] -// shape(expand_dims(t, -1)) ==> [2, 1] -// -// # 't2' is a tensor of shape [2, 3, 5] -// shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] -// shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] -// shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] -// ``` -// -// This operation requires that: -// -// `-1-input.dims() <= dim <= input.dims()` -// -// This operation is related to `squeeze()`, which removes dimensions of -// size 1. -// -// Arguments: -// -// axis: 0-D (scalar). Specifies the dimension index at which to -// expand the shape of `input`. Must be in the range -// `[-rank(input) - 1, rank(input)]`. -// -// Returns Contains the same data as `input`, but its shape has an additional -// dimension of size 1 added. -func ExpandDims(scope *Scope, input tf.Output, axis tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ExpandDims", - Input: []tf.Input{ - input, axis, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adjust the saturation of one or more images. -// -// `images` is a tensor of at least 3 dimensions. The last dimension is -// interpretted as channels, and must be three. -// -// The input image is considered in the RGB colorspace. Conceptually, the RGB -// colors are first mapped into HSV. A scale is then applied all the saturation -// values, and then remapped back to RGB colorspace. -// -// Arguments: -// images: Images to adjust. At least 3-D. -// scale: A float scale to add to the saturation. -// -// Returns The hue-adjusted image or images. -func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AdjustSaturation", - Input: []tf.Input{ - images, scale, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingMomentumParametersGradAccumDebug. -type LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Momentum embedding parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the Momentum optimization algorithm. -// momenta: Value of momenta used in the Momentum optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the Momentum optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingMomentumParametersGradAccumDebug", - Input: []tf.Input{ - parameters, momenta, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// LoadTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingADAMParametersGradAccumDebug. -type LoadTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load ADAM embedding parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the ADAM optimization algorithm. -// momenta: Value of momenta used in the ADAM optimization algorithm. -// velocities: Value of velocities used in the ADAM optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the ADAM optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingADAMParametersGradAccumDebug", - Input: []tf.Input{ - parameters, momenta, velocities, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// RetrieveTPUEmbeddingRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParameters. -type RetrieveTPUEmbeddingRMSPropParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingRMSPropParametersTableName(value string) RetrieveTPUEmbeddingRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve RMSProp embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the RMSProp optimization algorithm.Parameter ms updated by the RMSProp optimization algorithm.Parameter mom updated by the RMSProp optimization algorithm. -func RetrieveTPUEmbeddingRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingRMSPropParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Computes rectified linear 6 gradients for a Relu6 operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding Relu6 operation. -// features: The features passed as input to the corresponding Relu6 operation, or -// its output; using either one produces the same result. -// -// Returns The gradients: -// `gradients * (features > 0) * (features < 6)`. -func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Relu6Grad", - Input: []tf.Input{ - gradients, features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingFTRLParametersGradAccumDebug. -type LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load FTRL embedding parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the FTRL optimization algorithm. -// accumulators: Value of accumulators used in the FTRL optimization algorithm. -// linears: Value of linears used in the FTRL optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the FTRL optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingFTRLParametersGradAccumDebug", - Input: []tf.Input{ - parameters, accumulators, linears, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ImageSummaryAttr is an optional argument to ImageSummary. -type ImageSummaryAttr func(optionalAttr) - -// ImageSummaryMaxImages sets the optional max_images attribute to value. -// -// value: Max number of batch elements to generate images for. -// If not specified, defaults to 3 -// -// REQUIRES: value >= 1 -func ImageSummaryMaxImages(value int64) ImageSummaryAttr { - return func(m optionalAttr) { - m["max_images"] = value - } -} - -// ImageSummaryBadColor sets the optional bad_color attribute to value. -// -// value: Color to use for pixels with non-finite values. -// If not specified, defaults to > int_val:255 int_val:0 int_val:0 int_val:255 > -func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { - return func(m optionalAttr) { - m["bad_color"] = value - } -} - -// Outputs a `Summary` protocol buffer with images. -// -// The summary has up to `max_images` summary values containing images. The -// images are built from `tensor` which must be 4-D with shape `[batch_size, -// height, width, channels]` and where `channels` can be: -// -// * 1: `tensor` is interpreted as Grayscale. -// * 3: `tensor` is interpreted as RGB. -// * 4: `tensor` is interpreted as RGBA. -// -// The images have the same number of channels as the input tensor. For float -// input, the values are normalized one image at a time to fit in the range -// `[0, 255]`. `uint8` values are unchanged. The op uses two different -// normalization algorithms: -// -// * If the input values are all positive, they are rescaled so the largest one -// is 255. -// -// * If any input value is negative, the values are shifted so input value 0.0 -// is at 127. They are then rescaled so that either the smallest value is 0, -// or the largest one is 255. -// -// The `tag` argument is a scalar `Tensor` of type `string`. It is used to -// build the `tag` of the summary values: -// -// * If `max_images` is 1, the summary value tag is '*tag*/image'. -// * If `max_images` is greater than 1, the summary value tags are -// generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. -// -// The `bad_color` argument is the color to use in the generated images for -// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. -// Each element must be in the range `[0, 255]` (It represents the value of a -// pixel in the output image). Non-finite values in the input tensor are -// replaced by this tensor in the output image. The default value is the color -// red. -// -// Arguments: -// tag: Scalar. Used to build the `tag` attribute of the summary values. -// tensor: 4-D of shape `[batch_size, height, width, channels]` where -// `channels` is 1, 3, or 4. -// -// Returns Scalar. Serialized `Summary` protocol buffer. -func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ImageSummary", - Input: []tf.Input{ - tag, tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the number of work units this Reader has finished processing. -// -// Arguments: -// reader_handle: Handle to a Reader. -func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderNumWorkUnitsCompletedV2", - Input: []tf.Input{ - reader_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes softplus: `log(exp(features) + 1)`. -func Softplus(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Softplus", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Greedily selects a subset of bounding boxes in descending order of score, -// -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes with score less than -// `score_threshold` are removed. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system and more -// generally is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression_v2( -// boxes, scores, max_output_size, iou_threshold, score_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) -// -// Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// iou_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too much with respect to IOU. -// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove -// boxes based on score. -// -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppressionV3(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NonMaxSuppressionV3", - Input: []tf.Input{ - boxes, scores, max_output_size, iou_threshold, score_threshold, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Encode audio data using the WAV file format. -// -// This operation will generate a string suitable to be saved out to create a .wav -// audio file. It will be encoded in the 16-bit PCM format. It takes in float -// values in the range -1.0f to 1.0f, and any outside that value will be clamped to -// that range. -// -// `audio` is a 2-D float Tensor of shape `[length, channels]`. -// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). -// -// Arguments: -// audio: 2-D with shape `[length, channels]`. -// sample_rate: Scalar containing the sample frequency. -// -// Returns 0-D. WAV-encoded file contents. -func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EncodeWav", - Input: []tf.Input{ - audio, sample_rate, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. -type NonMaxSuppressionAttr func(optionalAttr) - -// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. -// -// value: A float representing the threshold for deciding whether boxes -// overlap too much with respect to IOU. -// If not specified, defaults to 0.5 -func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { - return func(m optionalAttr) { - m["iou_threshold"] = value - } -} - -// Greedily selects a subset of bounding boxes in descending order of score, -// -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system. Note that this -// algorithm is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression( -// boxes, scores, max_output_size, iou_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) -// -// Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "NonMaxSuppression", - Input: []tf.Input{ - boxes, scores, max_output_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse. -type SparseReduceMaxSparseAttr func(optionalAttr) - -// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the max of elements across dimensions of a SparseTensor. -// -// This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a -// SparseTensor. -// -// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained -// with length 1. -// -// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor -// with a single element is returned. Additionally, the axes can be negative, -// which are interpreted according to the indexing rules in Python. -// -// Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. -func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseReduceMaxSparse", - Input: []tf.Input{ - input_indices, input_values, input_shape, reduction_axes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Serializes the tree handle to a proto -// -// Arguments: -// tree_handle: Handle to the tree resource to be serialized. -// -// Returns Serialied proto string of the tree resource. -func TensorForestTreeSerialize(scope *Scope, tree_handle tf.Output) (tree_config tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorForestTreeSerialize", - Input: []tf.Input{ - tree_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Assigns a new value to a variable. -// -// Any ReadVariableOp with a control dependency on this op is guaranteed to return -// this value or a subsequent newer value of the variable. -// -// Arguments: -// resource: handle to the resource in which to store the variable. -// value: the value to set the new tensor to use. -// -// Returns the created operation. -func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AssignVariableOp", - Input: []tf.Input{ - resource, value, - }, - } - return scope.AddOperation(opspec) -} - -// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. -type ResourceScatterNdUpdateAttr func(optionalAttr) - -// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. -// -// value: An optional bool. Defaults to True. If True, the assignment will -// be protected by a lock; otherwise the behavior is undefined, -// but may exhibit less contention. -// If not specified, defaults to true -func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Applies sparse `updates` to individual values or slices within a given -// -// variable according to `indices`. -// -// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. -// -// `indices` must be integer tensor, containing indices into `ref`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. -// -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th -// dimension of `ref`. -// -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: -// -// ``` -// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. -// ``` -// -// For example, say we want to update 4 scattered elements to a rank-1 tensor to -// 8 elements. In Python, that update would look like this: -// -// ```python -// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) -// indices = tf.constant([[4], [3], [1] ,[7]]) -// updates = tf.constant([9, 10, 11, 12]) -// update = tf.scatter_nd_update(ref, indices, updates) -// with tf.Session() as sess: -// print sess.run(update) -// ``` -// -// The resulting update to ref would look like this: -// -// [1, 11, 3, 10, 9, 6, 7, 12] -// -// See `tf.scatter_nd` for more details about how to make updates to -// slices. -// -// Arguments: -// ref: A resource handle. Must be from a VarHandleOp. -// indices: A Tensor. Must be one of the following types: int32, int64. -// A tensor of indices into ref. -// updates: A Tensor. Must have the same type as ref. A tensor of updated -// values to add to ref. -// -// Returns the created operation. -func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceScatterNdUpdate", - Input: []tf.Input{ - ref, indices, updates, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ExperimentalThreadPoolHandleAttr is an optional argument to ExperimentalThreadPoolHandle. -type ExperimentalThreadPoolHandleAttr func(optionalAttr) - -// ExperimentalThreadPoolHandleMaxIntraOpParallelism sets the optional max_intra_op_parallelism attribute to value. -// -// value: The maximum degree of parallelism to use within operations that execute on this -// threadpool. -// If not specified, defaults to 1 -func ExperimentalThreadPoolHandleMaxIntraOpParallelism(value int64) ExperimentalThreadPoolHandleAttr { - return func(m optionalAttr) { - m["max_intra_op_parallelism"] = value - } -} - -// ExperimentalThreadPoolHandleContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func ExperimentalThreadPoolHandleContainer(value string) ExperimentalThreadPoolHandleAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// ExperimentalThreadPoolHandleSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func ExperimentalThreadPoolHandleSharedName(value string) ExperimentalThreadPoolHandleAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Creates a dataset that uses a custom thread pool to compute `input_dataset`. -// -// Arguments: -// num_threads: The number of threads in the thread pool. -// display_name: A human-readable name for the threads that may be visible in some -// visualizations. -// threadpool. -// -// Returns A resource that can be consumed by one or more ExperimentalThreadPoolDataset -// ops. -func ExperimentalThreadPoolHandle(scope *Scope, num_threads int64, display_name string, optional ...ExperimentalThreadPoolHandleAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_threads": num_threads, "display_name": display_name} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ExperimentalThreadPoolHandle", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// BiasAddGradAttr is an optional argument to BiasAddGrad. -type BiasAddGradAttr func(optionalAttr) - -// BiasAddGradDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the bias tensor will be added to the last dimension -// of the value tensor. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// The tensor will be added to "in_channels", the third-to-the-last -// dimension. -// If not specified, defaults to "NHWC" -func BiasAddGradDataFormat(value string) BiasAddGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// The backward operation for "BiasAdd" on the "bias" tensor. -// -// It accumulates all the values from out_backprop into the feature dimension. -// For NHWC data format, the feature dimension is the last. For NCHW data format, -// the feature dimension is the third-to-last. -// -// Arguments: -// out_backprop: Any number of dimensions. -// -// Returns 1-D with size the feature dimension of `out_backprop`. -func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BiasAddGrad", - Input: []tf.Input{ - out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Splits a tensor into a list. -// -// list[i] corresponds to lengths[i] tensors from the input tensor. -// The tensor must have rank at least 1 and contain exactly sum(lengths) elements. -// -// tensor: The input tensor. -// element_shape: A shape compatible with that of elements in the tensor. -// lengths: Vector of sizes of the 0th dimension of tensors in the list. -// output_handle: The list. -func TensorListSplit(scope *Scope, tensor tf.Output, element_shape tf.Output, lengths tf.Output) (output_handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorListSplit", - Input: []tf.Input{ - tensor, element_shape, lengths, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// InfeedEnqueuePrelinearizedBufferAttr is an optional argument to InfeedEnqueuePrelinearizedBuffer. -type InfeedEnqueuePrelinearizedBufferAttr func(optionalAttr) - -// InfeedEnqueuePrelinearizedBufferDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. This should be -1 when the Op is running on a TPU device -// and = 0 when the Op is running on the CPU device. -// If not specified, defaults to -1 -func InfeedEnqueuePrelinearizedBufferDeviceOrdinal(value int64) InfeedEnqueuePrelinearizedBufferAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// An op which enqueues prelinearized buffer into TPU infeed. -// -// Arguments: -// input: A variant tensor representing linearized output. -// -// Returns the created operation. -func InfeedEnqueuePrelinearizedBuffer(scope *Scope, input tf.Output, optional ...InfeedEnqueuePrelinearizedBufferAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "InfeedEnqueuePrelinearizedBuffer", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Checks whether a resource handle-based variable has been initialized. -// -// Arguments: -// resource: the input resource handle. -// -// Returns a scalar boolean which is true if the variable has been -// initialized. -func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "VarIsInitializedOp", - Input: []tf.Input{ - resource, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PrelinearizeTupleAttr is an optional argument to PrelinearizeTuple. -type PrelinearizeTupleAttr func(optionalAttr) - -// PrelinearizeTupleLayouts sets the optional layouts attribute to value. -// -// value: A vector holding the requested layout in minor-to-major sequence for all the -// tuple shapes in the order the shapes appear in the "shapes" input. The layout -// elements for a sub-shape can be set to -1 in which case the corresponding layout -// will be computed by the infeed operation. -// If not specified, defaults to <> -func PrelinearizeTupleLayouts(value []int64) PrelinearizeTupleAttr { - return func(m optionalAttr) { - m["layouts"] = value - } -} - -// An op which linearizes multiple Tensor values to an opaque variant tensor. -// -// Arguments: -// inputs: A list of tensors that will be provided using the infeed mechanism. -// shapes: The shapes of each tensor in `inputs`. -func PrelinearizeTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...PrelinearizeTupleAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"shapes": shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "PrelinearizeTuple", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor. -// -// This operation folds the padded areas of `input` by `MirrorPad` according to the -// `paddings` you specify. `paddings` must be the same as `paddings` argument -// given to the corresponding `MirrorPad` op. -// -// The folded size of each dimension D of the output is: -// -// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)` -// -// For example: -// -// ``` -// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. -// # 'paddings' is [[0, 1]], [0, 1]]. -// # 'mode' is SYMMETRIC. -// # rank of 't' is 2. -// pad(t, paddings) ==> [[ 1, 5] -// [11, 28]] -// ``` -// -// Arguments: -// input: The input tensor to be folded. -// paddings: A two-column matrix specifying the padding sizes. The number of -// rows must be the same as the rank of `input`. -// mode: The mode used in the `MirrorPad` op. -// -// Returns The folded tensor. -func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"mode": mode} - opspec := tf.OpSpec{ - Type: "MirrorPadGrad", - Input: []tf.Input{ - input, paddings, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug. -type RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve proximal Adagrad embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the proximal Adagrad optimization algorithm.Parameter accumulators updated by the proximal Adagrad optimization algorithm.Parameter gradient_accumulators updated by the proximal Adagrad optimization algorithm. -func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingRMSPropParametersGradAccumDebug. -type LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load RMSProp embedding parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the RMSProp optimization algorithm. -// ms: Value of ms used in the RMSProp optimization algorithm. -// mom: Value of mom used in the RMSProp optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the RMSProp optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingRMSPropParametersGradAccumDebug", - Input: []tf.Input{ - parameters, ms, mom, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// UnbatchGradAttr is an optional argument to UnbatchGrad. -type UnbatchGradAttr func(optionalAttr) - -// UnbatchGradContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func UnbatchGradContainer(value string) UnbatchGradAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// UnbatchGradSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func UnbatchGradSharedName(value string) UnbatchGradAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Gradient of Unbatch. -// -// Acts like Batch but using the given batch_index index of batching things as they -// become available. This ensures that the gradients are propagated back in the -// same session which did the forward pass. -// -// original_input: The input to the Unbatch operation this is the gradient of. -// batch_index: The batch_index given to the Unbatch operation this is the gradient -// of. -// grad: The downstream gradient. -// id: The id scalar emitted by Batch. -// batched_grad: The return value, either an empty tensor or the batched gradient. -// container: Container to control resource sharing. -// shared_name: Instances of UnbatchGrad with the same container and shared_name -// are assumed to possibly belong to the same batch. If left empty, the op name -// will be used as the shared name. -func UnbatchGrad(scope *Scope, original_input tf.Output, batch_index tf.Output, grad tf.Output, id tf.Output, optional ...UnbatchGradAttr) (batched_grad tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UnbatchGrad", - Input: []tf.Input{ - original_input, batch_index, grad, id, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Deprecated. Use TensorArrayReadV3 -// -// DEPRECATED at GraphDef version 26: Use TensorArrayReadV3 -func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - opspec := tf.OpSpec{ - Type: "TensorArrayReadV2", - Input: []tf.Input{ - handle, index, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes exponential of x element-wise. \\(y = e^x\\). -// -// This function computes the exponential of every element in the input tensor. -// i.e. `exp(x)` or `e^(x)`, where `x` is the input tensor. -// `e` denotes Euler's number and is approximately equal to 2.718281. -// Output is positive for any real input. -// -// ```python -// x = tf.constant(2.0) -// tf.math.exp(x) ==> 7.389056 -// -// x = tf.constant([2.0, 8.0]) -// tf.math.exp(x) ==> array([7.389056, 2980.958], dtype=float32) -// ``` -// -// For complex numbers, the exponential value is calculated as follows: -// -// ``` -// e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y) -// ``` -// -// Let's consider complex number 1+1j as an example. -// e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j) -// -// ```python -// x = tf.constant(1 + 1j) -// tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j -// ``` -func Exp(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Exp", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adjust the hue of one or more images. -// -// `images` is a tensor of at least 3 dimensions. The last dimension is -// interpretted as channels, and must be three. -// -// The input image is considered in the RGB colorspace. Conceptually, the RGB -// colors are first mapped into HSV. A delta is then applied all the hue values, -// and then remapped back to RGB colorspace. -// -// Arguments: -// images: Images to adjust. At least 3-D. -// delta: A float delta to add to the hue. -// -// Returns The hue-adjusted image or images. -func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AdjustHue", - Input: []tf.Input{ - images, delta, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// InfeedEnqueueTupleAttr is an optional argument to InfeedEnqueueTuple. -type InfeedEnqueueTupleAttr func(optionalAttr) - -// InfeedEnqueueTupleLayouts sets the optional layouts attribute to value. -// -// value: A vector holding the requested layout in minor-to-major sequence for -// all the tuple shapes, in the order the shapes appear in the "shapes" input. -// The layout elements for a sub-shape can be set to -1, in which case the -// corresponding layout will be computed by the infeed operation. -// If not specified, defaults to <> -func InfeedEnqueueTupleLayouts(value []int64) InfeedEnqueueTupleAttr { - return func(m optionalAttr) { - m["layouts"] = value - } -} - -// InfeedEnqueueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. This should be -1 when the Op -// is running on a TPU device, and >= 0 when the Op is running on the CPU -// device. -// If not specified, defaults to -1 -func InfeedEnqueueTupleDeviceOrdinal(value int64) InfeedEnqueueTupleAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// Feeds multiple Tensor values into the computation as an XLA tuple. -// -// Arguments: -// inputs: A list of tensors that will be provided using the infeed mechanism. -// shapes: The shapes of each tensor in `inputs`. -// -// Returns the created operation. -func InfeedEnqueueTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...InfeedEnqueueTupleAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"shapes": shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "InfeedEnqueueTuple", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ShardDatasetAttr is an optional argument to ShardDataset. -type ShardDatasetAttr func(optionalAttr) - -// ShardDatasetRequireNonEmpty sets the optional require_non_empty attribute to value. -// If not specified, defaults to false -func ShardDatasetRequireNonEmpty(value bool) ShardDatasetAttr { - return func(m optionalAttr) { - m["require_non_empty"] = value - } -} - -// Creates a `Dataset` that includes only 1/`num_shards` of this dataset. -// -// Arguments: -// -// num_shards: An integer representing the number of shards operating in parallel. -// index: An integer representing the current worker index. -// -// -func ShardDataset(scope *Scope, input_dataset tf.Output, num_shards tf.Output, index tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShardDatasetAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ShardDataset", - Input: []tf.Input{ - input_dataset, num_shards, index, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdadeltaParametersGradAccumDebug. -type LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Adadelta parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the Adadelta optimization algorithm. -// accumulators: Value of accumulators used in the Adadelta optimization algorithm. -// updates: Value of updates used in the Adadelta optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the Adadelta optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", - Input: []tf.Input{ - parameters, accumulators, updates, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Outputs deterministic pseudorandom random integers from a uniform distribution. -// -// The generated values follow a uniform distribution in the range `[minval, maxval)`. -// -// The outputs are a deterministic function of `shape`, `seed`, `minval`, and `maxval`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). -// minval: Minimum value (inclusive, scalar). -// maxval: Maximum value (exclusive, scalar). -// -// Returns Random values with specified shape. -func StatelessRandomUniformInt(scope *Scope, shape tf.Output, seed tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "StatelessRandomUniformInt", - Input: []tf.Input{ - shape, seed, minval, maxval, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns a tensor of zeros with the same shape and type as x. -// -// Arguments: -// x: a tensor of type T. -// -// Returns a tensor of the same shape and type as x but filled with zeros. -func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ZerosLike", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// InfeedEnqueueAttr is an optional argument to InfeedEnqueue. -type InfeedEnqueueAttr func(optionalAttr) - -// InfeedEnqueueShape sets the optional shape attribute to value. -// -// value: The shape of the tensor. -// If not specified, defaults to <> -func InfeedEnqueueShape(value tf.Shape) InfeedEnqueueAttr { - return func(m optionalAttr) { - m["shape"] = value - } -} - -// InfeedEnqueueLayout sets the optional layout attribute to value. -// -// value: A vector holding the requested layout in minor-to-major sequence. -// If a layout attribute is passed, but its values are all -1, the layout will -// be computed by the infeed operation. -// If not specified, defaults to <> -func InfeedEnqueueLayout(value []int64) InfeedEnqueueAttr { - return func(m optionalAttr) { - m["layout"] = value - } -} - -// InfeedEnqueueDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. This should be -1 when the Op -// is running on a TPU device, and >= 0 when the Op is running on the CPU -// device. -// If not specified, defaults to -1 -func InfeedEnqueueDeviceOrdinal(value int64) InfeedEnqueueAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// An op which feeds a single Tensor value into the computation. -// -// Arguments: -// input: A tensor that will be provided using the infeed mechanism. -// -// Returns the created operation. -func InfeedEnqueue(scope *Scope, input tf.Output, optional ...InfeedEnqueueAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "InfeedEnqueue", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// OutfeedDequeueTupleAttr is an optional argument to OutfeedDequeueTuple. -type OutfeedDequeueTupleAttr func(optionalAttr) - -// OutfeedDequeueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. This should be -1 when the Op -// is running on a TPU device, and >= 0 when the Op is running on the CPU -// device. -// If not specified, defaults to -1 -func OutfeedDequeueTupleDeviceOrdinal(value int64) OutfeedDequeueTupleAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// Retrieve multiple values from the computation outfeed. -// -// This operation will block indefinitely until data is available. Output `i` -// corresponds to XLA tuple element `i`. -// -// Arguments: -// dtypes: The element types of each element in `outputs`. -// shapes: The shapes of each tensor in `outputs`. -// -// Returns A list of tensors that will be read from the outfeed. -func OutfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape, optional ...OutfeedDequeueTupleAttr) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OutfeedDequeueTuple", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("OutfeedDequeueTuple", err) - return - } - return outputs -} - -// CropAndResizeAttr is an optional argument to CropAndResize. -type CropAndResizeAttr func(optionalAttr) - -// CropAndResizeMethod sets the optional method attribute to value. -// -// value: A string specifying the sampling method for resizing. It can be either -// `"bilinear"` or `"nearest"` and default to `"bilinear"`. Currently two sampling -// methods are supported: Bilinear and Nearest Neighbor. -// If not specified, defaults to "bilinear" -func CropAndResizeMethod(value string) CropAndResizeAttr { - return func(m optionalAttr) { - m["method"] = value - } -} - -// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value. -// -// value: Value used for extrapolation, when applicable. -// If not specified, defaults to 0 -func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr { - return func(m optionalAttr) { - m["extrapolation_value"] = value - } -} - -// Extracts crops from the input image tensor and resizes them. -// -// Extracts crops from the input image tensor and resizes them using bilinear -// sampling or nearest neighbor sampling (possibly with aspect ratio change) to a -// common output size specified by `crop_size`. This is more general than the -// `crop_to_bounding_box` op which extracts a fixed size slice from the input image -// and does not allow resizing or aspect ratio change. -// -// Returns a tensor with `crops` from the input `image` at positions defined at the -// bounding box locations in `boxes`. The cropped boxes are all resized (with -// bilinear or nearest neighbor interpolation) to a fixed -// `size = [crop_height, crop_width]`. The result is a 4-D tensor -// `[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned. -// In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical -// results to using `tf.image.resize_bilinear()` or -// `tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with -// `align_corners=True`. -// -// Arguments: -// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. -// Both `image_height` and `image_width` need to be positive. -// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor -// specifies the coordinates of a box in the `box_ind[i]` image and is specified -// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of -// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the -// `[0, 1]` interval of normalized image height is mapped to -// `[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in -// which case the sampled crop is an up-down flipped version of the original -// image. The width dimension is treated similarly. Normalized coordinates -// outside the `[0, 1]` range are allowed, in which case we use -// `extrapolation_value` to extrapolate the input image values. -// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. -// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. -// crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All -// cropped image patches are resized to this size. The aspect ratio of the image -// content is not preserved. Both `crop_height` and `crop_width` need to be -// positive. -// -// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. -func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CropAndResize", - Input: []tf.Input{ - image, boxes, box_ind, crop_size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of (x <= y) element-wise. -// -// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LessEqual", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatefulStandardNormalV2Attr is an optional argument to StatefulStandardNormalV2. -type StatefulStandardNormalV2Attr func(optionalAttr) - -// StatefulStandardNormalV2Dtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatefulStandardNormalV2Dtype(value tf.DataType) StatefulStandardNormalV2Attr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random values from a normal distribution. -// -// The generated values will have mean 0 and standard deviation 1. -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// shape: The shape of the output tensor. -// -// Returns A tensor of the specified shape filled with random normal values. -func StatefulStandardNormalV2(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulStandardNormalV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatefulStandardNormalV2", - Input: []tf.Input{ - resource, algorithm, shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolGradWithArgmaxAttr is an optional argument to MaxPoolGradWithArgmax. -type MaxPoolGradWithArgmaxAttr func(optionalAttr) - -// MaxPoolGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. -// -// value: Whether to include batch dimension in flattened index of `argmax`. -// If not specified, defaults to false -func MaxPoolGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradWithArgmaxAttr { - return func(m optionalAttr) { - m["include_batch_in_index"] = value - } -} - -// Computes gradients of the maxpooling function. -// -// Arguments: -// input: The original input. -// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the -// output of `max_pool`. -// argmax: The indices of the maximum values chosen for each output of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients w.r.t. the input of `max_pool`. -func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradWithArgmaxAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradWithArgmax", - Input: []tf.Input{ - input, grad, argmax, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CompilationResultProto indicating the status of the TPU compilation. -func TPUCompilationResult(scope *Scope) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TPUCompilationResult", - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad. -type ResizeNearestNeighborGradAttr func(optionalAttr) - -// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and grad tensors are -// aligned. Defaults to false. -// If not specified, defaults to false -func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// ResizeNearestNeighborGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func ResizeNearestNeighborGradHalfPixelCenters(value bool) ResizeNearestNeighborGradAttr { - return func(m optionalAttr) { - m["half_pixel_centers"] = value - } -} - -// Computes the gradient of nearest neighbor interpolation. -// -// Arguments: -// grads: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The -// original input size. -// -// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients -// with respect to the input image. -func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeNearestNeighborGrad", - Input: []tf.Input{ - grads, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2. -type ResourceApplyFtrlV2Attr func(optionalAttr) - -// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the Ftrl-proximal scheme. -// -// grad_with_shrinkage = grad + 2 * l2_shrinkage * var -// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage -// linear += grad_with_shrinkage + -// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regulariation. Must be a scalar. -// l2: L2 shrinkage regulariation. Must be a scalar. -// -// lr_power: Scaling factor. Must be a scalar. -// -// Returns the created operation. -func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyFtrlV2", - Input: []tf.Input{ - var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Component-wise multiplies a SparseTensor by a dense Tensor. -// -// The output locations corresponding to the implicitly zero elements in the sparse -// tensor will be zero (i.e., will not take up storage space), regardless of the -// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). -// -// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not -// the other direction. -// -// Arguments: -// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// dense: `R`-D. The dense Tensor operand. -// -// Returns 1-D. The `N` values that are operated on. -func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseDenseCwiseMul", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, dense, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Aggregates the summary of accumulated stats for the batch. -// -// The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket. -// -// Arguments: -// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. -// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. -// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. -// feature: int32; Rank 2 feature Tensors (shape=[batch_size, feature_dimension]). -// max_splits: int; the maximum number of splits possible in the whole tree. -// num_buckets: int; equals to the maximum possible value of bucketized feature. -// -// Returns output Rank 4 Tensor (shape=[splits, feature_dimension, buckets, logits_dimension + hessian_dimension]) -// containing accumulated stats for each node, feature dimension and bucket. -func BoostedTreesAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "BoostedTreesAggregateStats", - Input: []tf.Input{ - node_ids, gradients, hessians, feature, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Deprecated. Use TensorArrayGradV3 -// -// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3 -func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorArrayWriteV2", - Input: []tf.Input{ - handle, index, value, flow_in, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ArgMaxAttr is an optional argument to ArgMax. -type ArgMaxAttr func(optionalAttr) - -// ArgMaxOutputType sets the optional output_type attribute to value. -// If not specified, defaults to DT_INT64 -func ArgMaxOutputType(value tf.DataType) ArgMaxAttr { - return func(m optionalAttr) { - m["output_type"] = value - } -} - -// Returns the index with the largest value across dimensions of a tensor. -// -// Note that in case of ties the identity of the return value is not guaranteed. -// -// Usage: -// ```python -// import tensorflow as tf -// a = [1, 10, 26.9, 2.8, 166.32, 62.3] -// b = tf.math.argmax(input = a) -// c = tf.keras.backend.eval(b) -// # c = 4 -// # here a[4] = 166.32 which is the largest element of a across axis 0 -// ``` -// -// Arguments: -// -// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. -// Describes which dimension of the input Tensor to reduce across. For vectors, -// use dimension = 0. -func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ArgMax", - Input: []tf.Input{ - input, dimension, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates and returns an empty tensor list. -// -// All list elements must be tensors of dtype element_dtype and shape compatible -// with element_shape. -// -// handle: an empty tensor list. -// element_dtype: the type of elements in the list. -// element_shape: a shape compatible with that of elements in the list. -func EmptyTensorList(scope *Scope, element_shape tf.Output, max_num_elements tf.Output, element_dtype tf.DataType) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"element_dtype": element_dtype} - opspec := tf.OpSpec{ - Type: "EmptyTensorList", - Input: []tf.Input{ - element_shape, max_num_elements, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// An Op to exchange data across TPU replicas. -// -// On each replica, the input is split into `split_count` blocks along -// `split_dimension` and send to the other replicas given group_assignment. After -// receiving `split_count` - 1 blocks from other replicas, we concatenate the -// blocks along `concat_dimension` as the output. -// -// For example, suppose there are 2 TPU replicas: -// replica 0 receives input: `[[A, B]]` -// replica 1 receives input: `[[C, D]]` -// -// group_assignment=`[[0, 1]]` -// concat_dimension=0 -// split_dimension=1 -// split_count=2 -// -// replica 0's output: `[[A], [C]]` -// replica 1's output: `[[B], [D]]` -// -// Arguments: -// input: The local input to the sum. -// group_assignment: An int32 tensor with shape -// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the -// replica ids in the ith subgroup. -// concat_dimension: The dimension number to concatenate. -// split_dimension: The dimension number to split. -// split_count: The number of splits, this number must equal to the sub-group -// size(group_assignment.get_shape()[1]) -// -// Returns The exchanged result. -func AllToAll(scope *Scope, input tf.Output, group_assignment tf.Output, concat_dimension int64, split_dimension int64, split_count int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"concat_dimension": concat_dimension, "split_dimension": split_dimension, "split_count": split_count} - opspec := tf.OpSpec{ - Type: "AllToAll", - Input: []tf.Input{ - input, group_assignment, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes natural logarithm of x element-wise. -// -// I.e., \\(y = \log_e x\\). -func Log(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Log", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Gather ragged slices from `params` axis `0` according to `indices`. -// -// Outputs a `RaggedTensor` output composed from `output_dense_values` and -// `output_nested_splits`, such that: -// -// ```python -// output.shape = indices.shape + params.shape[1:] -// output.ragged_rank = indices.shape.ndims + params.ragged_rank -// output[i...j, d0...dn] = params[indices[i...j], d0...dn] -// ``` -// -// where -// -// * `params = -// ragged.from_nested_row_splits(params_dense_values, params_nested_splits)` -// provides the values that should be gathered. -// * `indices` ia a dense tensor with dtype `int32` or `int64`, indicating which -// values should be gathered. -// * `output = -// ragged.from_nested_row_splits(output_dense_values, output_nested_splits)` -// is the output tensor. -// -// (Note: This c++ op is used to implement the higher-level python -// `tf.ragged.gather` op, which also supports ragged indices.) -// -// -// Arguments: -// params_nested_splits: The `nested_row_splits` tensors that define the row-partitioning for the -// `params` RaggedTensor input. -// params_dense_values: The `flat_values` for the `params` RaggedTensor. There was a terminology change -// at the python level from dense_values to flat_values, so dense_values is the -// deprecated name. -// indices: Indices in the outermost dimension of `params` of the values that should be -// gathered. -// OUTPUT_RAGGED_RANK: The ragged rank of the output RaggedTensor. `output_nested_splits` will contain -// this number of `row_splits` tensors. This value should equal -// `indices.shape.ndims + params.ragged_rank - 1`. -// -// Returns The `nested_row_splits` tensors that define the row-partitioning for the -// returned RaggedTensor.The `flat_values` for the returned RaggedTensor. -func RaggedGather(scope *Scope, params_nested_splits []tf.Output, params_dense_values tf.Output, indices tf.Output, OUTPUT_RAGGED_RANK int64) (output_nested_splits []tf.Output, output_dense_values tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"OUTPUT_RAGGED_RANK": OUTPUT_RAGGED_RANK} - opspec := tf.OpSpec{ - Type: "RaggedGather", - Input: []tf.Input{ - tf.OutputList(params_nested_splits), params_dense_values, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { - scope.UpdateErr("RaggedGather", err) - return - } - output_dense_values = op.Output(idx) - return output_nested_splits, output_dense_values -} - -// Makes a new iterator from the given `dataset` and stores it in `iterator`. -// -// This operation may be executed multiple times. Each execution will reset the -// iterator in `iterator` to the first element of `dataset`. -// -// Returns the created operation. -func MakeIterator(scope *Scope, dataset tf.Output, iterator tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MakeIterator", - Input: []tf.Input{ - dataset, iterator, - }, - } - return scope.AddOperation(opspec) -} - -// Enqueue a Tensor on the computation outfeed. -// -// Arguments: -// input: A tensor that will be inserted into the outfeed queue. -// -// Returns the created operation. -func OutfeedEnqueue(scope *Scope, input tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OutfeedEnqueue", - Input: []tf.Input{ - input, - }, - } - return scope.AddOperation(opspec) -} - -// Enqueue multiple Tensor values on the computation outfeed. -// -// Arguments: -// inputs: A list of tensors that will be inserted into the outfeed queue as an -// XLA tuple. -// -// Returns the created operation. -func OutfeedEnqueueTuple(scope *Scope, inputs []tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "OutfeedEnqueueTuple", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - } - return scope.AddOperation(opspec) -} - -// Extract `patches` from `input` and put them in the "depth" output dimension. 3D extension of `extract_image_patches`. -// -// Arguments: -// input: 5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`. -// ksizes: The size of the sliding window for each dimension of `input`. -// strides: 1-D of length 5. How far the centers of two consecutive patches are in -// `input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`. -// padding: The type of padding algorithm to use. -// -// We specify the size-related attributes as: -// -// ```python -// ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1] -// strides = [1, stride_planes, strides_rows, strides_cols, 1] -// ``` -// -// Returns 5-D Tensor with shape `[batch, out_planes, out_rows, out_cols, -// ksize_planes * ksize_rows * ksize_cols * depth]` containing patches -// with size `ksize_planes x ksize_rows x ksize_cols x depth` vectorized -// in the "depth" dimension. Note `out_planes`, `out_rows` and `out_cols` -// are the dimensions of the output patches. -func ExtractVolumePatches(scope *Scope, input tf.Output, ksizes []int64, strides []int64, padding string) (patches tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "ExtractVolumePatches", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the matrix square root of one or more square matrices: -// -// matmul(sqrtm(A), sqrtm(A)) = A -// -// The input matrix should be invertible. If the input matrix is real, it should -// have no eigenvalues which are real and negative (pairs of complex conjugate -// eigenvalues are allowed). -// -// The matrix square root is computed by first reducing the matrix to -// quasi-triangular form with the real Schur decomposition. The square root -// of the quasi-triangular matrix is then computed directly. Details of -// the algorithm can be found in: Nicholas J. Higham, "Computing real -// square roots of a real matrix", Linear Algebra Appl., 1987. -// -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. The output is a tensor of the same shape as the input -// containing the matrix square root for all input submatrices `[..., :, :]`. -// -// Arguments: -// input: Shape is `[..., M, M]`. -// -// Returns Shape is `[..., M, M]`. -// -// @compatibility(scipy) -// Equivalent to scipy.linalg.sqrtm -// @end_compatibility -func MatrixSquareRoot(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixSquareRoot", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Connects outputs of an N-way replicated computation to N outputs. -func TPUReplicatedOutput(scope *Scope, input tf.Output, num_replicas int64) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_replicas": num_replicas} - opspec := tf.OpSpec{ - Type: "TPUReplicatedOutput", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("TPUReplicatedOutput", err) - return - } - return outputs -} - -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process and will never change. However, it is not suitable for cryptography. -// This function may be used when CPU time is scarce and inputs are trusted or -// unimportant. There is a risk of adversaries constructing inputs that all hash -// to the same bucket. To prevent this problem, use a strong hash function with -// `tf.string_to_hash_bucket_strong`. -// -// Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. -// -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "StringToHashBucketFast", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 3D real-valued fast Fourier transform. -// -// Computes the 3-dimensional discrete Fourier transform of a real-valued signal -// over the inner-most 3 dimensions of `input`. -// -// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the -// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension -// of `output`: the zero-frequency term, followed by the `fft_length / 2` -// positive-frequency terms. -// -// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A float32 tensor. -// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. -// -// Returns A complex64 tensor of the same rank as `input`. The inner-most 3 -// dimensions of `input` are replaced with the their 3D Fourier transform. The -// inner-most dimension contains `fft_length / 2 + 1` unique frequency -// components. -// -// @compatibility(numpy) -// Equivalent to np.fft.rfftn with 3 dimensions. -// @end_compatibility -func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RFFT3D", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// HistogramFixedWidthAttr is an optional argument to HistogramFixedWidth. -type HistogramFixedWidthAttr func(optionalAttr) - -// HistogramFixedWidthDtype sets the optional dtype attribute to value. -// If not specified, defaults to DT_INT32 -func HistogramFixedWidthDtype(value tf.DataType) HistogramFixedWidthAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Return histogram of values. -// -// Given the tensor `values`, this operation returns a rank 1 histogram counting -// the number of entries in `values` that fall into every bin. The bins are -// equal width and determined by the arguments `value_range` and `nbins`. -// -// ```python -// # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) -// nbins = 5 -// value_range = [0.0, 5.0] -// new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] -// -// with tf.get_default_session() as sess: -// hist = tf.histogram_fixed_width(new_values, value_range, nbins=5) -// variables.global_variables_initializer().run() -// sess.run(hist) => [2, 1, 1, 0, 2] -// ``` -// -// Arguments: -// values: Numeric `Tensor`. -// value_range: Shape [2] `Tensor` of same `dtype` as `values`. -// values <= value_range[0] will be mapped to hist[0], -// values >= value_range[1] will be mapped to hist[-1]. -// nbins: Scalar `int32 Tensor`. Number of histogram bins. -// -// Returns A 1-D `Tensor` holding histogram of values. -func HistogramFixedWidth(scope *Scope, values tf.Output, value_range tf.Output, nbins tf.Output, optional ...HistogramFixedWidthAttr) (out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "HistogramFixedWidth", - Input: []tf.Input{ - values, value_range, nbins, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. -type ResourceApplyPowerSignAttr func(optionalAttr) - -// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and m tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the AddSign update. -// -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g -// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g -// variable <- variable - lr_t * update -// -// Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// logbase: Must be a scalar. -// sign_decay: Must be a scalar. -// beta: Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyPowerSign", - Input: []tf.Input{ - var_, m, lr, logbase, sign_decay, beta, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ExperimentalStatsAggregatorHandleAttr is an optional argument to ExperimentalStatsAggregatorHandle. -type ExperimentalStatsAggregatorHandleAttr func(optionalAttr) - -// ExperimentalStatsAggregatorHandleContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func ExperimentalStatsAggregatorHandleContainer(value string) ExperimentalStatsAggregatorHandleAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// ExperimentalStatsAggregatorHandleSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func ExperimentalStatsAggregatorHandleSharedName(value string) ExperimentalStatsAggregatorHandleAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Creates a statistics manager resource. -func ExperimentalStatsAggregatorHandle(scope *Scope, optional ...ExperimentalStatsAggregatorHandleAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ExperimentalStatsAggregatorHandle", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse. -type SparseReduceSumSparseAttr func(optionalAttr) - -// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the sum of elements across dimensions of a SparseTensor. -// -// This Op takes a SparseTensor and is the sparse counterpart to -// `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a -// SparseTensor. -// -// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained -// with length 1. -// -// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor -// with a single element is returned. Additionally, the axes can be negative, -// which are interpreted according to the indexing rules in Python. -// -// Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. -func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SparseReduceSumSparse", - Input: []tf.Input{ - input_indices, input_values, input_shape, reduction_axes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap. -type AddManySparseToTensorsMapAttr func(optionalAttr) - -// AddManySparseToTensorsMapContainer sets the optional container attribute to value. -// -// value: The container name for the `SparseTensorsMap` created by this op. -// If not specified, defaults to "" -func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value. -// -// value: The shared name for the `SparseTensorsMap` created by this op. -// If blank, the new Operation's unique name is used. -// If not specified, defaults to "" -func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles. -// -// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`, -// `sparse_values`, and `sparse_shape`, where -// -// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R``` -// -// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor` -// having a first `sparse_indices` column taking values between `[0, N)`, where -// the minibatch size `N == sparse_shape[0]`. -// -// The input `SparseTensor` must have rank `R` greater than 1, and the first -// dimension is treated as the minibatch dimension. Elements of the `SparseTensor` -// must be sorted in increasing order of this first dimension. The stored -// `SparseTensor` objects pointed to by each row of the output `sparse_handles` -// will have rank `R-1`. -// -// The `SparseTensor` values can then be read out as part of a minibatch by passing -// the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure -// the correct `SparseTensorsMap` is accessed, ensure that the same -// `container` and `shared_name` are passed to that Op. If no `shared_name` -// is provided here, instead use the *name* of the Operation created by calling -// `AddManySparseToTensorsMap` as the `shared_name` passed to -// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. -// -// Arguments: -// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. -// `sparse_indices[:, 0]` must be ordered values in `[0, N)`. -// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. -// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. -// The minibatch size `N == sparse_shape[0]`. -// -// Returns 1-D. The handles of the `SparseTensor` now stored in the -// `SparseTensorsMap`. Shape: `[N]`. -func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AddManySparseToTensorsMap", - Input: []tf.Input{ - sparse_indices, sparse_values, sparse_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StringToNumberAttr is an optional argument to StringToNumber. -type StringToNumberAttr func(optionalAttr) - -// StringToNumberOutType sets the optional out_type attribute to value. -// -// value: The numeric type to interpret each string in `string_tensor` as. -// If not specified, defaults to DT_FLOAT -func StringToNumberOutType(value tf.DataType) StringToNumberAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Converts each string in the input Tensor to the specified numeric type. -// -// (Note that int32 overflow results in an error while float overflow -// results in a rounded value.) -// -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StringToNumber", - Input: []tf.Input{ - string_tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingFTRLParametersAttr is an optional argument to LoadTPUEmbeddingFTRLParameters. -type LoadTPUEmbeddingFTRLParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingFTRLParametersTableId(value int64) LoadTPUEmbeddingFTRLParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingFTRLParametersTableName(value string) LoadTPUEmbeddingFTRLParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load FTRL embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the FTRL optimization algorithm. -// accumulators: Value of accumulators used in the FTRL optimization algorithm. -// linears: Value of linears used in the FTRL optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingFTRLParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingFTRLParameters", - Input: []tf.Input{ - parameters, accumulators, linears, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// FusedBatchNormAttr is an optional argument to FusedBatchNorm. -type FusedBatchNormAttr func(optionalAttr) - -// FusedBatchNormEpsilon sets the optional epsilon attribute to value. -// -// value: A small float number added to the variance of x. -// If not specified, defaults to 0.0001 -func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr { - return func(m optionalAttr) { - m["epsilon"] = value - } -} - -// FusedBatchNormDataFormat sets the optional data_format attribute to value. -// -// value: The data format for x and y. Either "NHWC" (default) or "NCHW". -// If not specified, defaults to "NHWC" -func FusedBatchNormDataFormat(value string) FusedBatchNormAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// FusedBatchNormIsTraining sets the optional is_training attribute to value. -// -// value: A bool value to indicate the operation is for training (default) -// or inference. -// If not specified, defaults to true -func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr { - return func(m optionalAttr) { - m["is_training"] = value - } -} - -// Batch normalization. -// -// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". -// The size of 1D Tensors matches the dimension C of the 4D Tensors. -// -// Arguments: -// x: A 4D Tensor for input data. -// scale: A 1D Tensor for scaling factor, to scale the normalized x. -// offset: A 1D Tensor for offset, to shift to the normalized x. -// mean: A 1D Tensor for population mean. Used for inference only; -// must be empty for training. -// variance: A 1D Tensor for population variance. Used for inference only; -// must be empty for training. -// -// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow -// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by -// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused -// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance -// in the cuDNN case), to be reused in the gradient computation. -func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FusedBatchNorm", - Input: []tf.Input{ - x, scale, offset, mean, variance, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) -} - -// ComplexAbsAttr is an optional argument to ComplexAbs. -type ComplexAbsAttr func(optionalAttr) - -// ComplexAbsTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func ComplexAbsTout(value tf.DataType) ComplexAbsAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Computes the complex absolute value of a tensor. -// -// Given a tensor `x` of complex numbers, this operation returns a tensor of type -// `float` or `double` that is the absolute value of each element in `x`. All -// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute -// value is computed as \\( \sqrt{a^2 + b^2}\\). -func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ComplexAbs", - Input: []tf.Input{ - x, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// A placeholder op for a value that will be fed into the computation. -// -// Arguments: -// dtype: The type of elements in the tensor. -// shape: The shape of the tensor. -// -// Returns A tensor that will be provided using the infeed mechanism. -func InfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype, "shape": shape} - opspec := tf.OpSpec{ - Type: "InfeedDequeue", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LeakyReluAttr is an optional argument to LeakyRelu. -type LeakyReluAttr func(optionalAttr) - -// LeakyReluAlpha sets the optional alpha attribute to value. -// If not specified, defaults to 0.2 -func LeakyReluAlpha(value float32) LeakyReluAttr { - return func(m optionalAttr) { - m["alpha"] = value - } -} - -// Computes rectified linear: `max(features, features * alpha)`. -func LeakyRelu(scope *Scope, features tf.Output, optional ...LeakyReluAttr) (activations tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LeakyRelu", - Input: []tf.Input{ - features, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the product along segments of a tensor. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. -// -// Computes a tensor such that -// \\(output_i = \prod_j data_j\\) where the product is over `j` such -// that `segment_ids[j] == i`. -// -// If the product is empty for a given segment ID `i`, `output[i] = 1`. -// -//
-// -//
-// -// For example: -// -// ``` -// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) -// tf.segment_prod(c, tf.constant([0, 0, 1])) -// # ==> [[4, 6, 6, 4], -// # [5, 6, 7, 8]] -// ``` -// -// -// Arguments: -// -// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s -// first dimension. Values should be sorted and can be repeated. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SegmentProd", - Input: []tf.Input{ - data, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// OutfeedDequeueAttr is an optional argument to OutfeedDequeue. -type OutfeedDequeueAttr func(optionalAttr) - -// OutfeedDequeueDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. This should be -1 when the Op -// is running on a TPU device, and >= 0 when the Op is running on the CPU -// device. -// If not specified, defaults to -1 -func OutfeedDequeueDeviceOrdinal(value int64) OutfeedDequeueAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// Retrieves a single tensor from the computation outfeed. -// -// This operation will block indefinitely until data is available. -// -// Arguments: -// dtype: The type of elements in the tensor. -// shape: The shape of the tensor. -// -// Returns A tensor that will be read from the device outfeed. -func OutfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...OutfeedDequeueAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype, "shape": shape} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OutfeedDequeue", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SkipgramAttr is an optional argument to Skipgram. -type SkipgramAttr func(optionalAttr) - -// SkipgramWindowSize sets the optional window_size attribute to value. -// -// value: The number of words to predict to the left and right of the target. -// If not specified, defaults to 5 -func SkipgramWindowSize(value int64) SkipgramAttr { - return func(m optionalAttr) { - m["window_size"] = value - } -} - -// SkipgramMinCount sets the optional min_count attribute to value. -// -// value: The minimum number of word occurrences for it to be included in the -// vocabulary. -// If not specified, defaults to 5 -func SkipgramMinCount(value int64) SkipgramAttr { - return func(m optionalAttr) { - m["min_count"] = value - } -} - -// SkipgramSubsample sets the optional subsample attribute to value. -// -// value: Threshold for word occurrence. Words that appear with higher -// frequency will be randomly down-sampled. Set to 0 to disable. -// If not specified, defaults to 0.001 -func SkipgramSubsample(value float32) SkipgramAttr { - return func(m optionalAttr) { - m["subsample"] = value - } -} - -// Parses a text file and creates a batch of examples. -// -// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result -// -// Arguments: -// filename: The corpus's text file name. -// batch_size: The size of produced batch. -// -// Returns A vector of words in the corpus.Frequencies of words. Sorted in the non-ascending order.Number of words per epoch in the data file.The current epoch number.The total number of words processed so far.A vector of word ids.A vector of word ids. -func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Skipgram", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) -} - -// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation. -type DenseToSparseSetOperationAttr func(optionalAttr) - -// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Applies set operation along last dimension of `Tensor` and `SparseTensor`. -// -// See SetOperationOp::SetOperationFromContext for values of `set_operation`. -// -// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, -// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same -// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but -// ignored. -// -// If `validate_indices` is `True`, this op validates the order and range of `set2` -// indices. -// -// Output `result` is a `SparseTensor` represented by `result_indices`, -// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this -// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` -// dimension contains the result of `set_operation` applied to the corresponding -// `[0...n-1]` dimension of `set`. -// -// Arguments: -// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. -// Dimension `n` contains values in a set, duplicates are allowed but ignored. -// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major -// order. -// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major -// order. -// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must -// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the -// max set size across `n-1` dimensions. -// -// -// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is -// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` -// is the max result set size across all `0...n-1` dimensions. -func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"set_operation": set_operation} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DenseToSparseSetOperation", - Input: []tf.Input{ - set1, set2_indices, set2_values, set2_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// LoadTPUEmbeddingADAMParametersAttr is an optional argument to LoadTPUEmbeddingADAMParameters. -type LoadTPUEmbeddingADAMParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingADAMParametersTableId(value int64) LoadTPUEmbeddingADAMParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingADAMParametersTableName(value string) LoadTPUEmbeddingADAMParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load ADAM embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the ADAM optimization algorithm. -// momenta: Value of momenta used in the ADAM optimization algorithm. -// velocities: Value of velocities used in the ADAM optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingADAMParameters(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingADAMParameters", - Input: []tf.Input{ - parameters, momenta, velocities, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. -type ResourceApplyAdagradAttr func(optionalAttr) - -// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. -// If not specified, defaults to true -func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { - return func(m optionalAttr) { - m["update_slots"] = value - } -} - -// Update '*var' according to the adagrad scheme. -// -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdagrad", - Input: []tf.Input{ - var_, accum, lr, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// StagePeekAttr is an optional argument to StagePeek. -type StagePeekAttr func(optionalAttr) - -// StagePeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StagePeekCapacity(value int64) StagePeekAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// StagePeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StagePeekMemoryLimit(value int64) StagePeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// StagePeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StagePeekContainer(value string) StagePeekAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// StagePeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StagePeekSharedName(value string) StagePeekAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op peeks at the values at the specified index. If the -// -// underlying container does not contain sufficient elements -// this op will block until it does. This Op is optimized for -// performance. -func StagePeek(scope *Scope, index tf.Output, dtypes []tf.DataType, optional ...StagePeekAttr) (values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StagePeek", - Input: []tf.Input{ - index, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("StagePeek", err) - return - } - return values -} - -// Returns the truth value of (x != y) element-wise. -// -// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NotEqual", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StaticRegexReplaceAttr is an optional argument to StaticRegexReplace. -type StaticRegexReplaceAttr func(optionalAttr) - -// StaticRegexReplaceReplaceGlobal sets the optional replace_global attribute to value. -// -// value: If True, the replacement is global, otherwise the replacement -// is done only on the first match. -// If not specified, defaults to true -func StaticRegexReplaceReplaceGlobal(value bool) StaticRegexReplaceAttr { - return func(m optionalAttr) { - m["replace_global"] = value - } -} - -// Replaces the match of pattern in input with rewrite. -// -// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) -// -// Arguments: -// input: The text to be processed. -// pattern: The regular expression to match the input. -// rewrite: The rewrite to be applied to the matched expression. -// -// Returns The text after applying pattern and rewrite. -func StaticRegexReplace(scope *Scope, input tf.Output, pattern string, rewrite string, optional ...StaticRegexReplaceAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"pattern": pattern, "rewrite": rewrite} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StaticRegexReplace", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// A TPU core selector Op. -// -// This Op produces a set of TPU cores (for warm-up) or a single TPU core -// (for regular inference) to execute the TPU program on. The output is -// consumed by TPUPartitionedCall. -// -// Returns A vector 1 or more TPU cores. -func TPUOrdinalSelector(scope *Scope) (device_ordinals tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TPUOrdinalSelector", - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UnicodeTranscodeAttr is an optional argument to UnicodeTranscode. -type UnicodeTranscodeAttr func(optionalAttr) - -// UnicodeTranscodeErrors sets the optional errors attribute to value. -// -// value: Error handling policy when there is invalid formatting found in the input. -// The value of 'strict' will cause the operation to produce a InvalidArgument -// error on any invalid input formatting. A value of 'replace' (the default) will -// cause the operation to replace any invalid formatting in the input with the -// `replacement_char` codepoint. A value of 'ignore' will cause the operation to -// skip any invalid formatting in the input and produce no corresponding output -// character. -// If not specified, defaults to "replace" -func UnicodeTranscodeErrors(value string) UnicodeTranscodeAttr { - return func(m optionalAttr) { - m["errors"] = value - } -} - -// UnicodeTranscodeReplacementChar sets the optional replacement_char attribute to value. -// -// value: The replacement character codepoint to be used in place of any invalid -// formatting in the input when `errors='replace'`. Any valid unicode codepoint may -// be used. The default value is the default unicode replacement character is -// 0xFFFD or U+65533.) -// -// Note that for UTF-8, passing a replacement character expressible in 1 byte, such -// as ' ', will preserve string alignment to the source since invalid bytes will be -// replaced with a 1-byte replacement. For UTF-16-BE and UTF-16-LE, any 1 or 2 byte -// replacement character will preserve byte alignment to the source. -// If not specified, defaults to 65533 -func UnicodeTranscodeReplacementChar(value int64) UnicodeTranscodeAttr { - return func(m optionalAttr) { - m["replacement_char"] = value - } -} - -// UnicodeTranscodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. -// -// value: Whether to replace the C0 control characters (00-1F) with the -// `replacement_char`. Default is false. -// If not specified, defaults to false -func UnicodeTranscodeReplaceControlCharacters(value bool) UnicodeTranscodeAttr { - return func(m optionalAttr) { - m["replace_control_characters"] = value - } -} - -// Transcode the input text from a source encoding to a destination encoding. -// -// The input is a string tensor of any shape. The output is a string tensor of -// the same shape containing the transcoded strings. Output strings are always -// valid unicode. If the input contains invalid encoding positions, the -// `errors` attribute sets the policy for how to deal with them. If the default -// error-handling policy is used, invalid formatting will be substituted in the -// output by the `replacement_char`. If the errors policy is to `ignore`, any -// invalid encoding positions in the input are skipped and not included in the -// output. If it set to `strict` then any invalid formatting will result in an -// InvalidArgument error. -// -// This operation can be used with `output_encoding = input_encoding` to enforce -// correct formatting for inputs even if they are already in the desired encoding. -// -// If the input is prefixed by a Byte Order Mark needed to determine encoding -// (e.g. if the encoding is UTF-16 and the BOM indicates big-endian), then that -// BOM will be consumed and not emitted into the output. If the input encoding -// is marked with an explicit endianness (e.g. UTF-16-BE), then the BOM is -// interpreted as a non-breaking-space and is preserved in the output (including -// always for UTF-8). -// -// The end result is that if the input is marked as an explicit endianness the -// transcoding is faithful to all codepoints in the source. If it is not marked -// with an explicit endianness, the BOM is not considered part of the string itself -// but as metadata, and so is not preserved in the output. -// -// Arguments: -// input: The text to be processed. Can have any shape. -// input_encoding: Text encoding of the input strings. This is any of the encodings supported -// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. -// output_encoding: The unicode encoding to use in the output. Must be one of -// `"UTF-8", "UTF-16-BE", "UTF-32-BE"`. Multi-byte encodings will be big-endian. -// -// Returns A string tensor containing unicode text encoded using `output_encoding`. -func UnicodeTranscode(scope *Scope, input tf.Output, input_encoding string, output_encoding string, optional ...UnicodeTranscodeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"input_encoding": input_encoding, "output_encoding": output_encoding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UnicodeTranscode", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UnicodeDecodeAttr is an optional argument to UnicodeDecode. -type UnicodeDecodeAttr func(optionalAttr) - -// UnicodeDecodeErrors sets the optional errors attribute to value. -// -// value: Error handling policy when there is invalid formatting found in the input. -// The value of 'strict' will cause the operation to produce a InvalidArgument -// error on any invalid input formatting. A value of 'replace' (the default) will -// cause the operation to replace any invalid formatting in the input with the -// `replacement_char` codepoint. A value of 'ignore' will cause the operation to -// skip any invalid formatting in the input and produce no corresponding output -// character. -// If not specified, defaults to "replace" -func UnicodeDecodeErrors(value string) UnicodeDecodeAttr { - return func(m optionalAttr) { - m["errors"] = value - } -} - -// UnicodeDecodeReplacementChar sets the optional replacement_char attribute to value. -// -// value: The replacement character codepoint to be used in place of any invalid -// formatting in the input when `errors='replace'`. Any valid unicode codepoint may -// be used. The default value is the default unicode replacement character is -// 0xFFFD or U+65533.) -// If not specified, defaults to 65533 -func UnicodeDecodeReplacementChar(value int64) UnicodeDecodeAttr { - return func(m optionalAttr) { - m["replacement_char"] = value - } -} - -// UnicodeDecodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. -// -// value: Whether to replace the C0 control characters (00-1F) with the -// `replacement_char`. Default is false. -// If not specified, defaults to false -func UnicodeDecodeReplaceControlCharacters(value bool) UnicodeDecodeAttr { - return func(m optionalAttr) { - m["replace_control_characters"] = value - } -} - -// UnicodeDecodeTsplits sets the optional Tsplits attribute to value. -// If not specified, defaults to DT_INT64 -func UnicodeDecodeTsplits(value tf.DataType) UnicodeDecodeAttr { - return func(m optionalAttr) { - m["Tsplits"] = value - } -} - -// Decodes each string in `input` into a sequence of Unicode code points. -// -// The character codepoints for all strings are returned using a single vector -// `char_values`, with strings expanded to characters in row-major order. -// -// The `row_splits` tensor indicates where the codepoints for -// each input string begin and end within the `char_values` tensor. -// In particular, the values for the `i`th -// string (in row-major order) are stored in the slice -// `[row_splits[i]:row_splits[i+1]]`. Thus: -// -// * `char_values[row_splits[i]+j]` is the Unicode codepoint for the `j`th -// character in the `i`th string (in row-major order). -// * `row_splits[i+1] - row_splits[i]` is the number of characters in the `i`th -// string (in row-major order). -// -// Arguments: -// input: The text to be decoded. Can have any shape. Note that the output is flattened -// to a vector of char values. -// input_encoding: Text encoding of the input strings. This is any of the encodings supported -// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. -// -// Returns A 1D int32 tensor containing the row splits.A 1D int32 Tensor containing the decoded codepoints. -func UnicodeDecode(scope *Scope, input tf.Output, input_encoding string, optional ...UnicodeDecodeAttr) (row_splits tf.Output, char_values tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"input_encoding": input_encoding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UnicodeDecode", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Returns element-wise smallest integer not less than x. -func Ceil(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Ceil", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyAdaMaxAttr is an optional argument to ResourceApplyAdaMax. -type ResourceApplyAdaMaxAttr func(optionalAttr) - -// ResourceApplyAdaMaxUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, m, and v tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdaMaxUseLocking(value bool) ResourceApplyAdaMaxAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the AdaMax algorithm. -// -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g -// v_t <- max(beta2 * v_{t-1}, abs(g)) -// variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) -// -// Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// v: Should be from a Variable(). -// beta1_power: Must be a scalar. -// lr: Scaling factor. Must be a scalar. -// beta1: Momentum factor. Must be a scalar. -// beta2: Momentum factor. Must be a scalar. -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdaMax(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdaMaxAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdaMax", - Input: []tf.Input{ - var_, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Bucketize each feature based on bucket boundaries. -// -// An op that returns a list of float tensors, where each tensor represents the -// bucketized values for a single feature. -// -// Arguments: -// float_values: float; List of Rank 1 Tensor each containing float values for a single feature. -// bucket_boundaries: float; List of Rank 1 Tensors each containing the bucket boundaries for a single -// feature. -// -// Returns int; List of Rank 1 Tensors each containing the bucketized values for a single feature. -func BoostedTreesBucketize(scope *Scope, float_values []tf.Output, bucket_boundaries []tf.Output) (buckets []tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BoostedTreesBucketize", - Input: []tf.Input{ - tf.OutputList(float_values), tf.OutputList(bucket_boundaries), - }, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if buckets, idx, err = makeOutputList(op, idx, "buckets"); err != nil { - scope.UpdateErr("BoostedTreesBucketize", err) - return - } - return buckets -} - -// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D. -type FusedResizeAndPadConv2DAttr func(optionalAttr) - -// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr { - return func(m optionalAttr) { - m["resize_align_corners"] = value - } -} - -// Performs a resize and padding as a preprocess during a convolution. -// -// It's often possible to do spatial transformations more efficiently as part of -// the packing stage of a convolution, so this op allows for an optimized -// implementation where these stages are fused together. This prevents the need to -// write out the intermediate results as whole tensors, reducing memory pressure, -// and we can get some latency gains by merging the transformation calculations. -// The data_format attribute for Conv2D isn't supported by this op, and defaults to -// 'NHWC' order. -// Internally this op uses a single per-graph scratch buffer, which means that it -// will block if multiple versions are being run in parallel. This is because this -// operator is primarily an optimization to minimize memory usage. -// -// Arguments: -// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. -// size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// paddings: A two-column matrix specifying the padding sizes. The number of -// rows must be the same as the rank of `input`. -// filter: 4-D with shape -// `[filter_height, filter_width, in_channels, out_channels]`. -// -// strides: 1-D of length 4. The stride of the sliding window for each dimension -// of `input`. Must be in the same order as the dimension specified with format. -// padding: The type of padding algorithm to use. -func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FusedResizeAndPadConv2D", - Input: []tf.Input{ - input, size, paddings, filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform over the inner-most -// 2 dimensions of `input`. -// -// Arguments: -// input: A complex tensor. -// -// Returns A complex tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft2 -// @end_compatibility -func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UpperBoundAttr is an optional argument to UpperBound. -type UpperBoundAttr func(optionalAttr) - -// UpperBoundOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func UpperBoundOutType(value tf.DataType) UpperBoundAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Applies upper_bound(sorted_search_values, values) along each row. -// -// Each set of rows with the same index in (sorted_inputs, values) is treated -// independently. The resulting row is the equivalent of calling -// `np.searchsorted(sorted_inputs, values, side='right')`. -// -// The result is not a global index to the entire -// `Tensor`, but rather just the index in the last dimension. -// -// A 2-D example: -// sorted_sequence = [[0, 3, 9, 9, 10], -// [1, 2, 3, 4, 5]] -// values = [[2, 4, 9], -// [0, 2, 6]] -// -// result = UpperBound(sorted_sequence, values) -// -// result == [[1, 2, 4], -// [0, 2, 5]] -// -// Arguments: -// sorted_inputs: 2-D Tensor where each row is ordered. -// values: 2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains -// the values that will be searched for in `sorted_search_values`. -// -// Returns A `Tensor` with the same shape as `values`. It contains the last scalar index -// into the last dimension where values can be inserted without changing the -// ordered property. -func UpperBound(scope *Scope, sorted_inputs tf.Output, values tf.Output, optional ...UpperBoundAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UpperBound", - Input: []tf.Input{ - sorted_inputs, values, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reshapes a quantized tensor as per the Reshape op. -// -// ``` -// -// Arguments: -// -// shape: Defines the shape of the output tensor. -// input_min: The minimum value of the input. -// input_max: The maximum value of the input. -// -// Returns This value is copied from input_min.This value is copied from input_max. -func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "QuantizedReshape", - Input: []tf.Input{ - tensor, shape, input_min, input_max, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Pads a tensor. -// -// This operation pads `input` according to the `paddings` and `constant_values` -// you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is -// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates -// how many padding values to add before the contents of `input` in that dimension, -// and `paddings[D, 1]` indicates how many padding values to add after the contents -// of `input` in that dimension. `constant_values` is a scalar tensor of the same -// type as `input` that indicates the value to use for padding `input`. -// -// The padded size of each dimension D of the output is: -// -// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` -// -// For example: -// -// ``` -// # 't' is [[1, 1], [2, 2]] -// # 'paddings' is [[1, 1], [2, 2]] -// # 'constant_values' is 0 -// # rank of 't' is 2 -// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] -// [0, 0, 1, 1, 0, 0] -// [0, 0, 2, 2, 0, 0] -// [0, 0, 0, 0, 0, 0]] -// ``` -func PadV2(scope *Scope, input tf.Output, paddings tf.Output, constant_values tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "PadV2", - Input: []tf.Input{ - input, paddings, constant_values, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Adds `bias` to `value`. -// -// This is a deprecated version of BiasAdd and will be soon removed. -// -// This is a special case of `tf.add` where `bias` is restricted to be 1-D. -// Broadcasting is supported, so `value` may have any number of dimensions. -// -// Arguments: -// value: Any number of dimensions. -// bias: 1-D with size the last dimension of `value`. -// -// Returns Broadcasted sum of `value` and `bias`. -func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BiasAddV1", - Input: []tf.Input{ - value, bias, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Locks a mutex resource. The output is the lock. So long as the lock tensor -// -// is alive, any other request to use `MutexLock` with this mutex will wait. -// -// This is particularly useful for creating a critical section when used in -// conjunction with `MutexLockIdentity`: -// -// ```python -// -// mutex = mutex_v2( -// shared_name=handle_name, container=container, name=name) -// -// def execute_in_critical_section(fn, *args, **kwargs): -// lock = gen_resource_variable_ops.mutex_lock(mutex) -// -// with ops.control_dependencies([lock]): -// r = fn(*args, **kwargs) -// -// with ops.control_dependencies(nest.flatten(r)): -// with ops.colocate_with(mutex): -// ensure_lock_exists = mutex_lock_identity(lock) -// -// # Make sure that if any element of r is accessed, all of -// # them are executed together. -// r = nest.map_structure(tf.identity, r) -// -// with ops.control_dependencies([ensure_lock_exists]): -// return nest.map_structure(tf.identity, r) -// ``` -// -// While `fn` is running in the critical section, no other functions which wish to -// use this critical section may run. -// -// Often the use case is that two executions of the same graph, in parallel, -// wish to run `fn`; and we wish to ensure that only one of them executes -// at a time. This is especially important if `fn` modifies one or more -// variables at a time. -// -// It is also useful if two separate functions must share a resource, but we -// wish to ensure the usage is exclusive. -// -// Arguments: -// mutex: The mutex resource to lock. -// -// Returns A tensor that keeps a shared pointer to a lock on the mutex; -// when the Tensor is destroyed, the use count on the shared pointer is decreased -// by 1. When it reaches 0, the lock is released. -func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MutexLock", - Input: []tf.Input{ - mutex, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes softsign: `features / (abs(features) + 1)`. -func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Softsign", - Input: []tf.Input{ - features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns 0 if x == 0, and x / y otherwise, elementwise. -func Xdivy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Xdivy", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdagradParametersGradAccumDebug. -type LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) - -// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Adagrad embedding parameters with debug support. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the Adagrad optimization algorithm. -// accumulators: Value of accumulators used in the Adagrad optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the Adagrad optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingAdagradParametersGradAccumDebug", - Input: []tf.Input{ - parameters, accumulators, gradient_accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Fast Fourier transform. -// -// Computes the 1-dimensional discrete Fourier transform over the inner-most -// dimension of `input`. -// -// Arguments: -// input: A complex tensor. -// -// Returns A complex tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its 1D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fft -// @end_compatibility -func FFT(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. -type TensorArrayGatherV3Attr func(optionalAttr) - -// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. -// -// value: The expected shape of an element, if known. Used to -// validate the shapes of TensorArray elements. If this shape is not -// fully specified, gathering zero-size TensorArrays is an error. -// If not specified, defaults to -func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { - return func(m optionalAttr) { - m["element_shape"] = value - } -} - -// Gather specific elements from the TensorArray into output `value`. -// -// All elements selected by `indices` must have the same shape. -// -// Arguments: -// handle: The handle to a TensorArray. -// indices: The locations in the TensorArray from which to read tensor elements. -// flow_in: A float scalar that enforces proper chaining of operations. -// dtype: The type of the elem that is returned. -// -// Returns All of the elements in the TensorArray, concatenated along a new -// axis (the new dimension 0). -func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorArrayGatherV3", - Input: []tf.Input{ - handle, indices, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Transforms a vector of brain.Example protos (as strings) into typed tensors. -// -// Arguments: -// serialized: A vector containing a batch of binary serialized Example protos. -// names: A vector containing the names of the serialized protos. -// May contain, for example, table key (descriptive) names for the -// corresponding serialized protos. These are purely useful for debugging -// purposes, and the presence of values here has no effect on the output. -// May also be an empty vector if no names are available. -// If non-empty, this vector must be the same length as "serialized". -// sparse_keys: A list of Nsparse string Tensors (scalars). -// The keys expected in the Examples' features associated with sparse values. -// dense_keys: A list of Ndense string Tensors (scalars). -// The keys expected in the Examples' features associated with dense values. -// dense_defaults: A list of Ndense Tensors (some may be empty). -// dense_defaults[j] provides default values -// when the example's feature_map lacks dense_key[j]. If an empty Tensor is -// provided for dense_defaults[j], then the Feature dense_keys[j] is required. -// The input type is inferred from dense_defaults[j], even when it's empty. -// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, -// then the shape of dense_defaults[j] must match that of dense_shapes[j]. -// If dense_shapes[j] has an undefined major dimension (variable strides dense -// feature), dense_defaults[j] must contain a single element: -// the padding element. -// sparse_types: A list of Nsparse types; the data types of data in each Feature -// given in sparse_keys. -// Currently the ParseExample supports DT_FLOAT (FloatList), -// DT_INT64 (Int64List), and DT_STRING (BytesList). -// dense_shapes: A list of Ndense shapes; the shapes of data in each Feature -// given in dense_keys. -// The number of elements in the Feature corresponding to dense_key[j] -// must always equal dense_shapes[j].NumEntries(). -// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output -// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): -// The dense outputs are just the inputs row-stacked by batch. -// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case -// the shape of the output Tensor dense_values[j] will be -// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks -// of elements of length D1 * .... * DN, across all minibatch entries -// in the input. Any minibatch entry with less than M blocks of elements of -// length D1 * ... * DN will be padded with the corresponding default_value -// scalar element along the second dimension. -func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes} - opspec := tf.OpSpec{ - Type: "ParseExample", - Input: []tf.Input{ - serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { - scope.UpdateErr("ParseExample", err) - return - } - if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { - scope.UpdateErr("ParseExample", err) - return - } - if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { - scope.UpdateErr("ParseExample", err) - return - } - if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { - scope.UpdateErr("ParseExample", err) - return - } - return sparse_indices, sparse_values, sparse_shapes, dense_values -} - -// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam. -type ResourceApplyAdamAttr func(optionalAttr) - -// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, m, and v tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value. -// -// value: If `True`, uses the nesterov update. -// If not specified, defaults to false -func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update '*var' according to the Adam algorithm. -// -// $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ -// $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ -// $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ -// $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ -// -// Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// v: Should be from a Variable(). -// beta1_power: Must be a scalar. -// beta2_power: Must be a scalar. -// lr: Scaling factor. Must be a scalar. -// beta1: Momentum factor. Must be a scalar. -// beta2: Momentum factor. Must be a scalar. -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdam", - Input: []tf.Input{ - var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize. -type QuantizeAndDequantizeAttr func(optionalAttr) - -// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value. -// If not specified, defaults to true -func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr { - return func(m optionalAttr) { - m["signed_input"] = value - } -} - -// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value. -// If not specified, defaults to false -func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr { - return func(m optionalAttr) { - m["range_given"] = value - } -} - -// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value. -// If not specified, defaults to 0 -func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr { - return func(m optionalAttr) { - m["input_min"] = value - } -} - -// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value. -// If not specified, defaults to 0 -func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr { - return func(m optionalAttr) { - m["input_max"] = value - } -} - -// Use QuantizeAndDequantizeV2 instead. -// -// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2 -func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizeAndDequantize", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp. -type ResourceApplyCenteredRMSPropAttr func(optionalAttr) - -// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, mg, ms, and mom tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the centered RMSProp algorithm. -// -// The centered RMSProp algorithm uses an estimate of the centered second moment -// (i.e., the variance) for normalization, as opposed to regular RMSProp, which -// uses the (uncentered) second moment. This often helps with training, but is -// slightly more expensive in terms of computation and memory. -// -// Note that in dense implementation of this algorithm, mg, ms, and mom will -// update even if the grad is zero, but in this sparse implementation, mg, ms, -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// mean_grad = decay * mean_grad + (1-decay) * gradient -// -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) -// -// mg <- rho * mg_{t-1} + (1-rho) * grad -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) -// var <- var - mom -// -// Arguments: -// var_: Should be from a Variable(). -// mg: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceApplyCenteredRMSProp", + Type: "BoostedTreesDeserializeEnsemble", Input: []tf.Input{ - var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, + tree_ensemble_handle, stamp_token, tree_ensemble_serialized, }, - Attrs: attrs, } return scope.AddOperation(opspec) } @@ -21156,454 +28715,32 @@ func UnicodeDecodeWithOffsets(scope *Scope, input tf.Output, input_encoding stri return op.Output(0), op.Output(1), op.Output(2) } -// An op enabling differentiation of TPU Embeddings. -// -// This op simply returns its first input, which is assumed to have been sliced -// from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of -// this op, and its first argument being a trainable Variable, enables automatic -// differentiation of graphs containing embeddings via the TPU Embedding Python -// libraries. -// -// Arguments: -// embedding_variable: A trainable variable, enabling optimizers to find this op. -// sliced_activations: The embedding activations Tensor to return. -// table_id: The id of the table in the embedding layer configuration from which -// these activations were computed. -// lookup_id: Identifier of the set of embedding indices which produced these -// activations. -func TPUEmbeddingActivations(scope *Scope, embedding_variable tf.Output, sliced_activations tf.Output, table_id int64, lookup_id int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"table_id": table_id, "lookup_id": lookup_id} - opspec := tf.OpSpec{ - Type: "TPUEmbeddingActivations", - Input: []tf.Input{ - embedding_variable, sliced_activations, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. +type StatelessRandomUniformAttr func(optionalAttr) -// ResourceApplyAdamWithAmsgradAttr is an optional argument to ResourceApplyAdamWithAmsgrad. -type ResourceApplyAdamWithAmsgradAttr func(optionalAttr) - -// ResourceApplyAdamWithAmsgradUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, m, and v tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAdamWithAmsgradUseLocking(value bool) ResourceApplyAdamWithAmsgradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the Adam algorithm. -// -// $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$ -// $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ -// $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ -// $$vhat_t := max{vhat_{t-1}, v_t}$$ -// $$variable := variable - lr_t * m_t / (\sqrt{vhat_t} + \epsilon)$$ -// -// Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// v: Should be from a Variable(). -// vhat: Should be from a Variable(). -// beta1_power: Must be a scalar. -// beta2_power: Must be a scalar. -// lr: Scaling factor. Must be a scalar. -// beta1: Momentum factor. Must be a scalar. -// beta2: Momentum factor. Must be a scalar. -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdamWithAmsgrad(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, vhat tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamWithAmsgradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdamWithAmsgrad", - Input: []tf.Input{ - var_, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Rounds the values of a tensor to the nearest integer, element-wise. -// -// Rounds half to even. Also known as bankers rounding. If you want to round -// according to the current system rounding mode use std::cint. -func Round(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Round", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolGradGradWithArgmaxAttr is an optional argument to MaxPoolGradGradWithArgmax. -type MaxPoolGradGradWithArgmaxAttr func(optionalAttr) - -// MaxPoolGradGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. -// -// value: Whether to include batch dimension in flattened index of `argmax`. -// If not specified, defaults to false -func MaxPoolGradGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradGradWithArgmaxAttr { - return func(m optionalAttr) { - m["include_batch_in_index"] = value - } -} - -// Computes second-order gradients of the maxpooling function. -// -// Arguments: -// input: The original input. -// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the -// input of `max_pool`. -// argmax: The indices of the maximum values chosen for each output of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients of gradients w.r.t. the input of `max_pool`. -func MaxPoolGradGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradWithArgmaxAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradGradWithArgmax", - Input: []tf.Input{ - input, grad, argmax, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RestoreSliceAttr is an optional argument to RestoreSlice. -type RestoreSliceAttr func(optionalAttr) - -// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. -// -// value: Index of file to open first if multiple files match -// `file_pattern`. See the documentation for `Restore`. -// If not specified, defaults to -1 -func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { - return func(m optionalAttr) { - m["preferred_shard"] = value - } -} - -// Restores a tensor from checkpoint files. -// -// This is like `Restore` except that restored tensor can be listed as filling -// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the -// larger tensor and the slice that the restored tensor covers. -// -// The `shape_and_slice` input has the same format as the -// elements of the `shapes_and_slices` input of the `SaveSlices` op. -// -// Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. -// tensor_name: Must have a single element. The name of the tensor to be -// restored. -// shape_and_slice: Scalar. The shapes and slice specifications to use when -// restoring a tensors. -// dt: The type of the tensor to be restored. -// -// Returns The restored tensor. -func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dt": dt} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RestoreSlice", - Input: []tf.Input{ - file_pattern, tensor_name, shape_and_slice, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RandomStandardNormalAttr is an optional argument to RandomStandardNormal. -type RandomStandardNormalAttr func(optionalAttr) - -// RandomStandardNormalSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomStandardNormalSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Outputs random values from a normal distribution. -// -// The generated values will have mean 0 and standard deviation 1. -// -// Arguments: -// shape: The shape of the output tensor. -// dtype: The type of the output. -// -// Returns A tensor of the specified shape filled with random normal values. -func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomStandardNormal", - Input: []tf.Input{ - shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// OrderedMapStageAttr is an optional argument to OrderedMapStage. -type OrderedMapStageAttr func(optionalAttr) - -// OrderedMapStageCapacity sets the optional capacity attribute to value. -// -// value: Maximum number of elements in the Staging Area. If > 0, inserts -// on the container will block when the capacity is reached. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapStageCapacity(value int64) OrderedMapStageAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// OrderedMapStageMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapStageMemoryLimit(value int64) OrderedMapStageAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// OrderedMapStageContainer sets the optional container attribute to value. -// -// value: If non-empty, this queue is placed in the given container. Otherwise, -// a default container is used. -// If not specified, defaults to "" -func OrderedMapStageContainer(value string) OrderedMapStageAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// OrderedMapStageSharedName sets the optional shared_name attribute to value. -// -// value: It is necessary to match this name to the matching Unstage Op. -// If not specified, defaults to "" -func OrderedMapStageSharedName(value string) OrderedMapStageAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Stage (key, values) in the underlying container which behaves like a ordered -// -// associative container. Elements are ordered by key. -// -// Arguments: -// key: int64 -// -// values: a list of tensors -// dtypes A list of data types that inserted values should adhere to. -// -// -// Returns the created operation. -func OrderedMapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...OrderedMapStageAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OrderedMapStage", - Input: []tf.Input{ - key, indices, tf.OutputList(values), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Updates the table to associates keys with values. -// -// The tensor `keys` must be of the same type as the keys of the table. -// The tensor `values` must be of the type of the table values. -// -// Arguments: -// table_handle: Handle to the table. -// keys: Any shape. Keys to look up. -// values: Values to associate with keys. -// -// Returns the created operation. -func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LookupTableInsertV2", - Input: []tf.Input{ - table_handle, keys, values, - }, - } - return scope.AddOperation(opspec) -} - -// OrderedMapUnstageAttr is an optional argument to OrderedMapUnstage. -type OrderedMapUnstageAttr func(optionalAttr) - -// OrderedMapUnstageCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapUnstageCapacity(value int64) OrderedMapUnstageAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// OrderedMapUnstageMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapUnstageMemoryLimit(value int64) OrderedMapUnstageAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// OrderedMapUnstageContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapUnstageContainer(value string) OrderedMapUnstageAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// OrderedMapUnstageSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapUnstageSharedName(value string) OrderedMapUnstageAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op removes and returns the values associated with the key -// -// from the underlying container. If the underlying container -// does not contain this key, the op will block until it does. -func OrderedMapUnstage(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageAttr) (values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OrderedMapUnstage", - Input: []tf.Input{ - key, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("OrderedMapUnstage", err) - return - } - return values -} - -// NonDeterministicIntsAttr is an optional argument to NonDeterministicInts. -type NonDeterministicIntsAttr func(optionalAttr) - -// NonDeterministicIntsDtype sets the optional dtype attribute to value. +// StatelessRandomUniformDtype sets the optional dtype attribute to value. // // value: The type of the output. -// If not specified, defaults to DT_INT64 -func NonDeterministicIntsDtype(value tf.DataType) NonDeterministicIntsAttr { +// If not specified, defaults to DT_FLOAT +func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { return func(m optionalAttr) { m["dtype"] = value } } -// Non-deterministically generates some integers. +// Outputs deterministic pseudorandom random values from a uniform distribution. // -// This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// The outputs are a deterministic function of `shape` and `seed`. // // Arguments: // shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). // -// Returns Non-deterministic integer values with specified shape. -func NonDeterministicInts(scope *Scope, shape tf.Output, optional ...NonDeterministicIntsAttr) (output tf.Output) { +// Returns Random values with specified shape. +func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -21612,9 +28749,9 @@ func NonDeterministicInts(scope *Scope, shape tf.Output, optional ...NonDetermin a(attrs) } opspec := tf.OpSpec{ - Type: "NonDeterministicInts", + Type: "StatelessRandomUniform", Input: []tf.Input{ - shape, + shape, seed, }, Attrs: attrs, } @@ -21622,61 +28759,53 @@ func NonDeterministicInts(scope *Scope, shape tf.Output, optional ...NonDetermin return op.Output(0) } -// Deprecated. Use TensorArrayScatterV3 +// Deprecated. Disallowed in GraphDef version >= 2. // -// DEPRECATED at GraphDef version 26: Use TensorArrayScatterV3 -func TensorArrayScatterV2(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { +// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead +func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "TensorArrayScatterV2", + Type: "AdjustContrast", Input: []tf.Input{ - handle, indices, value, flow_in, + images, contrast_factor, min_value, max_value, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug. -type LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) +// RetrieveTPUEmbeddingProximalAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParameters. +type RetrieveTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) -// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// RetrieveTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. // If not specified, defaults to -1 // // REQUIRES: value >= -1 -func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { +func RetrieveTPUEmbeddingProximalAdagradParametersTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersAttr { return func(m optionalAttr) { m["table_id"] = value } } -// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// RetrieveTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. // If not specified, defaults to "" -func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { +func RetrieveTPUEmbeddingProximalAdagradParametersTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersAttr { return func(m optionalAttr) { m["table_name"] = value } } -// Load proximal Adagrad embedding parameters with debug support. +// Retrieve proximal Adagrad embedding parameters. // -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. // -// Arguments: -// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. -// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. -// gradient_accumulators: Value of gradient_accumulators used in the proximal Adagrad optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { +// Returns Parameter parameters updated by the proximal Adagrad optimization algorithm.Parameter accumulators updated by the proximal Adagrad optimization algorithm. +func RetrieveTPUEmbeddingProximalAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { if scope.Err() != nil { return } @@ -21685,189 +28814,51 @@ func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, param a(attrs) } opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", - Input: []tf.Input{ - parameters, accumulators, gradient_accumulators, - }, + Type: "RetrieveTPUEmbeddingProximalAdagradParameters", + Attrs: attrs, } - return scope.AddOperation(opspec) -} - -// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. -type ResourceApplyAdadeltaAttr func(optionalAttr) - -// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var, accum and update_accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the adadelta scheme. -// -// accum = rho() * accum + (1 - rho()) * grad.square(); -// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; -// update_accum = rho() * update_accum + (1 - rho()) * update.square(); -// var -= update; -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// accum_update: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay factor. Must be a scalar. -// epsilon: Constant factor. Must be a scalar. -// grad: The gradient. -// -// Returns the created operation. -func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyAdadelta", - Input: []tf.Input{ - var_, accum, accum_update, lr, rho, epsilon, grad, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Creates a dataset that executes a SQL query and emits rows of the result set. -// -// Arguments: -// driver_name: The database type. Currently, the only supported type is 'sqlite'. -// data_source_name: A connection string to connect to the database. -// query: A SQL query to execute. -// -// -func ExperimentalSqlDataset(scope *Scope, driver_name tf.Output, data_source_name tf.Output, query tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalSqlDataset", - Input: []tf.Input{ - driver_name, data_source_name, query, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolV2Attr is an optional argument to MaxPoolV2. -type MaxPoolV2Attr func(optionalAttr) - -// MaxPoolV2DataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolV2DataFormat(value string) MaxPoolV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs max pooling on the input. -// -// Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPoolV2(scope *Scope, input tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolV2", - Input: []tf.Input{ - input, ksize, strides, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Forwards the value of an available tensor from `inputs` to `output`. -// -// `Merge` waits for at least one of the tensors in `inputs` to become available. -// It is usually combined with `Switch` to implement branching. -// -// `Merge` forwards the first tensor to become available to `output`, and sets -// `value_index` to its index in `inputs`. -// -// Arguments: -// inputs: The input tensors, exactly one of which will become available. -// -// Returns Will be set to the available input tensor.The index of the chosen input tensor in `inputs`. -func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Merge", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1) } -// SetSizeAttr is an optional argument to SetSize. -type SetSizeAttr func(optionalAttr) +// SparseReduceSumAttr is an optional argument to SparseReduceSum. +type SparseReduceSumAttr func(optionalAttr) -// SetSizeValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func SetSizeValidateIndices(value bool) SetSizeAttr { +// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { return func(m optionalAttr) { - m["validate_indices"] = value + m["keep_dims"] = value } } -// Number of unique elements along last dimension of input `set`. +// Computes the sum of elements across dimensions of a SparseTensor. // -// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, -// and `set_shape`. The last dimension contains values in a set, duplicates are -// allowed but ignored. +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. // -// If `validate_indices` is `True`, this op validates the order and range of `set` -// indices. +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. // // Arguments: -// set_indices: 2D `Tensor`, indices of a `SparseTensor`. -// set_values: 1D `Tensor`, values of a `SparseTensor`. -// set_shape: 1D `Tensor`, shape of a `SparseTensor`. +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. // -// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st -// `n-1` dimensions as `set`. Each value is the number of unique elements in -// the corresponding `[0...n-1]` dimension of `set`. -func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) { +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -21876,9 +28867,9 @@ func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shap a(attrs) } opspec := tf.OpSpec{ - Type: "SetSize", + Type: "SparseReduceSum", Input: []tf.Input{ - set_indices, set_values, set_shape, + input_indices, input_values, input_shape, reduction_axes, }, Attrs: attrs, } @@ -21886,171 +28877,51 @@ func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shap return op.Output(0) } -// StridedSliceGradAttr is an optional argument to StridedSliceGrad. -type StridedSliceGradAttr func(optionalAttr) +// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. +type Conv3DBackpropFilterV2Attr func(optionalAttr) -// StridedSliceGradBeginMask sets the optional begin_mask attribute to value. -// If not specified, defaults to 0 -func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr { - return func(m optionalAttr) { - m["begin_mask"] = value - } -} - -// StridedSliceGradEndMask sets the optional end_mask attribute to value. -// If not specified, defaults to 0 -func StridedSliceGradEndMask(value int64) StridedSliceGradAttr { - return func(m optionalAttr) { - m["end_mask"] = value - } -} - -// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value. -// If not specified, defaults to 0 -func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr { - return func(m optionalAttr) { - m["ellipsis_mask"] = value - } -} - -// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value. -// If not specified, defaults to 0 -func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr { - return func(m optionalAttr) { - m["new_axis_mask"] = value - } -} - -// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value. -// If not specified, defaults to 0 -func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr { - return func(m optionalAttr) { - m["shrink_axis_mask"] = value - } -} - -// Returns the gradient of `StridedSlice`. +// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. // -// Since `StridedSlice` cuts out pieces of its `input` which is size -// `shape`, its gradient will have the same shape (which is passed here -// as `shape`). The gradient will be zero in any element that the slice -// does not select. -// -// Arguments are the same as StridedSliceGrad with the exception that -// `dy` is the input gradient to be propagated and `shape` is the -// shape of `StridedSlice`'s `input`. -func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StridedSliceGrad", - Input: []tf.Input{ - shape, begin, end, strides, dy, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Output a fact about factorials. -func Fact(scope *Scope) (fact tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Fact", - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the gradient of morphological 2-D dilation with respect to the input. -// -// Arguments: -// input: 4-D with shape `[batch, in_height, in_width, depth]`. -// filter: 3-D with shape `[filter_height, filter_width, depth]`. -// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. -// strides: 1-D of length 4. The stride of the sliding window for each dimension of -// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. -// rates: 1-D of length 4. The input stride for atrous morphological dilation. -// Must be: `[1, rate_height, rate_width, 1]`. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape `[batch, in_height, in_width, depth]`. -func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} - opspec := tf.OpSpec{ - Type: "Dilation2DBackpropInput", - Input: []tf.Input{ - input, filter, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput. -type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr) - -// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, height, width, channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, channels, height, width]. -// If not specified, defaults to "NHWC" -func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr { +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { return func(m optionalAttr) { m["data_format"] = value } } -// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value. +// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. // -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { +// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { return func(m optionalAttr) { m["dilations"] = value } } -// Computes the gradients of depthwise convolution with respect to the input. +// Computes the gradients of 3-D convolution with respect to the filter. // // Arguments: -// input_sizes: An integer vector representing the shape of `input`, based -// on `data_format`. For example, if `data_format` is 'NHWC' then -// `input` is a 4-D `[batch, height, width, channels]` tensor. -// filter: 4-D with shape -// `[filter_height, filter_width, in_channels, depthwise_multiplier]`. -// out_backprop: 4-D with shape based on `data_format`. -// For example, if `data_format` is 'NHWC' then -// out_backprop shape is `[batch, out_height, out_width, out_channels]`. -// Gradients w.r.t. the output of the convolution. -// strides: The stride of the sliding window for each dimension of the input -// of the convolution. +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 5-D +// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` +// tensor. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. // padding: The type of padding algorithm to use. -// -// Returns 4-D with shape according to `data_format`. For example, if -// `data_format` is 'NHWC', output shape is `[batch, in_height, -// in_width, in_channels]`. Gradient w.r.t. the input of the -// convolution. -func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) { +func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { if scope.Err() != nil { return } @@ -22059,9 +28930,9 @@ func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, fil a(attrs) } opspec := tf.OpSpec{ - Type: "DepthwiseConv2dNativeBackpropInput", + Type: "Conv3DBackpropFilterV2", Input: []tf.Input{ - input_sizes, filter, out_backprop, + input, filter_sizes, out_backprop, }, Attrs: attrs, } @@ -22069,142 +28940,74 @@ func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, fil return op.Output(0) } -// TensorArrayConcatV3Attr is an optional argument to TensorArrayConcatV3. -type TensorArrayConcatV3Attr func(optionalAttr) +// ImageSummaryAttr is an optional argument to ImageSummary. +type ImageSummaryAttr func(optionalAttr) -// TensorArrayConcatV3ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. +// ImageSummaryMaxImages sets the optional max_images attribute to value. // -// value: The expected shape of an element, if known, -// excluding the first dimension. Used to validate the shapes of -// TensorArray elements. If this shape is not fully specified, concatenating -// zero-size TensorArrays is an error. -// If not specified, defaults to -func TensorArrayConcatV3ElementShapeExcept0(value tf.Shape) TensorArrayConcatV3Attr { +// value: Max number of batch elements to generate images for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func ImageSummaryMaxImages(value int64) ImageSummaryAttr { return func(m optionalAttr) { - m["element_shape_except0"] = value + m["max_images"] = value } } -// Concat the elements from the TensorArray into value `value`. +// ImageSummaryBadColor sets the optional bad_color attribute to value. // -// Takes `T` elements of shapes +// value: Color to use for pixels with non-finite values. +// If not specified, defaults to > int_val:255 int_val:0 int_val:0 int_val:255 > +func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { + return func(m optionalAttr) { + m["bad_color"] = value + } +} + +// Outputs a `Summary` protocol buffer with images. // -// ``` -// (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...) -// ``` +// The summary has up to `max_images` summary values containing images. The +// images are built from `tensor` which must be 4-D with shape `[batch_size, +// height, width, channels]` and where `channels` can be: // -// and concatenates them into a Tensor of shape: +// * 1: `tensor` is interpreted as Grayscale. +// * 3: `tensor` is interpreted as RGB. +// * 4: `tensor` is interpreted as RGBA. // -// ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)``` +// The images have the same number of channels as the input tensor. For float +// input, the values are normalized one image at a time to fit in the range +// `[0, 255]`. `uint8` values are unchanged. The op uses two different +// normalization algorithms: // -// All elements must have the same shape (excepting the first dimension). +// * If the input values are all positive, they are rescaled so the largest one +// is 255. +// +// * If any input value is negative, the values are shifted so input value 0.0 +// is at 127. They are then rescaled so that either the smallest value is 0, +// or the largest one is 255. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_images` is 1, the summary value tag is '*tag*/image'. +// * If `max_images` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. +// +// The `bad_color` argument is the color to use in the generated images for +// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. +// Each element must be in the range `[0, 255]` (It represents the value of a +// pixel in the output image). Non-finite values in the input tensor are +// replaced by this tensor in the output image. The default value is the color +// red. // // Arguments: -// handle: The handle to a TensorArray. -// flow_in: A float scalar that enforces proper chaining of operations. -// dtype: The type of the elem that is returned. +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 4-D of shape `[batch_size, height, width, channels]` where +// `channels` is 1, 3, or 4. // -// Returns All of the elements in the TensorArray, concatenated along the first -// axis.A vector of the row sizes of the original T elements in the -// value output. In the example above, this would be the values: -// `(n1, n2, ..., n(T-1))`. -func TensorArrayConcatV3(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV3Attr) (value tf.Output, lengths tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorArrayConcatV3", - Input: []tf.Input{ - handle, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Computes the gradient for the rsqrt of `x` wrt its input. -// -// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy` -// is the corresponding input gradient. -func RsqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RsqrtGrad", - Input: []tf.Input{ - y, dy, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceScatterNdAddAttr is an optional argument to ResourceScatterNdAdd. -type ResourceScatterNdAddAttr func(optionalAttr) - -// ResourceScatterNdAddUseLocking sets the optional use_locking attribute to value. -// -// value: An optional bool. Defaults to True. If True, the assignment will -// be protected by a lock; otherwise the behavior is undefined, -// but may exhibit less contention. -// If not specified, defaults to true -func ResourceScatterNdAddUseLocking(value bool) ResourceScatterNdAddAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Applies sparse addition to individual values or slices in a Variable. -// -// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. -// -// `indices` must be integer tensor, containing indices into `ref`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. -// -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th -// dimension of `ref`. -// -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: -// -// ``` -// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] -// ``` -// -// For example, say we want to add 4 scattered elements to a rank-1 tensor to -// 8 elements. In Python, that addition would look like this: -// -// ```python -// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// add = tf.scatter_nd_add(ref, indices, updates) -// with tf.Session() as sess: -// print sess.run(add) -// ``` -// -// The resulting update to ref would look like this: -// -// [1, 13, 3, 14, 14, 6, 7, 20] -// -// See `tf.scatter_nd` for more details about how to make updates to -// slices. -// -// Arguments: -// ref: A resource handle. Must be from a VarHandleOp. -// indices: A Tensor. Must be one of the following types: int32, int64. -// A tensor of indices into ref. -// updates: A Tensor. Must have the same type as ref. A tensor of -// values to add to ref. -// -// Returns the created operation. -func ResourceScatterNdAdd(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdAddAttr) (o *tf.Operation) { +// Returns Scalar. Serialized `Summary` protocol buffer. +func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) { if scope.Err() != nil { return } @@ -22213,52 +29016,838 @@ func ResourceScatterNdAdd(scope *Scope, ref tf.Output, indices tf.Output, update a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceScatterNdAdd", + Type: "ImageSummary", Input: []tf.Input{ - ref, indices, updates, + tag, tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SkipgramAttr is an optional argument to Skipgram. +type SkipgramAttr func(optionalAttr) + +// SkipgramWindowSize sets the optional window_size attribute to value. +// +// value: The number of words to predict to the left and right of the target. +// If not specified, defaults to 5 +func SkipgramWindowSize(value int64) SkipgramAttr { + return func(m optionalAttr) { + m["window_size"] = value + } +} + +// SkipgramMinCount sets the optional min_count attribute to value. +// +// value: The minimum number of word occurrences for it to be included in the +// vocabulary. +// If not specified, defaults to 5 +func SkipgramMinCount(value int64) SkipgramAttr { + return func(m optionalAttr) { + m["min_count"] = value + } +} + +// SkipgramSubsample sets the optional subsample attribute to value. +// +// value: Threshold for word occurrence. Words that appear with higher +// frequency will be randomly down-sampled. Set to 0 to disable. +// If not specified, defaults to 0.001 +func SkipgramSubsample(value float32) SkipgramAttr { + return func(m optionalAttr) { + m["subsample"] = value + } +} + +// Parses a text file and creates a batch of examples. +// +// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result +// +// Arguments: +// filename: The corpus's text file name. +// batch_size: The size of produced batch. +// +// Returns A vector of words in the corpus.Frequencies of words. Sorted in the non-ascending order.Number of words per epoch in the data file.The current epoch number.The total number of words processed so far.A vector of word ids.A vector of word ids. +func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Skipgram", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// VariableShapeAttr is an optional argument to VariableShape. +type VariableShapeAttr func(optionalAttr) + +// VariableShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func VariableShapeOutType(value tf.DataType) VariableShapeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns the shape of the variable pointed to by `resource`. +// +// This operation returns a 1-D integer tensor representing the shape of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VariableShape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits `components` as a tuple of tensors once. +func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TensorDataset", + Input: []tf.Input{ + tf.OutputList(components), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs deterministic pseudorandom random integers from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[minval, maxval)`. +// +// The outputs are a deterministic function of `shape`, `seed`, `minval`, and `maxval`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// minval: Minimum value (inclusive, scalar). +// maxval: Maximum value (exclusive, scalar). +// +// Returns Random values with specified shape. +func StatelessRandomUniformInt(scope *Scope, shape tf.Output, seed tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatelessRandomUniformInt", + Input: []tf.Input{ + shape, seed, minval, maxval, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Output the logits for the given input data +// +// Arguments: +// tree_handle: Handle to the tree resource. +// dense_features: Rank 2 dense features tensor. +// logits_dimension: Scalar, dimension of the logits. +// +// Returns The logits predictions from the tree for each instance in the batch. +func TensorForestTreePredict(scope *Scope, tree_handle tf.Output, dense_features tf.Output, logits_dimension int64) (logits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "TensorForestTreePredict", + Input: []tf.Input{ + tree_handle, dense_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyKerasMomentumAttr is an optional argument to ResourceApplyKerasMomentum. +type ResourceApplyKerasMomentumAttr func(optionalAttr) + +// ResourceApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyKerasMomentumUseLocking(value bool) ResourceApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var + momentum * accum, so in the end, the var you get is actually +// var + momentum * accum. +// If not specified, defaults to false +func ResourceApplyKerasMomentumUseNesterov(value bool) ResourceApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the momentum scheme. Set use_nesterov = True if you +// +// want to use Nesterov momentum. +// +// accum = accum * momentum - lr * grad +// var += accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyKerasMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyKerasMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, momentum, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// Writes the given dataset to the given file using the TFRecord format. +// Returns x / y element-wise for real types. // -// Arguments: -// input_dataset: A variant tensor representing the dataset to write. -// filename: A scalar string tensor representing the filename to use. -// compression_type: A scalar string tensor containing either (i) the empty string (no -// compression), (ii) "ZLIB", or (iii) "GZIP". +// If `x` and `y` are reals, this will return the floating-point division. // -// Returns the created operation. -func ExperimentalDatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) { +// *NOTE*: `Div` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ExperimentalDatasetToTFRecord", + Type: "RealDiv", Input: []tf.Input{ - input_dataset, filename, compression_type, + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs a `Summary` protocol buffer with a tensor and per-plugin data. +// +// Arguments: +// tag: A string attached to this summary. Used for organization in TensorBoard. +// tensor: A tensor to serialize. +// serialized_summary_metadata: A serialized SummaryMetadata proto. Contains plugin +// data. +func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_summary_metadata tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorSummaryV2", + Input: []tf.Input{ + tag, tensor, serialized_summary_metadata, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reshapes a quantized tensor as per the Reshape op. +// +// ``` +// +// Arguments: +// +// shape: Defines the shape of the output tensor. +// input_min: The minimum value of the input. +// input_max: The maximum value of the input. +// +// Returns This value is copied from input_min.This value is copied from input_max. +func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedReshape", + Input: []tf.Input{ + tensor, shape, input_min, input_max, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatelessMultinomialAttr is an optional argument to StatelessMultinomial. +type StatelessMultinomialAttr func(optionalAttr) + +// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// seed: 2 seeds (shape [2]). +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessMultinomial", + Input: []tf.Input{ + logits, num_samples, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation. +type DenseToSparseSetOperationAttr func(optionalAttr) + +// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of `Tensor` and `SparseTensor`. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set2` +// indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the +// max set size across `n-1` dimensions. +// +// +// Returns 2D indices of a `SparseTensor`.1D values of a `SparseTensor`.1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseToSparseSetOperation", + Input: []tf.Input{ + set1, set2_indices, set2_values, set2_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Reshapes a SparseTensor to represent values in a new dense shape. +// +// This operation has the same semantics as reshape on the represented dense +// tensor. The `input_indices` are recomputed based on the requested `new_shape`. +// +// If one component of `new_shape` is the special value -1, the size of that +// dimension is computed so that the total dense size remains constant. At +// most one component of `new_shape` can be -1. The number of dense elements +// implied by `new_shape` must be the same as the number of dense elements +// originally implied by `input_shape`. +// +// Reshaping does not affect the order of values in the SparseTensor. +// +// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape` +// has length `R_out`, then `input_indices` has shape `[N, R_in]`, +// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and +// `output_shape` has length `R_out`. +// +// Arguments: +// input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a +// SparseTensor. +// input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape. +// new_shape: 1-D. `R_out` vector with the requested new dense shape. +// +// Returns 2-D. `N x R_out` matrix with the updated indices of non-empty +// values in the output SparseTensor.1-D. `R_out` vector with the full dense shape of the output +// SparseTensor. This is the same as `new_shape` but with any -1 dimensions +// filled in. +func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseReshape", + Input: []tf.Input{ + input_indices, input_shape, new_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// EncodeBase64Attr is an optional argument to EncodeBase64. +type EncodeBase64Attr func(optionalAttr) + +// EncodeBase64Pad sets the optional pad attribute to value. +// +// value: Bool whether padding is applied at the ends. +// If not specified, defaults to false +func EncodeBase64Pad(value bool) EncodeBase64Attr { + return func(m optionalAttr) { + m["pad"] = value + } +} + +// Encode strings into web-safe base64 format. +// +// Refer to the following article for more information on base64 format: +// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the +// end so that the encoded has length multiple of 4. See Padding section of the +// link above. +// +// Web-safe means that the encoder uses - and _ instead of + and /. +// +// Arguments: +// input: Strings to be encoded. +// +// Returns Input strings encoded in base64. +func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodeBase64", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingFTRLParametersAttr is an optional argument to RetrieveTPUEmbeddingFTRLParameters. +type RetrieveTPUEmbeddingFTRLParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingFTRLParametersTableId(value int64) RetrieveTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersTableName(value string) RetrieveTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve FTRL embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the FTRL optimization algorithm.Parameter accumulators updated by the FTRL optimization algorithm.Parameter linears updated by the FTRL optimization algorithm. +func RetrieveTPUEmbeddingFTRLParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingFTRLParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// RaggedRangeAttr is an optional argument to RaggedRange. +type RaggedRangeAttr func(optionalAttr) + +// RaggedRangeTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func RaggedRangeTsplits(value tf.DataType) RaggedRangeAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Returns a `RaggedTensor` containing the specified sequences of numbers. +// +// +// Returns a `RaggedTensor` `result` composed from `rt_dense_values` and +// `rt_nested_splits`, such that +// `result[i] = range(starts[i], limits[i], deltas[i])`. +// +// ```python +// >>> (rt_nested_splits, rt_dense_values) = gen_ragged_ops.ragged_range( +// ... starts=[2, 5, 8], limits=[3, 5, 12], deltas=1) +// >>> result = ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) +// >>> print result.eval().tolist() +// [[2], # result[0] = range(2, 3) +// [], # result[1] = range(5, 5) +// [8, 9, 10, 11]] # result[2] = range(8, 12) +// ``` +// +// The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors. +// The vector inputs must all have the same size. Scalar inputs are broadcast +// to match the size of the vector inputs. +// +// Arguments: +// starts: The starts of each range. +// limits: The limits of each range. +// deltas: The deltas of each range. +// +// Returns The `row_splits` for the returned `RaggedTensor`.The `flat_values` for the returned `RaggedTensor`. +func RaggedRange(scope *Scope, starts tf.Output, limits tf.Output, deltas tf.Output, optional ...RaggedRangeAttr) (rt_nested_splits tf.Output, rt_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RaggedRange", + Input: []tf.Input{ + starts, limits, deltas, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StringToNumberAttr is an optional argument to StringToNumber. +type StringToNumberAttr func(optionalAttr) + +// StringToNumberOutType sets the optional out_type attribute to value. +// +// value: The numeric type to interpret each string in `string_tensor` as. +// If not specified, defaults to DT_FLOAT +func StringToNumberOutType(value tf.DataType) StringToNumberAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Converts each string in the input Tensor to the specified numeric type. +// +// (Note that int32 overflow results in an error while float overflow +// results in a rounded value.) +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringToNumber", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU. +type ConfigureDistributedTPUAttr func(optionalAttr) + +// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value. +// +// value: Reserved. Do not use. +// If not specified, defaults to "" +func ConfigureDistributedTPUEmbeddingConfig(value string) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["embedding_config"] = value + } +} + +// ConfigureDistributedTPUTpuEmbeddingConfig sets the optional tpu_embedding_config attribute to value. +// +// value: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that +// describes the embedding lookups of the program. +// If not specified, defaults to "" +func ConfigureDistributedTPUTpuEmbeddingConfig(value string) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["tpu_embedding_config"] = value + } +} + +// ConfigureDistributedTPUIsGlobalInit sets the optional is_global_init attribute to value. +// +// value: Reserved. Do not use. +// If not specified, defaults to false +func ConfigureDistributedTPUIsGlobalInit(value bool) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["is_global_init"] = value + } +} + +// Sets up the centralized structures for a distributed TPU system. +// +// Returns A serialized tensorflow.tpu.TopologyProto that describes the TPU +// topology. +func ConfigureDistributedTPU(scope *Scope, optional ...ConfigureDistributedTPUAttr) (topology tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ConfigureDistributedTPU", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the lines of one or more text files. +// +// Arguments: +// filenames: A scalar or a vector containing the name(s) of the file(s) to be +// read. +// compression_type: A scalar containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// buffer_size: A scalar containing the number of bytes to buffer. +func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TextLineDataset", + Input: []tf.Input{ + filenames, compression_type, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Enqueue a Tensor on the computation outfeed. +// +// Arguments: +// input: A tensor that will be inserted into the outfeed queue. +// +// Returns the created operation. +func OutfeedEnqueue(scope *Scope, input tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OutfeedEnqueue", + Input: []tf.Input{ + input, }, } return scope.AddOperation(opspec) } -// Subtracts sparse updates from the variable referenced by `resource`. +// RetrieveTPUEmbeddingRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParameters. +type RetrieveTPUEmbeddingRMSPropParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersTableName(value string) RetrieveTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve RMSProp embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the RMSProp optimization algorithm.Parameter ms updated by the RMSProp optimization algorithm.Parameter mom updated by the RMSProp optimization algorithm. +func RetrieveTPUEmbeddingRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingRMSPropParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Gradients for batch normalization. +// +// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() +// +// This op is deprecated. See `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this Tensor will be multiplied +// with the normalized Tensor. +// backprop: 4D backprop Tensor. +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +// +// Returns 4D backprop tensor for input.1D backprop tensor for mean.1D backprop tensor for variance.1D backprop tensor for beta.1D backprop tensor for gamma. +func BatchNormWithGlobalNormalizationGrad(scope *Scope, t tf.Output, m tf.Output, v tf.Output, gamma tf.Output, backprop tf.Output, variance_epsilon float32, scale_after_normalization bool) (dx tf.Output, dm tf.Output, dv tf.Output, db tf.Output, dg tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "BatchNormWithGlobalNormalizationGrad", + Input: []tf.Input{ + t, m, v, gamma, backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Reduces sparse updates into the variable referenced by `resource` using the `max` operation. // // This operation computes // // # Scalar indices -// ref[indices, ...] -= updates[...] +// ref[indices, ...] = max(ref[indices, ...], updates[...]) // // # Vector indices (for each i) -// ref[indices[i], ...] -= updates[i, ...] +// ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...]) // // # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...] +// ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) // // Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions add. +// the same location, their contributions are combined. // // Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. // @@ -22272,12 +29861,12 @@ func ExperimentalDatasetToTFRecord(scope *Scope, input_dataset tf.Output, filena // updates: A tensor of updated values to add to `ref`. // // Returns the created operation. -func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +func ResourceScatterMax(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ResourceScatterSub", + Type: "ResourceScatterMax", Input: []tf.Input{ resource, indices, updates, }, @@ -22285,395 +29874,6 @@ func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } -// ExtractGlimpseAttr is an optional argument to ExtractGlimpse. -type ExtractGlimpseAttr func(optionalAttr) - -// ExtractGlimpseCentered sets the optional centered attribute to value. -// -// value: indicates if the offset coordinates are centered relative to -// the image, in which case the (0, 0) offset is relative to the center -// of the input images. If false, the (0,0) offset corresponds to the -// upper left corner of the input images. -// If not specified, defaults to true -func ExtractGlimpseCentered(value bool) ExtractGlimpseAttr { - return func(m optionalAttr) { - m["centered"] = value - } -} - -// ExtractGlimpseNormalized sets the optional normalized attribute to value. -// -// value: indicates if the offset coordinates are normalized. -// If not specified, defaults to true -func ExtractGlimpseNormalized(value bool) ExtractGlimpseAttr { - return func(m optionalAttr) { - m["normalized"] = value - } -} - -// ExtractGlimpseUniformNoise sets the optional uniform_noise attribute to value. -// -// value: indicates if the noise should be generated using a -// uniform distribution or a Gaussian distribution. -// If not specified, defaults to true -func ExtractGlimpseUniformNoise(value bool) ExtractGlimpseAttr { - return func(m optionalAttr) { - m["uniform_noise"] = value - } -} - -// ExtractGlimpseNoise sets the optional noise attribute to value. -// -// value: indicates if the noise should `uniform`, `gaussian`, or -// `zero`. The default is `uniform` which means the the noise type -// will be decided by `uniform_noise`. -// If not specified, defaults to "uniform" -func ExtractGlimpseNoise(value string) ExtractGlimpseAttr { - return func(m optionalAttr) { - m["noise"] = value - } -} - -// Extracts a glimpse from the input tensor. -// -// Returns a set of windows called glimpses extracted at location -// `offsets` from the input tensor. If the windows only partially -// overlaps the inputs, the non overlapping areas will be filled with -// random noise. -// -// The result is a 4-D tensor of shape `[batch_size, glimpse_height, -// glimpse_width, channels]`. The channels and batch dimensions are the -// same as that of the input tensor. The height and width of the output -// windows are specified in the `size` parameter. -// -// The argument `normalized` and `centered` controls how the windows are built: -// -// * If the coordinates are normalized but not centered, 0.0 and 1.0 -// correspond to the minimum and maximum of each height and width -// dimension. -// * If the coordinates are both normalized and centered, they range from -// -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper -// left corner, the lower right corner is located at (1.0, 1.0) and the -// center is at (0, 0). -// * If the coordinates are not normalized they are interpreted as -// numbers of pixels. -// -// Arguments: -// input: A 4-D float tensor of shape `[batch_size, height, width, channels]`. -// size: A 1-D tensor of 2 elements containing the size of the glimpses -// to extract. The glimpse height must be specified first, following -// by the glimpse width. -// offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing -// the y, x locations of the center of each window. -// -// Returns A tensor representing the glimpses `[batch_size, -// glimpse_height, glimpse_width, channels]`. -func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Output, optional ...ExtractGlimpseAttr) (glimpse tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ExtractGlimpse", - Input: []tf.Input{ - input, size, offsets, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedDepthwiseConv2DAttr is an optional argument to QuantizedDepthwiseConv2D. -type QuantizedDepthwiseConv2DAttr func(optionalAttr) - -// QuantizedDepthwiseConv2DOutType sets the optional out_type attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_QINT32 -func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2DAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value. -// -// value: List of dilation values. -// If not specified, defaults to -func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes quantized depthwise Conv2D. -// -// Arguments: -// input: The original input tensor. -// filter: The original filter tensor. -// min_input: The float value that the minimum quantized input value represents. -// max_input: The float value that the maximum quantized input value represents. -// min_filter: The float value that the minimum quantized filter value represents. -// max_filter: The float value that the maximum quantized filter value represents. -// strides: List of stride values. -// -// -// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedDepthwiseConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedDepthwiseConv2D", - Input: []tf.Input{ - input, filter, min_input, max_input, min_filter, max_filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Adds Tensor 'bias' to Tensor 'input' for Quantized types. -// -// Broadcasts the values of bias on dimensions 0..N-2 of 'input'. -// -// Arguments: -// -// bias: A 1D bias Tensor with size matching the last dimension of 'input'. -// min_input: The float value that the lowest quantized input value represents. -// max_input: The float value that the highest quantized input value represents. -// min_bias: The float value that the lowest quantized bias value represents. -// max_bias: The float value that the highest quantized bias value represents. -// -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"out_type": out_type} - opspec := tf.OpSpec{ - Type: "QuantizedBiasAdd", - Input: []tf.Input{ - input, bias, min_input, max_input, min_bias, max_bias, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// RetrieveTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingCenteredRMSPropParameters. -type RetrieveTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingCenteredRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingCenteredRMSPropParametersTableName(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve centered RMSProp embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the centered RMSProp optimization algorithm.Parameter ms updated by the centered RMSProp optimization algorithm.Parameter mom updated by the centered RMSProp optimization algorithm.Parameter mg updated by the centered RMSProp optimization algorithm. -func RetrieveTPUEmbeddingCenteredRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingCenteredRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingCenteredRMSPropParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// AsStringAttr is an optional argument to AsString. -type AsStringAttr func(optionalAttr) - -// AsStringPrecision sets the optional precision attribute to value. -// -// value: The post-decimal precision to use for floating point numbers. -// Only used if precision > -1. -// If not specified, defaults to -1 -func AsStringPrecision(value int64) AsStringAttr { - return func(m optionalAttr) { - m["precision"] = value - } -} - -// AsStringScientific sets the optional scientific attribute to value. -// -// value: Use scientific notation for floating point numbers. -// If not specified, defaults to false -func AsStringScientific(value bool) AsStringAttr { - return func(m optionalAttr) { - m["scientific"] = value - } -} - -// AsStringShortest sets the optional shortest attribute to value. -// -// value: Use shortest representation (either scientific or standard) for -// floating point numbers. -// If not specified, defaults to false -func AsStringShortest(value bool) AsStringAttr { - return func(m optionalAttr) { - m["shortest"] = value - } -} - -// AsStringWidth sets the optional width attribute to value. -// -// value: Pad pre-decimal numbers to this width. -// Applies to both floating point and integer numbers. -// Only used if width > -1. -// If not specified, defaults to -1 -func AsStringWidth(value int64) AsStringAttr { - return func(m optionalAttr) { - m["width"] = value - } -} - -// AsStringFill sets the optional fill attribute to value. -// -// value: The value to pad if width > -1. If empty, pads with spaces. -// Another typical value is '0'. String cannot be longer than 1 character. -// If not specified, defaults to "" -func AsStringFill(value string) AsStringAttr { - return func(m optionalAttr) { - m["fill"] = value - } -} - -// Converts each entry in the given tensor to strings. Supports many numeric -// -// types and boolean. -func AsString(scope *Scope, input tf.Output, optional ...AsStringAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AsString", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RandomShuffleAttr is an optional argument to RandomShuffle. -type RandomShuffleAttr func(optionalAttr) - -// RandomShuffleSeed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomShuffleSeed(value int64) RandomShuffleAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomShuffleSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomShuffleSeed2(value int64) RandomShuffleAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Randomly shuffles a tensor along its first dimension. -// -// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped -// to one and only one `output[i]`. For example, a mapping that might occur for a -// 3x2 tensor is: -// -// ``` -// [[1, 2], [[5, 6], -// [3, 4], ==> [1, 2], -// [5, 6]] [3, 4]] -// ``` -// -// Arguments: -// value: The tensor to be shuffled. -// -// Returns A tensor of same shape and type as `value`, shuffled along its first -// dimension. -func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomShuffle", - Input: []tf.Input{ - value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reads and outputs the entire contents of the input filename. -func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReadFile", - Input: []tf.Input{ - filename, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // ReduceJoinAttr is an optional argument to ReduceJoin. type ReduceJoinAttr func(optionalAttr) @@ -22750,213 +29950,71 @@ func ReduceJoin(scope *Scope, inputs tf.Output, reduction_indices tf.Output, opt return op.Output(0) } -// Creates a Tensor by indexing into the TensorList. -// -// Each row in the produced Tensor corresponds to the element in the TensorList -// specified by the given index (see `tf.gather`). -// -// input_handle: The input tensor list. -// indices: The indices used to index into the list. -// values: The tensor. -func TensorListGather(scope *Scope, input_handle tf.Output, indices tf.Output, element_shape tf.Output, element_dtype tf.DataType) (values tf.Output) { +// CompilationResultProto indicating the status of the TPU compilation. +func TPUCompilationResult(scope *Scope) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "TensorListGather", - Input: []tf.Input{ - input_handle, indices, element_shape, - }, - Attrs: attrs, + Type: "TPUCompilationResult", } op := scope.AddOperation(opspec) return op.Output(0) } -// RandomPoissonAttr is an optional argument to RandomPoisson. -type RandomPoissonAttr func(optionalAttr) - -// RandomPoissonSeed sets the optional seed attribute to value. -// If not specified, defaults to 0 -func RandomPoissonSeed(value int64) RandomPoissonAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomPoissonSeed2 sets the optional seed2 attribute to value. -// If not specified, defaults to 0 -func RandomPoissonSeed2(value int64) RandomPoissonAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Use RandomPoissonV2 instead. +// Inverse 2D real-valued fast Fourier transform. // -// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2 -func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomPoisson", - Input: []tf.Input{ - shape, rate, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent. -type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr) - -// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. +// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 2 dimensions of `input`. // -// value: If True, the subtraction will be protected by a lock; -// otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Sparse update '*var' as FOBOS algorithm with fixed learning rate. +// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 2 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. // -// That is for rows we have grad for, we update var as follows: -// prox_v = var - alpha * grad -// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} +// Along each axis `IRFFT2D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. // // Arguments: -// var_: Should be from a Variable(). -// alpha: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. +// input: A complex64 tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. // -// Returns the created operation. -func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyProximalGradientDescent", - Input: []tf.Input{ - var_, alpha, l1, l2, grad, indices, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. -type MaxPool3DGradAttr func(optionalAttr) - -// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// Returns A float32 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 2D Fourier transform. // -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of max pooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool3DGrad", - Input: []tf.Input{ - orig_input, orig_output, grad, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Applies softmax to a batched N-D `SparseTensor`. -// -// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` -// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. -// -// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost -// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly -// zero elements do not participate*. Specifically, the algorithm is equivalent -// to the following: -// -// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix -// with shape `[B, C]`, along the size-C dimension; -// (2) Masks out the original implicitly-zero locations; -// (3) Renormalizes the remaining elements. -// -// Hence, the `SparseTensor` result has exactly the same non-zero indices and -// shape. -// -// Arguments: -// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a -// SparseTensor, in canonical ordering. -// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// -// Returns 1-D. The `NNZ` values for the result `SparseTensor`. -func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { +// @compatibility(numpy) +// Equivalent to np.fft.irfft2 +// @end_compatibility +func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSoftmax", + Type: "IRFFT2D", Input: []tf.Input{ - sp_indices, sp_values, sp_shape, + input, fft_length, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Computes square of x element-wise. +// Returns an element-wise indication of the sign of a number. // -// I.e., \\(y = x * x = x^2\\). -func Square(scope *Scope, x tf.Output) (y tf.Output) { +// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`. +// +// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`. +func Sign(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Square", + Type: "Sign", Input: []tf.Input{ x, }, @@ -22965,93 +30023,28 @@ func Square(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Computes the gradient of `igamma(a, x)` wrt `a`. -func IgammaGradA(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IgammaGradA", - Input: []tf.Input{ - a, x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Connects N inputs to an N-way replicated TPU computation. -func TPUReplicatedInput(scope *Scope, inputs []tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TPUReplicatedInput", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Table initializer that takes two tensors for keys and values respectively. +// Creates a dataset that caches elements from `input_dataset`. +// +// A CacheDataset will iterate over the input_dataset, and store tensors. If the +// cache already exists, the cache will be used. If the cache is inappropriate +// (e.g. cannot be opened, contains tensors of the wrong shape / size), an error +// will the returned when used. // // Arguments: -// table_handle: Handle to a table which will be initialized. -// keys: Keys of type Tkey. -// values: Values of type Tval. // -// Returns the created operation. -func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { +// filename: A path on the filesystem where we should cache the dataset. Note: this +// will be a directory. +// +// +func CacheDataset(scope *Scope, input_dataset tf.Output, filename tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "InitializeTableV2", + Type: "CacheDataset", Input: []tf.Input{ - table_handle, keys, values, - }, - } - return scope.AddOperation(opspec) -} - -// Conv3DBackpropInputAttr is an optional argument to Conv3DBackpropInput. -type Conv3DBackpropInputAttr func(optionalAttr) - -// Conv3DBackpropInputDilations sets the optional dilations attribute to value. -// If not specified, defaults to -func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of 3-D convolution with respect to the input. -// -// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, in_channels]`. -// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. -// `in_channels` must match between `input` and `filter`. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Conv3DBackpropInput", - Input: []tf.Input{ - input, filter, out_backprop, + input_dataset, filename, }, Attrs: attrs, } @@ -23059,448 +30052,21 @@ func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_ba return op.Output(0) } -// ResourceApplyKerasMomentumAttr is an optional argument to ResourceApplyKerasMomentum. -type ResourceApplyKerasMomentumAttr func(optionalAttr) - -// ResourceApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyKerasMomentumUseLocking(value bool) ResourceApplyKerasMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. -// -// value: If `True`, the tensor passed to compute grad will be -// var + momentum * accum, so in the end, the var you get is actually -// var + momentum * accum. -// If not specified, defaults to false -func ResourceApplyKerasMomentumUseNesterov(value bool) ResourceApplyKerasMomentumAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update '*var' according to the momentum scheme. Set use_nesterov = True if you -// -// want to use Nesterov momentum. -// -// accum = accum * momentum - lr * grad -// var += accum +// Checks whether a tree ensemble has been initialized. // // Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. -// momentum: Momentum. Must be a scalar. +// tree_ensemble_handle: Handle to the tree ensemble resouce. // -// Returns the created operation. -func ResourceApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyKerasMomentumAttr) (o *tf.Operation) { +// Returns output boolean on whether it is initialized or not. +func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceApplyKerasMomentum", + Type: "IsBoostedTreesEnsembleInitialized", Input: []tf.Input{ - var_, accum, lr, grad, momentum, + tree_ensemble_handle, }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample. -type ParseSingleSequenceExampleAttr func(optionalAttr) - -// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value. -// -// value: A list of Ncontext_sparse types; the data types of data in -// each context Feature given in context_sparse_keys. -// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), -// DT_INT64 (Int64List), and DT_STRING (BytesList). -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { - return func(m optionalAttr) { - m["context_sparse_types"] = value - } -} - -// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { - return func(m optionalAttr) { - m["feature_list_dense_types"] = value - } -} - -// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value. -// -// value: A list of Ncontext_dense shapes; the shapes of data in -// each context Feature given in context_dense_keys. -// The number of elements in the Feature corresponding to context_dense_key[j] -// must always equal context_dense_shapes[j].NumEntries(). -// The shape of context_dense_values[j] will match context_dense_shapes[j]. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { - return func(m optionalAttr) { - m["context_dense_shapes"] = value - } -} - -// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value. -// -// value: A list of Nfeature_list_sparse types; the data types -// of data in each FeatureList given in feature_list_sparse_keys. -// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), -// DT_INT64 (Int64List), and DT_STRING (BytesList). -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { - return func(m optionalAttr) { - m["feature_list_sparse_types"] = value - } -} - -// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value. -// -// value: A list of Nfeature_list_dense shapes; the shapes of -// data in each FeatureList given in feature_list_dense_keys. -// The shape of each Feature in the FeatureList corresponding to -// feature_list_dense_key[j] must always equal -// feature_list_dense_shapes[j].NumEntries(). -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { - return func(m optionalAttr) { - m["feature_list_dense_shapes"] = value - } -} - -// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. -// -// Arguments: -// serialized: A scalar containing a binary serialized SequenceExample proto. -// feature_list_dense_missing_assumed_empty: A vector listing the -// FeatureList keys which may be missing from the SequenceExample. If the -// associated FeatureList is missing, it is treated as empty. By default, -// any FeatureList not listed in this vector must exist in the SequenceExample. -// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars). -// The keys expected in the Examples' features associated with context_sparse -// values. -// context_dense_keys: A list of Ncontext_dense string Tensors (scalars). -// The keys expected in the SequenceExamples' context features associated with -// dense values. -// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors -// (scalars). The keys expected in the FeatureLists associated with sparse -// values. -// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars). -// The keys expected in the SequenceExamples' feature_lists associated -// with lists of dense values. -// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). -// context_dense_defaults[j] provides default values -// when the SequenceExample's context map lacks context_dense_key[j]. -// If an empty Tensor is provided for context_dense_defaults[j], -// then the Feature context_dense_keys[j] is required. -// The input type is inferred from context_dense_defaults[j], even when it's -// empty. If context_dense_defaults[j] is not empty, its shape must match -// context_dense_shapes[j]. -// debug_name: A scalar containing the name of the serialized proto. -// May contain, for example, table key (descriptive) name for the -// corresponding serialized proto. This is purely useful for debugging -// purposes, and the presence of values here has no effect on the output. -// May also be an empty scalar if no name is available. -func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ParseSingleSequenceExample", - Input: []tf.Input{ - serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil { - scope.UpdateErr("ParseSingleSequenceExample", err) - return - } - return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values -} - -// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. -type ResourceSparseApplyAdagradDAAttr func(optionalAttr) - -// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. -// -// Arguments: -// var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Learning rate. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagradDA", - Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Replaces the contents of the table with the specified keys and values. -// -// The tensor `keys` must be of the same type as the keys of the table. -// The tensor `values` must be of the type of the table values. -// -// Arguments: -// table_handle: Handle to the table. -// keys: Any shape. Keys to look up. -// values: Values to associate with keys. -// -// Returns the created operation. -func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LookupTableImportV2", - Input: []tf.Input{ - table_handle, keys, values, - }, - } - return scope.AddOperation(opspec) -} - -// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug. -type RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve Adadelta embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the Adadelta optimization algorithm.Parameter accumulators updated by the Adadelta optimization algorithm.Parameter updates updated by the Adadelta optimization algorithm.Parameter gradient_accumulators updated by the Adadelta optimization algorithm. -func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// AudioSpectrogramAttr is an optional argument to AudioSpectrogram. -type AudioSpectrogramAttr func(optionalAttr) - -// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value. -// -// value: Whether to return the squared magnitude or just the -// magnitude. Using squared magnitude can avoid extra calculations. -// If not specified, defaults to false -func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr { - return func(m optionalAttr) { - m["magnitude_squared"] = value - } -} - -// Produces a visualization of audio data over time. -// -// Spectrograms are a standard way of representing audio information as a series of -// slices of frequency information, one slice for each window of time. By joining -// these together into a sequence, they form a distinctive fingerprint of the sound -// over time. -// -// This op expects to receive audio data as an input, stored as floats in the range -// -1 to 1, together with a window width in samples, and a stride specifying how -// far to move the window between slices. From this it generates a three -// dimensional output. The first dimension is for the channels in the input, so a -// stereo audio input would have two here for example. The second dimension is time, -// with successive frequency slices. The third dimension has an amplitude value for -// each frequency during that time slice. -// -// This means the layout when converted and saved as an image is rotated 90 degrees -// clockwise from a typical spectrogram. Time is descending down the Y axis, and -// the frequency decreases from left to right. -// -// Each value in the result represents the square root of the sum of the real and -// imaginary parts of an FFT on the current window of samples. In this way, the -// lowest dimension represents the power of each frequency in the current window, -// and adjacent windows are concatenated in the next dimension. -// -// To get a more intuitive and visual look at what this operation does, you can run -// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the -// resulting spectrogram as a PNG image. -// -// Arguments: -// input: Float representation of audio data. -// window_size: How wide the input window is in samples. For the highest efficiency -// this should be a power of two, but other values are accepted. -// stride: How widely apart the center of adjacent sample windows should be. -// -// Returns 3D representation of the audio frequencies as an image. -func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"window_size": window_size, "stride": stride} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AudioSpectrogram", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts each string in the input Tensor to its hash mod by a number of buckets. -// -// The hash function is deterministic on the content of the string within the -// process. The hash function is a keyed hash function, where attribute `key` -// defines the key of the hash function. `key` is an array of 2 elements. -// -// A strong hash is important when inputs may be malicious, e.g. URLs with -// additional components. Adversaries could try to make their inputs hash to the -// same bucket for a denial-of-service attack or to skew the results. A strong -// hash can be used to make it difficult to find inputs with a skewed hash value -// distribution over buckets. This requires that the hash function is -// seeded by a high-entropy (random) "key" unknown to the adversary. -// -// The additional robustness comes at a cost of roughly 4x higher compute -// time than `tf.string_to_hash_bucket_fast`. -// -// Arguments: -// input: The strings to assign a hash bucket. -// num_buckets: The number of buckets. -// key: The key used to seed the hash function, passed as a list of two uint64 -// elements. -// -// Returns A Tensor of the same shape as the input `string_tensor`. -func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} - opspec := tf.OpSpec{ - Type: "StringToHashBucketStrong", - Input: []tf.Input{ - input, - }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -23590,2175 +30156,52 @@ func QuantizedMatMulWithBiasAndReluAndRequantize(scope *Scope, a tf.Output, b tf return op.Output(0), op.Output(1), op.Output(2) } -// Says whether the targets are in the top `K` predictions. -// -// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the -// prediction for the target class is among the top `k` predictions among -// all predictions for example `i`. Note that the behavior of `InTopK` differs -// from the `TopK` op in its handling of ties; if multiple classes have the -// same prediction value and straddle the top-`k` boundary, all of those -// classes are considered to be in the top `k`. -// -// More formally, let -// -// \\(predictions_i\\) be the predictions for all classes for example `i`, -// \\(targets_i\\) be the target class for example `i`, -// \\(out_i\\) be the output for example `i`, -// -// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ -// -// Arguments: -// predictions: A `batch_size` x `classes` tensor. -// targets: A `batch_size` vector of class ids. -// k: Number of top elements to look at for computing precision. -// -// Returns Computed Precision at `k` as a `bool Tensor`. -func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"k": k} - opspec := tf.OpSpec{ - Type: "InTopK", - Input: []tf.Input{ - predictions, targets, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// LuAttr is an optional argument to Lu. +type LuAttr func(optionalAttr) -// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2. -type ResourceSparseApplyFtrlV2Attr func(optionalAttr) - -// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// grad_with_shrinkage = grad + 2 * l2_shrinkage * var -// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage -// linear += grad_with_shrinkage + -// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 shrinkage regulariation. Must be a scalar. -// -// lr_power: Scaling factor. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrlV2", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Transforms a Tensor into a serialized TensorProto proto. -// -// Arguments: -// tensor: A Tensor of type `T`. -// -// Returns A serialized TensorProto proto of the input tensor. -func SerializeTensor(scope *Scope, tensor tf.Output) (serialized tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SerializeTensor", - Input: []tf.Input{ - tensor, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. -type ResourceSparseApplyFtrlAttr func(optionalAttr) - -// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update relevant entries in '*var' according to the Ftrl-proximal scheme. -// -// That is for rows we have grad for, we update var, accum and linear as follows: -// accum_new = accum + grad * grad -// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// linear: Should be from a Variable(). -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyFtrl", - Input: []tf.Input{ - var_, accum, linear, grad, indices, lr, l1, l2, lr_power, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// TensorArrayConcatV2Attr is an optional argument to TensorArrayConcatV2. -type TensorArrayConcatV2Attr func(optionalAttr) - -// TensorArrayConcatV2ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. -// If not specified, defaults to -func TensorArrayConcatV2ElementShapeExcept0(value tf.Shape) TensorArrayConcatV2Attr { - return func(m optionalAttr) { - m["element_shape_except0"] = value - } -} - -// Deprecated. Use TensorArrayConcatV3 -func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV2Attr) (value tf.Output, lengths tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorArrayConcatV2", - Input: []tf.Input{ - handle, flow_in, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// RetrieveTPUEmbeddingADAMParametersAttr is an optional argument to RetrieveTPUEmbeddingADAMParameters. -type RetrieveTPUEmbeddingADAMParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingADAMParametersTableId(value int64) RetrieveTPUEmbeddingADAMParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingADAMParametersTableName(value string) RetrieveTPUEmbeddingADAMParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve ADAM embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the ADAM optimization algorithm.Parameter momenta updated by the ADAM optimization algorithm.Parameter velocities updated by the ADAM optimization algorithm. -func RetrieveTPUEmbeddingADAMParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingADAMParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Deprecated, use python implementation tf.linalg.matrix_exponential. -// -// DEPRECATED at GraphDef version 27: Use Python implementation tf.linalg.matrix_exponential instead. -func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixExponential", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// LoadTPUEmbeddingProximalAdagradParametersAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParameters. -type LoadTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingProximalAdagradParametersTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingProximalAdagradParametersTableName(value string) LoadTPUEmbeddingProximalAdagradParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load proximal Adagrad embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. -// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingProximalAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingProximalAdagradParameters", - Input: []tf.Input{ - parameters, accumulators, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// SumAttr is an optional argument to Sum. -type SumAttr func(optionalAttr) - -// SumKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func SumKeepDims(value bool) SumAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the sum of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Sum", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CudnnRNNBackpropV3Attr is an optional argument to CudnnRNNBackpropV3. -type CudnnRNNBackpropV3Attr func(optionalAttr) - -// CudnnRNNBackpropV3RnnMode sets the optional rnn_mode attribute to value. -// If not specified, defaults to "lstm" -func CudnnRNNBackpropV3RnnMode(value string) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["rnn_mode"] = value - } -} - -// CudnnRNNBackpropV3InputMode sets the optional input_mode attribute to value. -// If not specified, defaults to "linear_input" -func CudnnRNNBackpropV3InputMode(value string) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["input_mode"] = value - } -} - -// CudnnRNNBackpropV3Direction sets the optional direction attribute to value. -// If not specified, defaults to "unidirectional" -func CudnnRNNBackpropV3Direction(value string) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["direction"] = value - } -} - -// CudnnRNNBackpropV3Dropout sets the optional dropout attribute to value. -// If not specified, defaults to 0 -func CudnnRNNBackpropV3Dropout(value float32) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["dropout"] = value - } -} - -// CudnnRNNBackpropV3Seed sets the optional seed attribute to value. -// If not specified, defaults to 0 -func CudnnRNNBackpropV3Seed(value int64) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// CudnnRNNBackpropV3Seed2 sets the optional seed2 attribute to value. -// If not specified, defaults to 0 -func CudnnRNNBackpropV3Seed2(value int64) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// CudnnRNNBackpropV3TimeMajor sets the optional time_major attribute to value. -// If not specified, defaults to true -func CudnnRNNBackpropV3TimeMajor(value bool) CudnnRNNBackpropV3Attr { - return func(m optionalAttr) { - m["time_major"] = value - } -} - -// Backprop step of CudnnRNNV3. -// -// Compute the backprop of both data and weights in a RNN. Takes an extra -// "sequence_lengths" input than CudnnRNNBackprop. -// -// rnn_mode: Indicates the type of the RNN model. -// input_mode: Indicates whether there is a linear projection between the input and -// the actual computation before the first layer. 'skip_input' is only allowed -// when input_size == num_units; 'auto_select' implies 'skip_input' when -// input_size == num_units; otherwise, it implies 'linear_input'. -// direction: Indicates whether a bidirectional model will be used. Should be -// "unidirectional" or "bidirectional". -// dropout: Dropout probability. When set to 0., dropout is disabled. -// seed: The 1st part of a seed to initialize dropout. -// seed2: The 2nd part of a seed to initialize dropout. -// input: If time_major is true, this is a 3-D tensor with the shape of -// [seq_length, batch_size, input_size]. If time_major is false, the shape is -// [batch_size, seq_length, input_size]. -// input_h: If time_major is true, this is a 3-D tensor with the shape of -// [num_layer * dir, batch_size, num_units]. If time_major is false, the shape -// is [batch_size, num_layer * dir, num_units]. -// input_c: For LSTM, a 3-D tensor with the shape of -// [num_layer * dir, batch, num_units]. For other models, it is ignored. -// params: A 1-D tensor that contains the weights and biases in an opaque layout. -// The size must be created through CudnnRNNParamsSize, and initialized -// separately. Note that they might not be compatible across different -// generations. So it is a good idea to save and restore -// sequence_lengths: a vector of lengths of each input sequence. -// output: If time_major is true, this is a 3-D tensor with the shape of -// [seq_length, batch_size, dir * num_units]. If time_major is false, the -// shape is [batch_size, seq_length, dir * num_units]. -// output_h: The same shape has input_h. -// output_c: The same shape as input_c for LSTM. An empty tensor for other models. -// output_backprop: A 3-D tensor with the same shape as output in the forward pass. -// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward -// pass. -// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward -// pass. -// time_major: Indicates whether the input/output format is time major or batch -// major. -// reserve_space: The same reserve_space produced in the forward operation. -// input_backprop: The backprop to input in the forward pass. Has the same shape -// as input. -// input_h_backprop: The backprop to input_h in the forward pass. Has the same -// shape as input_h. -// input_c_backprop: The backprop to input_c in the forward pass. Has the same -// shape as input_c. -// params_backprop: The backprop to the params buffer in the forward pass. Has the -// same shape as params. -func CudnnRNNBackpropV3(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, sequence_lengths tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, host_reserved tf.Output, optional ...CudnnRNNBackpropV3Attr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CudnnRNNBackpropV3", - Input: []tf.Input{ - input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// Returns x - y element-wise. -// -// *NOTE*: `Subtract` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Sub", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedReluXAttr is an optional argument to QuantizedReluX. -type QuantizedReluXAttr func(optionalAttr) - -// QuantizedReluXOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QUINT8 -func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)` -// -// Arguments: -// -// -// min_features: The float value that the lowest quantized value represents. -// max_features: The float value that the highest quantized value represents. -// -// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. -func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedReluX", - Input: []tf.Input{ - features, max_value, min_features, max_features, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Computes square root of x element-wise. -// -// I.e., \\(y = \sqrt{x} = x^{1/2}\\). -func Sqrt(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Sqrt", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes a range that covers the actual values present in a quantized tensor. -// -// Given a quantized tensor described by `(input, input_min, input_max)`, outputs a -// range that covers the actual values present in that tensor. This op is typically -// used to produce the `requested_output_min` and `requested_output_max` for -// `Requantize`. -// -// Arguments: -// -// input_min: The float value that the minimum quantized input value represents. -// input_max: The float value that the maximum quantized input value represents. -// -// Returns The computed min output.the computed max output. -func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RequantizationRange", - Input: []tf.Input{ - input, input_min, input_max, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. -type ResourceSparseApplyAdagradAttr func(optionalAttr) - -// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. -// If not specified, defaults to true -func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { - return func(m optionalAttr) { - m["update_slots"] = value - } -} - -// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. -// -// That is for rows we have grad for, we update var and accum as follows: -// accum += grad * grad -// var -= lr * grad * (1 / sqrt(accum)) -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// -// Returns the created operation. -func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyAdagrad", - Input: []tf.Input{ - var_, accum, lr, grad, indices, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// VariableShapeAttr is an optional argument to VariableShape. -type VariableShapeAttr func(optionalAttr) - -// VariableShapeOutType sets the optional out_type attribute to value. +// LuOutputIdxType sets the optional output_idx_type attribute to value. // If not specified, defaults to DT_INT32 -func VariableShapeOutType(value tf.DataType) VariableShapeAttr { +func LuOutputIdxType(value tf.DataType) LuAttr { return func(m optionalAttr) { - m["out_type"] = value + m["output_idx_type"] = value } } -// Returns the shape of the variable pointed to by `resource`. +// Computes the LU decomposition of one or more square matrices. // -// This operation returns a 1-D integer tensor representing the shape of `input`. +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. // -// For example: +// The input has to be invertible. // -// ``` -// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] -// shape(t) ==> [2, 2, 3] -// ``` -func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "VariableShape", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// A container for an iterator resource. +// The output consists of two tensors LU and P containing the LU decomposition +// of all input submatrices `[..., :, :]`. LU encodes the lower triangular and +// upper triangular factors. // -// Returns A handle to the iterator that can be passed to a "MakeIterator" -// or "IteratorGetNext" op. -func Iterator(scope *Scope, shared_name string, container string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"shared_name": shared_name, "container": container, "output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "Iterator", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap. -type TakeManySparseFromTensorsMapAttr func(optionalAttr) - -// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value. +// For each input submatrix of shape `[M, M]`, L is a lower triangular matrix of +// shape `[M, M]` with unit diagonal whose entries correspond to the strictly lower +// triangular part of LU. U is a upper triangular matrix of shape `[M, M]` whose +// entries correspond to the upper triangular part, including the diagonal, of LU. // -// value: The container name for the `SparseTensorsMap` read by this op. -// If not specified, defaults to "" -func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value. -// -// value: The shared name for the `SparseTensorsMap` read by this op. -// It should not be blank; rather the `shared_name` or unique Operation name -// of the Op that created the original `SparseTensorsMap` should be used. -// If not specified, defaults to "" -func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. -// -// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where -// `N` is the minibatch size and the rows correspond to the output handles of -// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the -// original `SparseTensor` objects that went into the given input ops must all -// match. When the final `SparseTensor` is created, it has rank one -// higher than the ranks of the incoming `SparseTensor` objects -// (they have been concatenated along a new row dimension on the left). -// -// The output `SparseTensor` object's shape values for all dimensions but the -// first are the max across the input `SparseTensor` objects' shape values -// for the corresponding dimensions. Its first shape value is `N`, the minibatch -// size. -// -// The input `SparseTensor` objects' indices are assumed ordered in -// standard lexicographic order. If this is not the case, after this -// step run `SparseReorder` to restore index ordering. -// -// For example, if the handles represent an input, which is a `[2, 3]` matrix -// representing two original `SparseTensor` objects: -// -// ``` -// index = [ 0] -// [10] -// [20] -// values = [1, 2, 3] -// shape = [50] -// ``` -// -// and -// -// ``` -// index = [ 2] -// [10] -// values = [4, 5] -// shape = [30] -// ``` -// -// then the final `SparseTensor` will be: -// -// ``` -// index = [0 0] -// [0 10] -// [0 20] -// [1 2] -// [1 10] -// values = [1, 2, 3, 4, 5] -// shape = [2 50] -// ``` +// P represents a permutation matrix encoded as a list of indices each between `0` +// and `M-1`, inclusive. If P_mat denotes the permutation matrix corresponding to +// P, then the L, U and P satisfies P_mat * input = L * U. // // Arguments: -// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. -// Shape: `[N]`. -// dtype: The `dtype` of the `SparseTensor` objects stored in the -// `SparseTensorsMap`. -// -// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`. -func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TakeManySparseFromTensorsMap", - Input: []tf.Input{ - sparse_handles, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. -type ResourceSparseApplyMomentumAttr func(optionalAttr) - -// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. -// -// value: If `True`, the tensor passed to compute grad will be -// var - lr * momentum * accum, so in the end, the var you get is actually -// var - lr * momentum * accum. -// If not specified, defaults to false -func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update relevant entries in '*var' and '*accum' according to the momentum scheme. -// -// Set use_nesterov = True if you want to use Nesterov momentum. -// -// That is for rows we have grad for, we update var and accum as follows: -// -// accum = accum * momentum + grad -// var -= lr * accum -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// momentum: Momentum. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyMomentum", - Input: []tf.Input{ - var_, accum, lr, grad, indices, momentum, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Increments variable pointed to by 'resource' until it reaches 'limit'. -// -// Arguments: -// resource: Should be from a scalar `Variable` node. -// limit: If incrementing ref would bring it above limit, instead generates an -// 'OutOfRange' error. -// -// -// Returns A copy of the input before increment. If nothing else modifies the -// input, the values produced will all be distinct. -func ResourceCountUpTo(scope *Scope, resource tf.Output, limit int64, T tf.DataType) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"limit": limit, "T": T} - opspec := tf.OpSpec{ - Type: "ResourceCountUpTo", - Input: []tf.Input{ - resource, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes softmax cross entropy cost and gradients to backpropagate. -// -// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept -// a matrix of label probabilities, but rather a single label per row -// of features. This label is considered to have probability 1.0 for the -// given row. -// -// Inputs are the logits, not probabilities. -// -// Arguments: -// features: batch_size x num_classes matrix -// labels: batch_size vector with values in [0, num_classes). -// This is the label for the given minibatch entry. -// -// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix). -func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSoftmaxCrossEntropyWithLogits", - Input: []tf.Input{ - features, labels, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// QuantizedReluAttr is an optional argument to QuantizedRelu. -type QuantizedReluAttr func(optionalAttr) - -// QuantizedReluOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QUINT8 -func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Computes Quantized Rectified Linear: `max(features, 0)` -// -// Arguments: -// -// min_features: The float value that the lowest quantized value represents. -// max_features: The float value that the highest quantized value represents. -// -// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. -func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedRelu", - Input: []tf.Input{ - features, min_features, max_features, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// AudioSummaryAttr is an optional argument to AudioSummary. -type AudioSummaryAttr func(optionalAttr) - -// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value. -// -// value: Max number of batch elements to generate audio for. -// If not specified, defaults to 3 -// -// REQUIRES: value >= 1 -func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr { - return func(m optionalAttr) { - m["max_outputs"] = value - } -} - -// Outputs a `Summary` protocol buffer with audio. -// -// DEPRECATED at GraphDef version 15: Use AudioSummaryV2. -// -// The summary has up to `max_outputs` summary values containing audio. The -// audio is built from `tensor` which must be 3-D with shape `[batch_size, -// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are -// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. -// -// The `tag` argument is a scalar `Tensor` of type `string`. It is used to -// build the `tag` of the summary values: -// -// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. -// * If `max_outputs` is greater than 1, the summary value tags are -// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. -// -// Arguments: -// tag: Scalar. Used to build the `tag` attribute of the summary values. -// tensor: 2-D of shape `[batch_size, frames]`. -// sample_rate: The sample rate of the signal in hertz. -// -// Returns Scalar. Serialized `Summary` protocol buffer. -func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"sample_rate": sample_rate} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AudioSummary", - Input: []tf.Input{ - tag, tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Partitions `data` into `num_partitions` tensors using indices from `partitions`. -// -// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` -// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` -// are placed in `outputs[i]` in lexicographic order of `js`, and the first -// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. -// In detail, -// -// ```python -// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] -// -// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) -// ``` -// -// `data.shape` must start with `partitions.shape`. -// -// For example: -// -// ```python -// # Scalar partitions. -// partitions = 1 -// num_partitions = 2 -// data = [10, 20] -// outputs[0] = [] # Empty with shape [0, 2] -// outputs[1] = [[10, 20]] -// -// # Vector partitions. -// partitions = [0, 0, 1, 1, 0] -// num_partitions = 2 -// data = [10, 20, 30, 40, 50] -// outputs[0] = [10, 20, 50] -// outputs[1] = [30, 40] -// ``` -// -// See `dynamic_stitch` for an example on how to merge partitions back. -// -//
-// -//
-// -// Arguments: -// -// partitions: Any shape. Indices in the range `[0, num_partitions)`. -// num_partitions: The number of partitions to output. -func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_partitions": num_partitions} - opspec := tf.OpSpec{ - Type: "DynamicPartition", - Input: []tf.Input{ - data, partitions, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("DynamicPartition", err) - return - } - return outputs -} - -// Computes the mean along sparse segments of a tensor. -// -// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is -// misisng, the `output` tensor at that position will be zeroed. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. -// num_segments: Should equal the number of distinct segment IDs. -// -// Returns Has same shape as data, except for dimension 0 which has size -// `num_segments`. -func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentMeanWithNumSegments", - Input: []tf.Input{ - data, indices, segment_ids, num_segments, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to RetrieveTPUEmbeddingStochasticGradientDescentParameters. -type RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve SGD embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the stochastic gradient descent optimization algorithm. -func RetrieveTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr) (parameters tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingStochasticGradientDescentParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedDepthwiseConv2DWithBiasAttr is an optional argument to QuantizedDepthwiseConv2DWithBias. -type QuantizedDepthwiseConv2DWithBiasAttr func(optionalAttr) - -// QuantizedDepthwiseConv2DWithBiasOutType sets the optional out_type attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_QINT32 -func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value. -// -// value: List of dilation values. -// If not specified, defaults to -func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes quantized depthwise Conv2D with Bias. -// -// Arguments: -// input: The original input tensor. -// filter: The original filter tensor. -// bias: The original bias tensor. -// min_input: The float value that the minimum quantized input value represents. -// max_input: The float value that the maximum quantized input value represents. -// min_filter: The float value that the minimum quantized filter value represents. -// max_filter: The float value that the maximum quantized filter value represents. -// strides: List of stride values. -// -// -// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedDepthwiseConv2DWithBias(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedDepthwiseConv2DWithBias", - Input: []tf.Input{ - input, filter, bias, min_input, max_input, min_filter, max_filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Reshapes a SparseTensor to represent values in a new dense shape. -// -// This operation has the same semantics as reshape on the represented dense -// tensor. The `input_indices` are recomputed based on the requested `new_shape`. -// -// If one component of `new_shape` is the special value -1, the size of that -// dimension is computed so that the total dense size remains constant. At -// most one component of `new_shape` can be -1. The number of dense elements -// implied by `new_shape` must be the same as the number of dense elements -// originally implied by `input_shape`. -// -// Reshaping does not affect the order of values in the SparseTensor. -// -// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape` -// has length `R_out`, then `input_indices` has shape `[N, R_in]`, -// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and -// `output_shape` has length `R_out`. -// -// Arguments: -// input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a -// SparseTensor. -// input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape. -// new_shape: 1-D. `R_out` vector with the requested new dense shape. -// -// Returns 2-D. `N x R_out` matrix with the updated indices of non-empty -// values in the output SparseTensor.1-D. `R_out` vector with the full dense shape of the output -// SparseTensor. This is the same as `new_shape` but with any -1 dimensions -// filled in. -func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseReshape", - Input: []tf.Input{ - input_indices, input_shape, new_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. -type MapUnstageNoKeyAttr func(optionalAttr) - -// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapUnstageNoKeyContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op removes and returns a random (key, value) -// -// from the underlying container. If the underlying container -// does not contain elements, the op will block until it does. -func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapUnstageNoKey", - Input: []tf.Input{ - indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - key = op.Output(idx) - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapUnstageNoKey", err) - return - } - return key, values -} - -// Creates a sequence of numbers. -// -// This operation creates a sequence of numbers that begins at `start` and -// extends by increments of `delta` up to but not including `limit`. -// -// For example: -// -// ``` -// # 'start' is 3 -// # 'limit' is 18 -// # 'delta' is 3 -// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] -// ``` -// -// Arguments: -// start: 0-D (scalar). First entry in the sequence. -// limit: 0-D (scalar). Upper limit of sequence, exclusive. -// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. -// -// Returns 1-D. -func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Range", - Input: []tf.Input{ - start, limit, delta, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent. -type ResourceApplyGradientDescentAttr func(optionalAttr) - -// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, the subtraction will be protected by a lock; -// otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' by subtracting 'alpha' * 'delta' from it. -// -// Arguments: -// var_: Should be from a Variable(). -// alpha: Scaling factor. Must be a scalar. -// delta: The change. -// -// Returns the created operation. -func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyGradientDescent", - Input: []tf.Input{ - var_, alpha, delta, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Computes softsign gradients for a softsign operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding softsign operation. -// features: The features passed as input to the corresponding softsign operation. -// -// Returns The gradients: `gradients / (1 + abs(features)) ** 2`. -func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SoftsignGrad", - Input: []tf.Input{ - gradients, features, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Compute the upper regularized incomplete Gamma function `Q(a, x)`. -// -// The upper regularized incomplete Gamma function is defined as: -// -// \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\) -// -// where -// -// \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\) -// -// is the upper incomplete Gama function. -// -// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete -// Gamma function. -func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Igammac", - Input: []tf.Input{ - a, x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Selects num_to_sample rows of input using the KMeans++ criterion. -// -// Rows of points are assumed to be input points. One row is selected at random. -// Subsequent rows are sampled with probability proportional to the squared L2 -// distance from the nearest row selected thus far till num_to_sample rows have -// been sampled. -// -// Arguments: -// points: Matrix of shape (n, d). Rows are assumed to be input points. -// num_to_sample: Scalar. The number of rows to sample. This value must not be larger than n. -// seed: Scalar. Seed for initializing the random number generator. -// num_retries_per_sample: Scalar. For each row that is sampled, this parameter -// specifies the number of additional points to draw from the current -// distribution before selecting the best. If a negative value is specified, a -// heuristic is used to sample O(log(num_to_sample)) additional points. -// -// Returns Matrix of shape (num_to_sample, d). The sampled rows. -func KmeansPlusPlusInitialization(scope *Scope, points tf.Output, num_to_sample tf.Output, seed tf.Output, num_retries_per_sample tf.Output) (samples tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "KmeansPlusPlusInitialization", - Input: []tf.Input{ - points, num_to_sample, seed, num_retries_per_sample, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingFTRLParametersAttr is an optional argument to RetrieveTPUEmbeddingFTRLParameters. -type RetrieveTPUEmbeddingFTRLParametersAttr func(optionalAttr) - -// RetrieveTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingFTRLParametersTableId(value int64) RetrieveTPUEmbeddingFTRLParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingFTRLParametersTableName(value string) RetrieveTPUEmbeddingFTRLParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve FTRL embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the FTRL optimization algorithm.Parameter accumulators updated by the FTRL optimization algorithm.Parameter linears updated by the FTRL optimization algorithm. -func RetrieveTPUEmbeddingFTRLParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingFTRLParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// This op consumes a lock created by `MutexLock`. -// -// This op exists to consume a tensor created by `MutexLock` (other than -// direct control dependencies). It should be the only that consumes the tensor, -// and will raise an error if it is not. Its only purpose is to keep the -// mutex lock tensor alive until it is consumed by this op. -// -// **NOTE**: This operation must run on the same device as its input. This may -// be enforced via the `colocate_with` mechanism. -// -// Arguments: -// mutex_lock: A tensor returned by `MutexLock`. -// -// Returns the created operation. -func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ConsumeMutexLock", - Input: []tf.Input{ - mutex_lock, - }, - } - return scope.AddOperation(opspec) -} - -// The gradient of SparseFillEmptyRows. -// -// Takes vectors reverse_index_map, shaped `[N]`, and grad_values, -// shaped `[N_full]`, where `N_full >= N` and copies data into either -// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and -// `d_default_value` is a scalar. -// -// d_values[j] = grad_values[reverse_index_map[j]] -// d_default_value = sum_{k : 0 .. N_full - 1} ( -// grad_values[k] * 1{k not in reverse_index_map}) -// -// Arguments: -// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows. -// grad_values: 1-D. The gradients from backprop. -// -// Returns 1-D. The backprop into values.0-D. The backprop into default_value. -func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseFillEmptyRowsGrad", - Input: []tf.Input{ - reverse_index_map, grad_values, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingADAMParametersGradAccumDebug. -type RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve ADAM embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the ADAM optimization algorithm.Parameter momenta updated by the ADAM optimization algorithm.Parameter velocities updated by the ADAM optimization algorithm.Parameter gradient_accumulators updated by the ADAM optimization algorithm. -func RetrieveTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// LoadTPUEmbeddingMomentumParametersAttr is an optional argument to LoadTPUEmbeddingMomentumParameters. -type LoadTPUEmbeddingMomentumParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingMomentumParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingMomentumParametersTableId(value int64) LoadTPUEmbeddingMomentumParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingMomentumParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingMomentumParametersTableName(value string) LoadTPUEmbeddingMomentumParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Momentum embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the Momentum optimization algorithm. -// momenta: Value of momenta used in the Momentum optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingMomentumParameters(scope *Scope, parameters tf.Output, momenta tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingMomentumParameters", - Input: []tf.Input{ - parameters, momenta, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// IdentityReaderV2Attr is an optional argument to IdentityReaderV2. -type IdentityReaderV2Attr func(optionalAttr) - -// IdentityReaderV2Container sets the optional container attribute to value. -// -// value: If non-empty, this reader is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func IdentityReaderV2Container(value string) IdentityReaderV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// IdentityReaderV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this reader is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// A Reader that outputs the queued work as both the key and value. -// -// To use, enqueue strings in a Queue. ReaderRead will take the front -// work string and output (work, work). -// -// Returns The handle to reference the Reader. -func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "IdentityReaderV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reorders a SparseTensor into the canonical, row-major ordering. -// -// Note that by convention, all sparse ops preserve the canonical ordering along -// increasing dimension number. The only time ordering can be violated is during -// manual manipulation of the indices and values vectors to add entries. -// -// Reordering does not affect the shape of the SparseTensor. -// -// If the tensor has rank `R` and `N` non-empty values, `input_indices` has -// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`. -// -// Arguments: -// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. -// input_shape: 1-D. Shape of the input SparseTensor. -// -// Returns 2-D. `N x R` matrix with the same indices as input_indices, but -// in canonical row-major ordering.1-D. `N` non-empty values corresponding to `output_indices`. -func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseReorder", - Input: []tf.Input{ - input_indices, input_values, input_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Creates a dataset that emits each dim-0 slice of `components` once. -func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "TensorSliceDataset", - Input: []tf.Input{ - tf.OutputList(components), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reads the value of a variable. -// -// The tensor returned by this operation is immutable. -// -// The value returned by this operation is guaranteed to be influenced by all the -// writes on which this operation depends directly or indirectly, and to not be -// influenced by any of the writes which depend directly or indirectly on this -// operation. -// -// Arguments: -// resource: handle to the resource in which to store the variable. -// dtype: the dtype of the value. -func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - opspec := tf.OpSpec{ - Type: "ReadVariableOp", - Input: []tf.Input{ - resource, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StageSizeAttr is an optional argument to StageSize. -type StageSizeAttr func(optionalAttr) - -// StageSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageSizeCapacity(value int64) StageSizeAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// StageSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageSizeMemoryLimit(value int64) StageSizeAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// StageSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func StageSizeContainer(value string) StageSizeAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// StageSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func StageSizeSharedName(value string) StageSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of elements in the underlying container. -func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StageSize", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// An op that receives embedding activations on the TPU. -// -// The TPU system performs the embedding lookups and aggregations specified by -// the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The -// results of these aggregations are visible to the Tensorflow Graph as the -// outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing -// one Tensor of activations per table specified in the model. There can be at -// most one RecvTPUEmbeddingActivations op in the TPU graph. -// -// Arguments: -// num_outputs: The number of output activation tensors, equal to the number of -// embedding tables in the model. -// config: Serialized TPUEmbeddingConfiguration proto. -// -// Returns A TensorList of embedding activations containing one Tensor per -// embedding table in the model. -func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string) (outputs []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_outputs": num_outputs, "config": config} - opspec := tf.OpSpec{ - Type: "RecvTPUEmbeddingActivations", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { - scope.UpdateErr("RecvTPUEmbeddingActivations", err) - return - } - return outputs -} - -// UnicodeEncodeAttr is an optional argument to UnicodeEncode. -type UnicodeEncodeAttr func(optionalAttr) - -// UnicodeEncodeErrors sets the optional errors attribute to value. -// -// value: Error handling policy when there is invalid formatting found in the input. -// The value of 'strict' will cause the operation to produce a InvalidArgument -// error on any invalid input formatting. A value of 'replace' (the default) will -// cause the operation to replace any invalid formatting in the input with the -// `replacement_char` codepoint. A value of 'ignore' will cause the operation to -// skip any invalid formatting in the input and produce no corresponding output -// character. -// If not specified, defaults to "replace" -func UnicodeEncodeErrors(value string) UnicodeEncodeAttr { - return func(m optionalAttr) { - m["errors"] = value - } -} - -// UnicodeEncodeReplacementChar sets the optional replacement_char attribute to value. -// -// value: The replacement character codepoint to be used in place of any invalid -// formatting in the input when `errors='replace'`. Any valid unicode codepoint may -// be used. The default value is the default unicode replacement character is -// 0xFFFD (U+65533). -// If not specified, defaults to 65533 -func UnicodeEncodeReplacementChar(value int64) UnicodeEncodeAttr { - return func(m optionalAttr) { - m["replacement_char"] = value - } -} - -// Encode a tensor of ints into unicode strings. -// -// Returns a vector of strings, where `output[i]` is constructed by encoding the -// Unicode codepoints in `input_values[input_splits[i]:input_splits[i+1]]` -// using `output_encoding`. -// -// --- -// -// Example: -// -// ``` -// input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100] -// input_splits = [0, 5, 10] -// output_encoding = 'UTF-8' -// -// output = ['Hello', 'World'] -// ``` -// -// Arguments: -// input_values: A 1D tensor containing the unicode codepoints that should be encoded. -// input_splits: A 1D tensor specifying how the unicode codepoints should be split into strings. -// In particular, `output[i]` is constructed by encoding the codepoints in the -// slice `input_values[input_splits[i]:input_splits[i+1]]`. -// output_encoding: Unicode encoding of the output strings. Valid encodings are: `"UTF-8", -// "UTF-16-BE", and "UTF-32-BE"`. -// -// Returns The 1-D Tensor of strings encoded from the provided unicode codepoints. -func UnicodeEncode(scope *Scope, input_values tf.Output, input_splits tf.Output, output_encoding string, optional ...UnicodeEncodeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_encoding": output_encoding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UnicodeEncode", - Input: []tf.Input{ - input_values, input_splits, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes requantization range per channel. -// -// Arguments: -// input: The original input tensor. -// input_min: The minimum value of the input tensor -// input_max: The maximum value of the input tensor. -// clip_value_max: The maximum value of the output that needs to be clipped. -// Example: set this to 6 for Relu6. -// -// Returns The minimum value of the final output tensorThe maximum value of the final output tensor. -func RequantizationRangePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, clip_value_max float32) (output_min tf.Output, output_max tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"clip_value_max": clip_value_max} - opspec := tf.OpSpec{ - Type: "RequantizationRangePerChannel", - Input: []tf.Input{ - input, input_min, input_max, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Performs gradient updates of embedding tables. -// -// Arguments: -// inputs: A TensorList of gradients with which to update embedding tables. -// This argument has the same length and shapes as the return value of -// RecvTPUEmbeddingActivations, but contains gradients of the model's loss -// with respect to the embedding activations. The embedding tables are updated -// from these gradients via the optimizer specified in the TPU embedding -// configuration given to tpu.initialize_system. -// learning_rates: A TensorList of float32 scalars, one for each dynamic learning -// rate tag: see the comments in -// //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto. -// Multiple tables can share the same dynamic learning rate tag as specified -// in the configuration. If the learning rates for all tables are constant, -// this list should be empty. -// config: Serialized TPUEmbeddingConfiguration proto. -// -// Returns the created operation. -func SendTPUEmbeddingGradients(scope *Scope, inputs []tf.Output, learning_rates []tf.Output, config string) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"config": config} - opspec := tf.OpSpec{ - Type: "SendTPUEmbeddingGradients", - Input: []tf.Input{ - tf.OutputList(inputs), tf.OutputList(learning_rates), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// EnqueueTPUEmbeddingIntegerBatchAttr is an optional argument to EnqueueTPUEmbeddingIntegerBatch. -type EnqueueTPUEmbeddingIntegerBatchAttr func(optionalAttr) - -// EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. Should be >= 0 and less than the number -// of TPU cores in the task on which the node is placed. -// If not specified, defaults to -1 -func EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingIntegerBatchAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// An op that enqueues a list of input batch tensors to TPUEmbedding. -// -// Arguments: -// batch: A list of 1D tensors, one for each embedding table, containing the -// indices into the tables. -// mode_override: A string input that overrides the mode specified in the -// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', -// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set -// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. -// -// Returns the created operation. -func EnqueueTPUEmbeddingIntegerBatch(scope *Scope, batch []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingIntegerBatchAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EnqueueTPUEmbeddingIntegerBatch", - Input: []tf.Input{ - tf.OutputList(batch), mode_override, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// EncodeBase64Attr is an optional argument to EncodeBase64. -type EncodeBase64Attr func(optionalAttr) - -// EncodeBase64Pad sets the optional pad attribute to value. -// -// value: Bool whether padding is applied at the ends. -// If not specified, defaults to false -func EncodeBase64Pad(value bool) EncodeBase64Attr { - return func(m optionalAttr) { - m["pad"] = value - } -} - -// Encode strings into web-safe base64 format. -// -// Refer to the following article for more information on base64 format: -// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the -// end so that the encoded has length multiple of 4. See Padding section of the -// link above. -// -// Web-safe means that the encoder uses - and _ instead of + and /. -// -// Arguments: -// input: Strings to be encoded. -// -// Returns Input strings encoded in base64. -func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EncodeBase64", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AngleAttr is an optional argument to Angle. -type AngleAttr func(optionalAttr) - -// AngleTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func AngleTout(value tf.DataType) AngleAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Returns the argument of a complex number. -// -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// type `float` that is the argument of each element in `input`. All elements in -// `input` must be complex numbers of the form \\(a + bj\\), where *a* -// is the real part and *b* is the imaginary part. -// -// The argument returned by this operation is of the form \\(atan2(b, a)\\). -// -// For example: -// -// ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.angle(input) ==> [2.0132, 1.056] -// ``` -// -// @compatibility(numpy) -// Equivalent to np.angle. +// input: A tensor of shape `[..., M, M]` whose inner-most 2 dimensions form matrices of +// size `[M, M]`. +// +// Returns A tensor of shape `[..., M, M]` whose strictly lower triangular part denotes the +// lower triangular factor `L` with unit diagonal, and whose upper triangular part +// denotes the upper triangular factor `U`.Permutation of the rows encoded as a list of indices in `0..M-1`. Shape is +// `[..., M]`. +// @compatibility(scipy) +// Similar to `scipy.linalg.lu`, except the triangular factors `L` and `U` are +// packed into a single tensor, the permutation is applied to `input` instead of +// the right hand side and the permutation `P` is returned as a list of indices +// instead of a permutation matrix. // @end_compatibility -func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Output) { +func Lu(scope *Scope, input tf.Output, optional ...LuAttr) (lu tf.Output, p tf.Output) { if scope.Err() != nil { return } @@ -25767,2771 +30210,7 @@ func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Outp a(attrs) } opspec := tf.OpSpec{ - Type: "Angle", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AvgPoolAttr is an optional argument to AvgPool. -type AvgPoolAttr func(optionalAttr) - -// AvgPoolDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func AvgPoolDataFormat(value string) AvgPoolAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs average pooling on the input. -// -// Each entry in `output` is the mean of the corresponding size `ksize` -// window in `value`. -// -// Arguments: -// value: 4-D with shape `[batch, height, width, channels]`. -// ksize: The size of the sliding window for each dimension of `value`. -// strides: The stride of the sliding window for each dimension of `value`. -// padding: The type of padding algorithm to use. -// -// Returns The average pooled output tensor. -func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AvgPool", - Input: []tf.Input{ - value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// An Op to sum inputs across replicated TPU instances. -// -// Each instance supplies its own input. -// -// For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`. -// Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0, -// and `B, D, F, H` as group 1. Thus we get the outputs: -// `[A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]`. -// -// Arguments: -// input: The local input to the sum. -// group_assignment: An int32 tensor with shape -// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the -// replica ids in the ith subgroup. -// -// Returns The sum of all the distributed inputs. -func CrossReplicaSum(scope *Scope, input tf.Output, group_assignment tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "CrossReplicaSum", - Input: []tf.Input{ - input, group_assignment, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset with a range of values. Corresponds to python's xrange. -// -// Arguments: -// start: corresponds to start in python's xrange(). -// stop: corresponds to stop in python's xrange(). -// step: corresponds to step in python's xrange(). -// -// -func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "RangeDataset", - Input: []tf.Input{ - start, stop, step, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceGatherAttr is an optional argument to ResourceGather. -type ResourceGatherAttr func(optionalAttr) - -// ResourceGatherBatchDims sets the optional batch_dims attribute to value. -// If not specified, defaults to 0 -func ResourceGatherBatchDims(value int64) ResourceGatherAttr { - return func(m optionalAttr) { - m["batch_dims"] = value - } -} - -// ResourceGatherValidateIndices sets the optional validate_indices attribute to value. -// If not specified, defaults to true -func ResourceGatherValidateIndices(value bool) ResourceGatherAttr { - return func(m optionalAttr) { - m["validate_indices"] = value - } -} - -// Gather slices from the variable pointed to by `resource` according to `indices`. -// -// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). -// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: -// -// ```python -// # Scalar indices -// output[:, ..., :] = params[indices, :, ... :] -// -// # Vector indices -// output[i, :, ..., :] = params[indices[i], :, ... :] -// -// # Higher rank indices -// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] -// ``` -func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceGather", - Input: []tf.Input{ - resource, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CudnnRNNAttr is an optional argument to CudnnRNN. -type CudnnRNNAttr func(optionalAttr) - -// CudnnRNNRnnMode sets the optional rnn_mode attribute to value. -// If not specified, defaults to "lstm" -func CudnnRNNRnnMode(value string) CudnnRNNAttr { - return func(m optionalAttr) { - m["rnn_mode"] = value - } -} - -// CudnnRNNInputMode sets the optional input_mode attribute to value. -// If not specified, defaults to "linear_input" -func CudnnRNNInputMode(value string) CudnnRNNAttr { - return func(m optionalAttr) { - m["input_mode"] = value - } -} - -// CudnnRNNDirection sets the optional direction attribute to value. -// If not specified, defaults to "unidirectional" -func CudnnRNNDirection(value string) CudnnRNNAttr { - return func(m optionalAttr) { - m["direction"] = value - } -} - -// CudnnRNNDropout sets the optional dropout attribute to value. -// If not specified, defaults to 0 -func CudnnRNNDropout(value float32) CudnnRNNAttr { - return func(m optionalAttr) { - m["dropout"] = value - } -} - -// CudnnRNNSeed sets the optional seed attribute to value. -// If not specified, defaults to 0 -func CudnnRNNSeed(value int64) CudnnRNNAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// CudnnRNNSeed2 sets the optional seed2 attribute to value. -// If not specified, defaults to 0 -func CudnnRNNSeed2(value int64) CudnnRNNAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// CudnnRNNIsTraining sets the optional is_training attribute to value. -// If not specified, defaults to true -func CudnnRNNIsTraining(value bool) CudnnRNNAttr { - return func(m optionalAttr) { - m["is_training"] = value - } -} - -// A RNN backed by cuDNN. -// -// Computes the RNN from the input and initial states, with respect to the params -// buffer. -// -// rnn_mode: Indicates the type of the RNN model. -// input_mode: Indicate whether there is a linear projection between the input and -// the actual computation before the first layer. 'skip_input' is only allowed -// when input_size == num_units; 'auto_select' implies 'skip_input' when -// input_size == num_units; otherwise, it implies 'linear_input'. -// direction: Indicates whether a bidirectional model will be used. Should be -// "unidirectional" or "bidirectional". -// dropout: Dropout probability. When set to 0., dropout is disabled. -// seed: The 1st part of a seed to initialize dropout. -// seed2: The 2nd part of a seed to initialize dropout. -// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. -// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, -// num_units]. -// input_c: For LSTM, a 3-D tensor with the shape of -// [num_layer * dir, batch, num_units]. For other models, it is ignored. -// params: A 1-D tensor that contains the weights and biases in an opaque layout. -// The size must be created through CudnnRNNParamsSize, and initialized -// separately. Note that they might not be compatible across different -// generations. So it is a good idea to save and restore -// output: A 3-D tensor with the shape of [seq_length, batch_size, -// dir * num_units]. -// output_h: The same shape has input_h. -// output_c: The same shape as input_c for LSTM. An empty tensor for other models. -// is_training: Indicates whether this operation is used for inferenece or -// training. -// reserve_space: An opaque tensor that can be used in backprop calculation. It -// is only produced if is_training is false. -func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNAttr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CudnnRNN", - Input: []tf.Input{ - input, input_h, input_c, params, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// SerializeSparseAttr is an optional argument to SerializeSparse. -type SerializeSparseAttr func(optionalAttr) - -// SerializeSparseOutType sets the optional out_type attribute to value. -// -// value: The `dtype` to use for serialization; the supported types are `string` -// (default) and `variant`. -// If not specified, defaults to DT_STRING -func SerializeSparseOutType(value tf.DataType) SerializeSparseAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Serialize a `SparseTensor` into a `[3]` `Tensor` object. -// -// Arguments: -// sparse_indices: 2-D. The `indices` of the `SparseTensor`. -// sparse_values: 1-D. The `values` of the `SparseTensor`. -// sparse_shape: 1-D. The `shape` of the `SparseTensor`. -func SerializeSparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeSparseAttr) (serialized_sparse tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SerializeSparse", - Input: []tf.Input{ - sparse_indices, sparse_values, sparse_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns x + y element-wise. -// -// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "AddV2", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes log softmax activations. -// -// For each batch `i` and class `j` we have -// -// logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i]))) -// -// Arguments: -// logits: 2-D with shape `[batch_size, num_classes]`. -// -// Returns Same shape as `logits`. -func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LogSoftmax", - Input: []tf.Input{ - logits, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the next record (key, value pair) produced by a Reader. -// -// Will dequeue from the input queue if necessary (e.g. when the -// Reader needs to start reading from a new file since it has finished -// with the previous file). -// -// Arguments: -// reader_handle: Handle to a Reader. -// queue_handle: Handle to a Queue, with string work items. -// -// Returns A scalar.A scalar. -func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderReadV2", - Input: []tf.Input{ - reader_handle, queue_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Determine the script codes of a given tensor of Unicode integer code points. -// -// This operation converts Unicode code points to script codes corresponding to -// each code point. Script codes correspond to International Components for -// Unicode (ICU) UScriptCode values. See http://icu-project.org/apiref/icu4c/uscript_8h.html. -// Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will -// match input shape. -// -// Arguments: -// input: A Tensor of int32 Unicode code points. -// -// Returns A Tensor of int32 script codes corresponding to each input code point. -func UnicodeScript(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnicodeScript", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Shuts down a running distributed TPU system. -// -// The op returns an error if no system is running. -// -// Returns the created operation. -func ShutdownDistributedTPU(scope *Scope) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ShutdownDistributedTPU", - } - return scope.AddOperation(opspec) -} - -// MultinomialAttr is an optional argument to Multinomial. -type MultinomialAttr func(optionalAttr) - -// MultinomialSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 is set to be non-zero, the internal random number -// generator is seeded by the given seed. Otherwise, a random seed is used. -// If not specified, defaults to 0 -func MultinomialSeed(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// MultinomialSeed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func MultinomialSeed2(value int64) MultinomialAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// MultinomialOutputDtype sets the optional output_dtype attribute to value. -// If not specified, defaults to DT_INT64 -func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { - return func(m optionalAttr) { - m["output_dtype"] = value - } -} - -// Draws samples from a multinomial distribution. -// -// Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. -// -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Multinomial", - Input: []tf.Input{ - logits, num_samples, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StringJoinAttr is an optional argument to StringJoin. -type StringJoinAttr func(optionalAttr) - -// StringJoinSeparator sets the optional separator attribute to value. -// -// value: string, an optional join separator. -// If not specified, defaults to "" -func StringJoinSeparator(value string) StringJoinAttr { - return func(m optionalAttr) { - m["separator"] = value - } -} - -// Joins the strings in the given list of string tensors into one tensor; -// -// with the given separator (default is an empty separator). -// -// Arguments: -// inputs: A list of string tensors. The tensors must all have the same shape, -// or be scalars. Scalars may be mixed in; these will be broadcast to the shape -// of non-scalar inputs. -func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StringJoin", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PackAttr is an optional argument to Pack. -type PackAttr func(optionalAttr) - -// PackAxis sets the optional axis attribute to value. -// -// value: Dimension along which to pack. Negative values wrap around, so the -// valid range is `[-(R+1), R+1)`. -// If not specified, defaults to 0 -func PackAxis(value int64) PackAttr { - return func(m optionalAttr) { - m["axis"] = value - } -} - -// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. -// -// Packs the `N` tensors in `values` into a tensor with rank one higher than each -// tensor in `values`, by packing them along the `axis` dimension. -// Given a list of tensors of shape `(A, B, C)`; -// -// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. -// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. -// Etc. -// -// For example: -// -// ``` -// # 'x' is [1, 4] -// # 'y' is [2, 5] -// # 'z' is [3, 6] -// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. -// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] -// ``` -// -// This is the opposite of `unpack`. -// -// Arguments: -// values: Must be of same shape and type. -// -// Returns The packed tensor. -func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Pack", - Input: []tf.Input{ - tf.OutputList(values), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU. -type ConfigureDistributedTPUAttr func(optionalAttr) - -// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value. -// -// value: Reserved. Do not use. -// If not specified, defaults to "" -func ConfigureDistributedTPUEmbeddingConfig(value string) ConfigureDistributedTPUAttr { - return func(m optionalAttr) { - m["embedding_config"] = value - } -} - -// ConfigureDistributedTPUTpuEmbeddingConfig sets the optional tpu_embedding_config attribute to value. -// -// value: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that -// describes the embedding lookups of the program. -// If not specified, defaults to "" -func ConfigureDistributedTPUTpuEmbeddingConfig(value string) ConfigureDistributedTPUAttr { - return func(m optionalAttr) { - m["tpu_embedding_config"] = value - } -} - -// ConfigureDistributedTPUIsGlobalInit sets the optional is_global_init attribute to value. -// -// value: Reserved. Do not use. -// If not specified, defaults to false -func ConfigureDistributedTPUIsGlobalInit(value bool) ConfigureDistributedTPUAttr { - return func(m optionalAttr) { - m["is_global_init"] = value - } -} - -// Sets up the centralized structures for a distributed TPU system. -// -// Returns A serialized tensorflow.tpu.TopologyProto that describes the TPU -// topology. -func ConfigureDistributedTPU(scope *Scope, optional ...ConfigureDistributedTPUAttr) (topology tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ConfigureDistributedTPU", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns x * y element-wise. -// -// *NOTE*: `Multiply` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Mul", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Check if the input matches the regex pattern. -// -// The input is a string tensor of any shape. The pattern is a scalar -// string tensor which is applied to every element of the input tensor. -// The boolean values (True or False) of the output tensor indicate -// if the input matches the regex pattern provided. -// -// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) -// -// Arguments: -// input: A string tensor of the text to be processed. -// pattern: A scalar string tensor containing the regular expression to match the input. -// -// Returns A bool tensor with the same shape as `input`. -func RegexFullMatch(scope *Scope, input tf.Output, pattern tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RegexFullMatch", - Input: []tf.Input{ - input, pattern, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Checks a tensor for NaN and Inf values. -// -// When run, reports an `InvalidArgument` error if `tensor` has any values -// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is. -// -// Arguments: -// -// message: Prefix of the error message. -func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"message": message} - opspec := tf.OpSpec{ - Type: "CheckNumerics", - Input: []tf.Input{ - tensor, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Outputs all keys and values in the table. -// -// Arguments: -// table_handle: Handle to the table. -// -// -// -// Returns Vector of all keys present in the table.Tensor of all values in the table. Indexed in parallel with `keys`. -func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType, Tvalues tf.DataType) (keys tf.Output, values tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"Tkeys": Tkeys, "Tvalues": Tvalues} - opspec := tf.OpSpec{ - Type: "LookupTableExportV2", - Input: []tf.Input{ - table_handle, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// L2 Loss. -// -// Computes half the L2 norm of a tensor without the `sqrt`: -// -// output = sum(t ** 2) / 2 -// -// Arguments: -// t: Typically 2-D, but may have any dimensions. -// -// Returns 0-D. -func L2Loss(scope *Scope, t tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "L2Loss", - Input: []tf.Input{ - t, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Generates sparse cross from a list of sparse and dense tensors. -// -// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each -// representing features of one feature column. It outputs a 2D `SparseTensor` with -// the batchwise crosses of these features. -// -// For example, if the inputs are -// -// inputs[0]: SparseTensor with shape = [2, 2] -// [0, 0]: "a" -// [1, 0]: "b" -// [1, 1]: "c" -// -// inputs[1]: SparseTensor with shape = [2, 1] -// [0, 0]: "d" -// [1, 0]: "e" -// -// inputs[2]: Tensor [["f"], ["g"]] -// -// then the output will be -// -// shape = [2, 2] -// [0, 0]: "a_X_d_X_f" -// [1, 0]: "b_X_e_X_g" -// [1, 1]: "c_X_e_X_g" -// -// if hashed_output=true then the output will be -// -// shape = [2, 2] -// [0, 0]: FingerprintCat64( -// Fingerprint64("f"), FingerprintCat64( -// Fingerprint64("d"), Fingerprint64("a"))) -// [1, 0]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("b"))) -// [1, 1]: FingerprintCat64( -// Fingerprint64("g"), FingerprintCat64( -// Fingerprint64("e"), Fingerprint64("c"))) -// -// Arguments: -// indices: 2-D. Indices of each input `SparseTensor`. -// values: 1-D. values of each `SparseTensor`. -// shapes: 1-D. Shapes of each `SparseTensor`. -// dense_inputs: 2-D. Columns represented by dense `Tensor`. -// hashed_output: If true, returns the hash of the cross instead of the string. -// This will allow us avoiding string manipulations. -// num_buckets: It is used if hashed_output is true. -// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. -// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` -// function to combine the crosses fingerprints. -// -// -// -// Returns 2-D. Indices of the concatenated `SparseTensor`.1-D. Non-empty values of the concatenated or hashed -// `SparseTensor`.1-D. Shape of the concatenated `SparseTensor`. -func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} - opspec := tf.OpSpec{ - Type: "SparseCross", - Input: []tf.Input{ - tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Applies sparse addition to `input` using individual values or slices -// -// from `updates` according to indices `indices`. The updates are non-aliasing: -// `input` is only modified in-place if no other operations will use it. -// Otherwise, a copy of `input` is made. This operation has a gradient with -// respect to both `input` and `updates`. -// -// `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. -// -// `indices` must be integer tensor, containing indices into `input`. -// It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`. -// -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or `(P-K)`-dimensional slices -// (if `K < P`) along the `K`th dimension of `input`. -// -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: -// -// $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$ -// -// For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 -// elements. In Python, that addition would look like this: -// -// input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8]) -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// output = tf.scatter_nd_non_aliasing_add(input, indices, updates) -// with tf.Session() as sess: -// print(sess.run(output)) -// -// The resulting value `output` would look like this: -// -// [1, 13, 3, 14, 14, 6, 7, 20] -// -// See `tf.scatter_nd` for more details about how to make updates to slices. -// -// Arguments: -// input: A Tensor. -// indices: A Tensor. Must be one of the following types: `int32`, `int64`. -// A tensor of indices into `input`. -// updates: A Tensor. Must have the same type as ref. A tensor of updated values -// to add to `input`. -// -// Returns A `Tensor` with the same shape as `input`, containing values of `input` -// updated with `updates`. -func ScatterNdNonAliasingAdd(scope *Scope, input tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ScatterNdNonAliasingAdd", - Input: []tf.Input{ - input, indices, updates, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StringSplitV2Attr is an optional argument to StringSplitV2. -type StringSplitV2Attr func(optionalAttr) - -// StringSplitV2Maxsplit sets the optional maxsplit attribute to value. -// -// value: An `int`. If `maxsplit > 0`, limit of the split of the result. -// If not specified, defaults to -1 -func StringSplitV2Maxsplit(value int64) StringSplitV2Attr { - return func(m optionalAttr) { - m["maxsplit"] = value - } -} - -// Split elements of `source` based on `sep` into a `SparseTensor`. -// -// Let N be the size of source (typically N will be the batch size). Split each -// element of `source` based on `sep` and return a `SparseTensor` -// containing the split tokens. Empty tokens are ignored. -// -// For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', -// then the output will be -// ``` -// st.indices = [0, 0; -// 0, 1; -// 1, 0; -// 1, 1; -// 1, 2] -// st.shape = [2, 3] -// st.values = ['hello', 'world', 'a', 'b', 'c'] -// ``` -// -// If `sep` is given, consecutive delimiters are not grouped together and are -// deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and -// sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty -// string, consecutive whitespace are regarded as a single separator, and the -// result will contain no empty strings at the startor end if the string has -// leading or trailing whitespace. -// -// Note that the above mentioned behavior matches python's str.split. -// -// Arguments: -// input: `1-D` string `Tensor`, the strings to split. -// sep: `0-D` string `Tensor`, the delimiter character. -func StringSplitV2(scope *Scope, input tf.Output, sep tf.Output, optional ...StringSplitV2Attr) (indices tf.Output, values tf.Output, shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StringSplitV2", - Input: []tf.Input{ - input, sep, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Saves input tensors slices to disk. -// -// This is like `Save` except that tensors can be listed in the saved file as being -// a slice of a larger tensor. `shapes_and_slices` specifies the shape of the -// larger tensor and the slice that this tensor covers. `shapes_and_slices` must -// have as many elements as `tensor_names`. -// -// Elements of the `shapes_and_slices` input must either be: -// -// * The empty string, in which case the corresponding tensor is -// saved normally. -// * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the -// `dimI` are the dimensions of the larger tensor and `slice-spec` -// specifies what part is covered by the tensor to save. -// -// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1` -// where each `sliceI` is either: -// -// * The string `-` meaning that the slice covers all indices of this dimension -// * `start,length` where `start` and `length` are integers. In that -// case the slice covers `length` indices starting at `start`. -// -// See also `Save`. -// -// Arguments: -// filename: Must have a single element. The name of the file to which we write the -// tensor. -// tensor_names: Shape `[N]`. The names of the tensors to be saved. -// shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when -// saving the tensors. -// data: `N` tensors to save. -// -// Returns the created operation. -func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SaveSlices", - Input: []tf.Input{ - filename, tensor_names, shapes_and_slices, tf.OutputList(data), - }, - } - return scope.AddOperation(opspec) -} - -// Computes gradients for SparseSegmentMean. -// -// Returns tensor "output" with same shape as grad, except for dimension 0 whose -// value is output_dim0. -// -// Arguments: -// grad: gradient propagated to the SparseSegmentMean op. -// indices: indices passed to the corresponding SparseSegmentMean op. -// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. -// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. -func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentMeanGrad", - Input: []tf.Input{ - grad, indices, segment_ids, output_dim0, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Check if the input matches the regex pattern. -// -// The input is a string tensor of any shape. The pattern is the -// regular expression to be matched with every element of the input tensor. -// The boolean values (True or False) of the output tensor indicate -// if the input matches the regex pattern provided. -// -// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) -// -// Arguments: -// input: A string tensor of the text to be processed. -// pattern: The regular expression to match the input. -// -// Returns A bool tensor with the same shape as `input`. -func StaticRegexFullMatch(scope *Scope, input tf.Output, pattern string) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"pattern": pattern} - opspec := tf.OpSpec{ - Type: "StaticRegexFullMatch", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceScatterNdSubAttr is an optional argument to ResourceScatterNdSub. -type ResourceScatterNdSubAttr func(optionalAttr) - -// ResourceScatterNdSubUseLocking sets the optional use_locking attribute to value. -// -// value: An optional bool. Defaults to True. If True, the assignment will -// be protected by a lock; otherwise the behavior is undefined, -// but may exhibit less contention. -// If not specified, defaults to true -func ResourceScatterNdSubUseLocking(value bool) ResourceScatterNdSubAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Applies sparse subtraction to individual values or slices in a Variable. -// -// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. -// -// `indices` must be integer tensor, containing indices into `ref`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. -// -// The innermost dimension of `indices` (with length `K`) corresponds to -// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th -// dimension of `ref`. -// -// `updates` is `Tensor` of rank `Q-1+P-K` with shape: -// -// ``` -// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] -// ``` -// -// For example, say we want to subtract 4 scattered elements from a rank-1 tensor -// with 8 elements. In Python, that subtraction would look like this: -// -// ```python -// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) -// indices = tf.constant([[4], [3], [1], [7]]) -// updates = tf.constant([9, 10, 11, 12]) -// sub = tf.scatter_nd_sub(ref, indices, updates) -// with tf.Session() as sess: -// print sess.run(sub) -// ``` -// -// The resulting update to ref would look like this: -// -// [1, -9, 3, -6, -4, 6, 7, -4] -// -// See `tf.scatter_nd` for more details about how to make updates to -// slices. -// -// Arguments: -// ref: A resource handle. Must be from a VarHandleOp. -// indices: A Tensor. Must be one of the following types: int32, int64. -// A tensor of indices into ref. -// updates: A Tensor. Must have the same type as ref. A tensor of -// values to add to ref. -// -// Returns the created operation. -func ResourceScatterNdSub(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdSubAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceScatterNdSub", - Input: []tf.Input{ - ref, indices, updates, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. -type QuantizedRelu6Attr func(optionalAttr) - -// QuantizedRelu6OutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_QUINT8 -func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` -// -// Arguments: -// -// min_features: The float value that the lowest quantized value represents. -// max_features: The float value that the highest quantized value represents. -// -// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. -func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedRelu6", - Input: []tf.Input{ - features, min_features, max_features, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Strip leading and trailing whitespaces from the Tensor. -// -// Arguments: -// input: A string `Tensor` of any shape. -// -// Returns A string `Tensor` of the same shape as the input. -func StringStrip(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "StringStrip", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Records the latency of producing `input_dataset` elements in a StatsAggregator. -func ExperimentalLatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalLatencyStatsDataset", - Input: []tf.Input{ - input_dataset, tag, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UnpackAttr is an optional argument to Unpack. -type UnpackAttr func(optionalAttr) - -// UnpackAxis sets the optional axis attribute to value. -// -// value: Dimension along which to unpack. Negative values wrap around, so the -// valid range is `[-R, R)`. -// If not specified, defaults to 0 -func UnpackAxis(value int64) UnpackAttr { - return func(m optionalAttr) { - m["axis"] = value - } -} - -// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. -// -// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. -// For example, given a tensor of shape `(A, B, C, D)`; -// -// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` -// and each tensor in `output` will have shape `(B, C, D)`. (Note that the -// dimension unpacked along is gone, unlike `split`). -// -// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` -// and each tensor in `output` will have shape `(A, C, D)`. -// Etc. -// -// This is the opposite of `pack`. -// -// Arguments: -// value: 1-D or higher, with `axis` dimension size equal to `num`. -// -// -// Returns The list of tensors unpacked from `value`. -func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num": num} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Unpack", - Input: []tf.Input{ - value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("Unpack", err) - return - } - return output -} - -// ResourceSparseApplyKerasMomentumAttr is an optional argument to ResourceSparseApplyKerasMomentum. -type ResourceSparseApplyKerasMomentumAttr func(optionalAttr) - -// ResourceSparseApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyKerasMomentumUseLocking(value bool) ResourceSparseApplyKerasMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceSparseApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. -// -// value: If `True`, the tensor passed to compute grad will be -// var + momentum * accum, so in the end, the var you get is actually -// var + momentum * accum. -// If not specified, defaults to false -func ResourceSparseApplyKerasMomentumUseNesterov(value bool) ResourceSparseApplyKerasMomentumAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update relevant entries in '*var' and '*accum' according to the momentum scheme. -// -// Set use_nesterov = True if you want to use Nesterov momentum. -// -// That is for rows we have grad for, we update var and accum as follows: -// -// accum = accum * momentum - lr * grad -// var += accum -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Learning rate. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var and accum. -// momentum: Momentum. Must be a scalar. -// -// Returns the created operation. -func ResourceSparseApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyKerasMomentumAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyKerasMomentum", - Input: []tf.Input{ - var_, accum, lr, grad, indices, momentum, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Inverse 2D real-valued fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most 2 dimensions of `input`. -// -// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`: -// The inner-most dimension contains the `fft_length / 2 + 1` unique components of -// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed -// from the size of the inner-most 2 dimensions of `input`. If the FFT length used -// to compute `input` is odd, it should be provided since it cannot be inferred -// properly. -// -// Along each axis `IRFFT2D` is computed on, if `fft_length` (or -// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most 2 -// dimensions of `input` are replaced with the `fft_length` samples of their -// inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.irfft2 -// @end_compatibility -func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IRFFT2D", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FusedBatchNormGradV2Attr is an optional argument to FusedBatchNormGradV2. -type FusedBatchNormGradV2Attr func(optionalAttr) - -// FusedBatchNormGradV2Epsilon sets the optional epsilon attribute to value. -// -// value: A small float number added to the variance of x. -// If not specified, defaults to 0.0001 -func FusedBatchNormGradV2Epsilon(value float32) FusedBatchNormGradV2Attr { - return func(m optionalAttr) { - m["epsilon"] = value - } -} - -// FusedBatchNormGradV2DataFormat sets the optional data_format attribute to value. -// -// value: The data format for y_backprop, x, x_backprop. -// Either "NHWC" (default) or "NCHW". -// If not specified, defaults to "NHWC" -func FusedBatchNormGradV2DataFormat(value string) FusedBatchNormGradV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// FusedBatchNormGradV2IsTraining sets the optional is_training attribute to value. -// -// value: A bool value to indicate the operation is for training (default) -// or inference. -// If not specified, defaults to true -func FusedBatchNormGradV2IsTraining(value bool) FusedBatchNormGradV2Attr { - return func(m optionalAttr) { - m["is_training"] = value - } -} - -// Gradient for batch normalization. -// -// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". -// The size of 1D Tensors matches the dimension C of the 4D Tensors. -// -// Arguments: -// y_backprop: A 4D Tensor for the gradient with respect to y. -// x: A 4D Tensor for input data. -// scale: A 1D Tensor for scaling factor, to scale the normalized x. -// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch -// mean to be reused in gradient computation. When is_training is -// False, a 1D Tensor for the population mean to be reused in both -// 1st and 2nd order gradient computation. -// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch -// variance (inverted variance in the cuDNN case) to be reused in -// gradient computation. When is_training is False, a 1D Tensor -// for the population variance to be reused in both 1st and 2nd -// order gradient computation. -// -// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input -// in FusedBatchNorm. -func FusedBatchNormGradV2(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradV2Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FusedBatchNormGradV2", - Input: []tf.Input{ - y_backprop, x, scale, reserve_space_1, reserve_space_2, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) -} - -// Decode web-safe base64-encoded strings. -// -// Input may or may not have padding at the end. See EncodeBase64 for padding. -// Web-safe means that input must use - and _ instead of + and /. -// -// Arguments: -// input: Base64 strings to decode. -// -// Returns Decoded strings. -func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "DecodeBase64", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StringFormatAttr is an optional argument to StringFormat. -type StringFormatAttr func(optionalAttr) - -// StringFormatTemplate sets the optional template attribute to value. -// -// value: A string, the template to format tensor summaries into. -// If not specified, defaults to "%s" -func StringFormatTemplate(value string) StringFormatAttr { - return func(m optionalAttr) { - m["template"] = value - } -} - -// StringFormatPlaceholder sets the optional placeholder attribute to value. -// -// value: A string, at each placeholder in the template a subsequent tensor summary will be inserted. -// If not specified, defaults to "%s" -func StringFormatPlaceholder(value string) StringFormatAttr { - return func(m optionalAttr) { - m["placeholder"] = value - } -} - -// StringFormatSummarize sets the optional summarize attribute to value. -// -// value: When formatting the tensor summaries print the first and last summarize entries of each tensor dimension. -// If not specified, defaults to 3 -func StringFormatSummarize(value int64) StringFormatAttr { - return func(m optionalAttr) { - m["summarize"] = value - } -} - -// Formats a string template using a list of tensors. -// -// Formats a string template using a list of tensors, pretty-printing tensor summaries. -// -// Arguments: -// inputs: The list of tensors to format into the placeholder string. -// -// Returns = The resulting string scalar. -func StringFormat(scope *Scope, inputs []tf.Output, optional ...StringFormatAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StringFormat", - Input: []tf.Input{ - tf.OutputList(inputs), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. -type StatelessRandomNormalAttr func(optionalAttr) - -// StatelessRandomNormalDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs deterministic pseudorandom values from a normal distribution. -// -// The generated values will have mean 0 and standard deviation 1. -// -// The outputs are a deterministic function of `shape` and `seed`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). -// -// Returns Random values with specified shape. -func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatelessRandomNormal", - Input: []tf.Input{ - shape, seed, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Counts the number of occurrences of each value in an integer array. -// -// Outputs a vector with length `size` and the same dtype as `weights`. If -// `weights` are empty, then index `i` stores the number of times the value `i` is -// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of -// the value in `weights` at each index where the corresponding value in `arr` is -// `i`. -// -// Values in `arr` outside of the range [0, size) are ignored. -// -// Arguments: -// arr: int32 `Tensor`. -// size: non-negative int32 scalar `Tensor`. -// weights: is an int32, int64, float32, or float64 `Tensor` with the same -// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights -// equal to 1. -// -// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for -// each value in the range [0, size). -func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Bincount", - Input: []tf.Input{ - arr, size, weights, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxAttr is an optional argument to Max. -type MaxAttr func(optionalAttr) - -// MaxKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func MaxKeepDims(value bool) MaxAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the maximum of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Max(scope *Scope, input tf.Output, axis tf.Output, optional ...MaxAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Max", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns a batched diagonal tensor with a given batched diagonal values. -// -// Given a `diagonal`, this operation returns a tensor with the `diagonal` and -// everything else padded with zeros. The diagonal is computed as follows: -// -// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a -// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: -// -// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`. -// -// For example: -// -// ``` -// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] -// -// and diagonal.shape = (2, 4) -// -// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0] -// [0, 2, 0, 0] -// [0, 0, 3, 0] -// [0, 0, 0, 4]], -// [[5, 0, 0, 0] -// [0, 6, 0, 0] -// [0, 0, 7, 0] -// [0, 0, 0, 8]]] -// -// which has shape (2, 4, 4) -// ``` -// -// Arguments: -// diagonal: Rank `k`, where `k >= 1`. -// -// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`. -func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixDiag", - Input: []tf.Input{ - diagonal, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// This op is used as a placeholder in If branch functions. It doesn't provide a -// valid output when run, so must either be removed (e.g. replaced with a -// function input) or guaranteed not to be used (e.g. if mirroring an -// intermediate output needed for the gradient computation of the other branch). -// -// Arguments: -// dtype: The type of the output. -// shape: The purported shape of the output. This is only used for shape inference; -// the output will not necessarily have this shape. Can be a partial shape. -// -// Returns \"Fake\" output value. This should not be consumed by another op. -func FakeParam(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtype": dtype, "shape": shape} - opspec := tf.OpSpec{ - Type: "FakeParam", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes gradients for the scaled exponential linear (Selu) operation. -// -// Arguments: -// gradients: The backpropagated gradients to the corresponding Selu operation. -// outputs: The outputs of the corresponding Selu operation. -// -// Returns The gradients: `gradients * (outputs + scale * alpha)` -// if outputs < 0, `scale * gradients` otherwise. -func SeluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SeluGrad", - Input: []tf.Input{ - gradients, outputs, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes Psi, the derivative of Lgamma (the log of the absolute value of -// -// `Gamma(x)`), element-wise. -func Digamma(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Digamma", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Copy a tensor setting everything outside a central band in each innermost matrix -// -// to zero. -// -// The `band` part is computed as follows: -// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a -// tensor with the same shape where -// -// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. -// -// The indicator function -// -// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && -// (num_upper < 0 || (n-m) <= num_upper)`. -// -// For example: -// -// ``` -// # if 'input' is [[ 0, 1, 2, 3] -// [-1, 0, 1, 2] -// [-2, -1, 0, 1] -// [-3, -2, -1, 0]], -// -// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] -// [-1, 0, 1, 2] -// [ 0, -1, 0, 1] -// [ 0, 0, -1, 0]], -// -// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] -// [-1, 0, 1, 0] -// [-2, -1, 0, 1] -// [ 0, -2, -1, 0]] -// ``` -// -// Useful special cases: -// -// ``` -// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. -// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. -// tf.matrix_band_part(input, 0, 0) ==> Diagonal. -// ``` -// -// Arguments: -// input: Rank `k` tensor. -// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire -// lower triangle. -// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep -// entire upper triangle. -// -// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor. -func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixBandPart", - Input: []tf.Input{ - input, num_lower, num_upper, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the batched diagonal part of a batched tensor. -// -// This operation returns a tensor with the `diagonal` part -// of the batched `input`. The `diagonal` part is computed as follows: -// -// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a -// tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where: -// -// `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`. -// -// The input must be at least a matrix. -// -// For example: -// -// ``` -// # 'input' is [[[1, 0, 0, 0] -// [0, 2, 0, 0] -// [0, 0, 3, 0] -// [0, 0, 0, 4]], -// [[5, 0, 0, 0] -// [0, 6, 0, 0] -// [0, 0, 7, 0] -// [0, 0, 0, 8]]] -// -// and input.shape = (2, 4, 4) -// -// tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]] -// -// which has shape (2, 4) -// ``` -// -// Arguments: -// input: Rank `k` tensor where `k >= 2`. -// -// Returns The extracted diagonal(s) having shape -// `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`. -func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixDiagPart", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad. -type FractionalAvgPoolGradAttr func(optionalAttr) - -// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value. -// -// value: When set to True, it means when pooling, the values at the boundary -// of adjacent pooling cells are used by both cells. For example: -// -// `index 0 1 2 3 4` -// -// `value 20 5 16 3 7` -// -// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. -// The result would be [41/3, 26/3] for fractional avg pooling. -// If not specified, defaults to false -func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr { - return func(m optionalAttr) { - m["overlapping"] = value - } -} - -// Computes gradient of the FractionalAvgPool function. -// -// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for -// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of -// out_backprop to those indices that form the same pooling cell. Therefore, we -// just need to know the shape of original input tensor, instead of the whole -// tensor. -// -// Arguments: -// orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool` -// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients -// w.r.t. the output of `fractional_avg_pool`. -// row_pooling_sequence: row pooling sequence, form pooling region with -// col_pooling_sequence. -// col_pooling_sequence: column pooling sequence, form pooling region with -// row_pooling sequence. -// -// Returns 4-D. Gradients w.r.t. the input of `fractional_avg_pool`. -func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FractionalAvgPoolGrad", - Input: []tf.Input{ - orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. -type MaxPoolWithArgmaxAttr func(optionalAttr) - -// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. -// If not specified, defaults to DT_INT64 -func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { - return func(m optionalAttr) { - m["Targmax"] = value - } -} - -// MaxPoolWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. -// -// value: Whether to include batch dimension in flattened index of `argmax`. -// If not specified, defaults to false -func MaxPoolWithArgmaxIncludeBatchInIndex(value bool) MaxPoolWithArgmaxAttr { - return func(m optionalAttr) { - m["include_batch_in_index"] = value - } -} - -// Performs max pooling on the input and outputs both max values and indices. -// -// The indices in `argmax` are flattened, so that a maximum value at position -// `[b, y, x, c]` becomes flattened index: -// `(y * width + x) * channels + c` if `include_batch_in_index` is False; -// `((b * height + y) * width + x) * channels + c` if `include_batch_in_index` is True. -// -// The indices returned are always in `[0, height) x [0, width)` before flattening, -// even if padding is involved and the mathematically correct answer is outside -// (either negative or too large). This is a bug, but fixing it is difficult to do -// in a safe backwards compatible way, especially due to flattening. -// -// Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor.4-D. The flattened indices of the max values chosen for each output. -func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolWithArgmax", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// StatefulUniformAttr is an optional argument to StatefulUniform. -type StatefulUniformAttr func(optionalAttr) - -// StatefulUniformDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatefulUniformDtype(value tf.DataType) StatefulUniformAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random values from a uniform distribution. -// -// The generated values follow a uniform distribution in the range `[0, 1)`. The -// lower bound 0 is included in the range, while the upper bound 1 is excluded. -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// shape: The shape of the output tensor. -// -// Returns Random values with specified shape. -func StatefulUniform(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatefulUniform", - Input: []tf.Input{ - resource, algorithm, shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 2D real-valued fast Fourier transform. -// -// Computes the 2-dimensional discrete Fourier transform of a real-valued signal -// over the inner-most 2 dimensions of `input`. -// -// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the -// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension -// of `output`: the zero-frequency term, followed by the `fft_length / 2` -// positive-frequency terms. -// -// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A float32 tensor. -// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. -// -// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 -// dimensions of `input` are replaced with their 2D Fourier transform. The -// inner-most dimension contains `fft_length / 2 + 1` unique frequency -// components. -// -// @compatibility(numpy) -// Equivalent to np.fft.rfft2 -// @end_compatibility -func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RFFT2D", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Does nothing. Only useful as a placeholder for control edges. -// -// Returns the created operation. -func NoOp(scope *Scope) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NoOp", - } - return scope.AddOperation(opspec) -} - -// ArgMinAttr is an optional argument to ArgMin. -type ArgMinAttr func(optionalAttr) - -// ArgMinOutputType sets the optional output_type attribute to value. -// If not specified, defaults to DT_INT64 -func ArgMinOutputType(value tf.DataType) ArgMinAttr { - return func(m optionalAttr) { - m["output_type"] = value - } -} - -// Returns the index with the smallest value across dimensions of a tensor. -// -// Note that in case of ties the identity of the return value is not guaranteed. -// -// Usage: -// ```python -// import tensorflow as tf -// a = [1, 10, 26.9, 2.8, 166.32, 62.3] -// b = tf.math.argmin(input = a) -// c = tf.keras.backend.eval(b) -// # c = 0 -// # here a[0] = 1 which is the smallest element of a across axis 0 -// ``` -// -// Arguments: -// -// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. -// Describes which dimension of the input Tensor to reduce across. For vectors, -// use dimension = 0. -func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ArgMin", - Input: []tf.Input{ - input, dimension, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse 2D fast Fourier transform. -// -// Computes the inverse 2-dimensional discrete Fourier transform over the -// inner-most 2 dimensions of `input`. -// -// Arguments: -// input: A complex tensor. -// -// Returns A complex tensor of the same shape as `input`. The inner-most 2 -// dimensions of `input` are replaced with their inverse 2D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft2 -// @end_compatibility -func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT2D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// AvgPoolGradAttr is an optional argument to AvgPoolGrad. -type AvgPoolGradAttr func(optionalAttr) - -// AvgPoolGradDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of the average pooling function. -// -// Arguments: -// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. -// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. -// the output of `avg_pool`. -// ksize: The size of the sliding window for each dimension of the input. -// strides: The stride of the sliding window for each dimension of the input. -// padding: The type of padding algorithm to use. -// -// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. -func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AvgPoolGrad", - Input: []tf.Input{ - orig_input_shape, grad, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// 3D fast Fourier transform. -// -// Computes the 3-dimensional discrete Fourier transform over the inner-most 3 -// dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 3 -// dimensions of `input` are replaced with their 3D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.fftn with 3 dimensions. -// @end_compatibility -func FFT3D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FFT3D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedDepthwiseConv2DWithBiasAndReluAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndRelu. -type QuantizedDepthwiseConv2DWithBiasAndReluAttr func(optionalAttr) - -// QuantizedDepthwiseConv2DWithBiasAndReluOutType sets the optional out_type attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_QINT32 -func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value. -// -// value: List of dilation values. -// If not specified, defaults to -func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes quantized depthwise Conv2D with Bias and Relu. -// -// Arguments: -// input: The original input tensor. -// filter: The original filter tensor. -// bias: The original bias tensor. -// min_input: The float value that the minimum quantized input value represents. -// max_input: The float value that the maximum quantized input value represents. -// min_filter: The float value that the minimum quantized filter value represents. -// max_filter: The float value that the maximum quantized filter value represents. -// strides: List of stride values. -// -// -// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedDepthwiseConv2DWithBiasAndRelu(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedDepthwiseConv2DWithBiasAndRelu", - Input: []tf.Input{ - input, filter, bias, min_input, max_input, min_filter, max_filter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2. -type MaxPoolGradV2Attr func(optionalAttr) - -// MaxPoolGradV2DataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Computes gradients of the maxpooling function. -// -// Arguments: -// orig_input: The original input tensor. -// orig_output: The original output tensor. -// grad: 4-D. Gradients w.r.t. the output of `max_pool`. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns Gradients w.r.t. the input to `max_pool`. -func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPoolGradV2", - Input: []tf.Input{ - orig_input, orig_output, grad, ksize, strides, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse 3D real-valued fast Fourier transform. -// -// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued -// signal over the inner-most 3 dimensions of `input`. -// -// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: -// The inner-most dimension contains the `fft_length / 2 + 1` unique components of -// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed -// from the size of the inner-most 3 dimensions of `input`. If the FFT length used -// to compute `input` is odd, it should be provided since it cannot be inferred -// properly. -// -// Along each axis `IRFFT3D` is computed on, if `fft_length` (or -// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A complex64 tensor. -// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. -// -// Returns A float32 tensor of the same rank as `input`. The inner-most 3 -// dimensions of `input` are replaced with the `fft_length` samples of their -// inverse 3D real Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.irfftn with 3 dimensions. -// @end_compatibility -func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IRFFT3D", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// QuantizedMatMulAttr is an optional argument to QuantizedMatMul. -type QuantizedMatMulAttr func(optionalAttr) - -// QuantizedMatMulToutput sets the optional Toutput attribute to value. -// If not specified, defaults to DT_QINT32 -func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr { - return func(m optionalAttr) { - m["Toutput"] = value - } -} - -// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value. -// -// value: If true, `a` is transposed before multiplication. -// If not specified, defaults to false -func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value. -// -// value: If true, `b` is transposed before multiplication. -// If not specified, defaults to false -func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// QuantizedMatMulTactivation sets the optional Tactivation attribute to value. -// -// value: The type of output produced by activation function -// following this operation. -// If not specified, defaults to DT_QUINT8 -func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr { - return func(m optionalAttr) { - m["Tactivation"] = value - } -} - -// Perform a quantized matrix multiplication of `a` by the matrix `b`. -// -// The inputs must be two-dimensional matrices and the inner dimension of -// `a` (after being transposed if `transpose_a` is non-zero) must match the -// outer dimension of `b` (after being transposed if `transposed_b` is -// non-zero). -// -// Arguments: -// a: Must be a two-dimensional tensor. -// b: Must be a two-dimensional tensor. -// min_a: The float value that the lowest quantized `a` value represents. -// max_a: The float value that the highest quantized `a` value represents. -// min_b: The float value that the lowest quantized `b` value represents. -// max_b: The float value that the highest quantized `b` value represents. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedMatMul", - Input: []tf.Input{ - a, b, min_a, max_a, min_b, max_b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// BatchMatMulV2Attr is an optional argument to BatchMatMulV2. -type BatchMatMulV2Attr func(optionalAttr) - -// BatchMatMulV2AdjX sets the optional adj_x attribute to value. -// -// value: If `True`, adjoint the slices of `x`. Defaults to `False`. -// If not specified, defaults to false -func BatchMatMulV2AdjX(value bool) BatchMatMulV2Attr { - return func(m optionalAttr) { - m["adj_x"] = value - } -} - -// BatchMatMulV2AdjY sets the optional adj_y attribute to value. -// -// value: If `True`, adjoint the slices of `y`. Defaults to `False`. -// If not specified, defaults to false -func BatchMatMulV2AdjY(value bool) BatchMatMulV2Attr { - return func(m optionalAttr) { - m["adj_y"] = value - } -} - -// Multiplies slices of two tensors in batches. -// -// Multiplies all slices of `Tensor` `x` and `y` (each slice can be -// viewed as an element of a batch), and arranges the individual results -// in a single output tensor of the same batch size. Each of the -// individual slices can optionally be adjointed (to adjoint a matrix -// means to transpose and conjugate it) before multiplication by setting -// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. -// -// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` -// and `[..., r_y, c_y]`. -// -// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: -// -// r_o = c_x if adj_x else r_x -// c_o = r_y if adj_y else c_y -// -// It is computed as: -// -// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -// -// *NOTE*: `BatchMatMulV2` supports broadcasting in the batch dimensions. More -// about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). -// -// -// Arguments: -// x: 2-D or higher with shape `[..., r_x, c_x]`. -// y: 2-D or higher with shape `[..., r_y, c_y]`. -// -// Returns 3-D or higher with shape `[..., r_o, c_o]` -func BatchMatMulV2(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BatchMatMulV2", - Input: []tf.Input{ - x, y, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse fast Fourier transform. -// -// Computes the inverse 1-dimensional discrete Fourier transform over the -// inner-most dimension of `input`. -// -// Arguments: -// input: A complex tensor. -// -// Returns A complex tensor of the same shape as `input`. The inner-most -// dimension of `input` is replaced with its inverse 1D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifft -// @end_compatibility -func IFFT(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns x + y element-wise. -// -// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Add", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingFTRLParametersGradAccumDebug. -type RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve FTRL embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the FTRL optimization algorithm.Parameter accumulators updated by the FTRL optimization algorithm.Parameter linears updated by the FTRL optimization algorithm.Parameter gradient_accumulators updated by the FTRL optimization algorithm. -func RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// SdcaOptimizerV2Attr is an optional argument to SdcaOptimizerV2. -type SdcaOptimizerV2Attr func(optionalAttr) - -// SdcaOptimizerV2Adaptive sets the optional adaptive attribute to value. -// -// value: Whether to use Adaptive SDCA for the inner loop. -// If not specified, defaults to true -func SdcaOptimizerV2Adaptive(value bool) SdcaOptimizerV2Attr { - return func(m optionalAttr) { - m["adaptive"] = value - } -} - -// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for -// -// linear models with L1 + L2 regularization. As global optimization objective is -// strongly-convex, the optimizer optimizes the dual objective at each step. The -// optimizer applies each update one example at a time. Examples are sampled -// uniformly, and the optimizer is learning rate free and enjoys linear convergence -// rate. -// -// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
-// Shai Shalev-Shwartz, Tong Zhang. 2012 -// -// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ -// -// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
-// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, -// Peter Richtarik, Martin Takac. 2015 -// -// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
-// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 -// -// Arguments: -// sparse_example_indices: a list of vectors which contain example indices. -// sparse_feature_indices: a list of vectors which contain feature indices. -// sparse_feature_values: a list of vectors which contains feature value -// associated with each feature group. -// dense_features: a list of matrices which contains the dense feature values. -// example_weights: a vector which contains the weight associated with each -// example. -// example_labels: a vector which contains the label/target associated with each -// example. -// sparse_indices: a list of vectors where each value is the indices which has -// corresponding weights in sparse_weights. This field maybe omitted for the -// dense approach. -// sparse_weights: a list of vectors where each value is the weight associated with -// a sparse feature group. -// dense_weights: a list of vectors where the values are the weights associated -// with a dense feature group. -// example_state_data: a list of vectors containing the example state data. -// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, -// squared and hinge losses. -// l1: Symmetric l1 regularization strength. -// l2: Symmetric l2 regularization strength. -// num_loss_partitions: Number of partitions of the global loss function. -// num_inner_iterations: Number of iterations per mini-batch. -// -// Returns a list of vectors containing the updated example state -// data.a list of vectors where each value is the delta -// weights associated with a sparse feature group.a list of vectors where the values are the delta -// weights associated with a dense feature group. -func SdcaOptimizerV2(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerV2Attr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SdcaOptimizerV2", - Input: []tf.Input{ - tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - out_example_state_data = op.Output(idx) - if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { - scope.UpdateErr("SdcaOptimizerV2", err) - return - } - if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { - scope.UpdateErr("SdcaOptimizerV2", err) - return - } - return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights -} - -// TopKAttr is an optional argument to TopK. -type TopKAttr func(optionalAttr) - -// TopKSorted sets the optional sorted attribute to value. -// -// value: If true the resulting `k` elements will be sorted by the values in -// descending order. -// If not specified, defaults to true -func TopKSorted(value bool) TopKAttr { - return func(m optionalAttr) { - m["sorted"] = value - } -} - -// Finds values and indices of the `k` largest elements for the last dimension. -// -// DEPRECATED at GraphDef version 7: Use TopKV2 instead -// -// If the input is a vector (rank-1), finds the `k` largest entries in the vector -// and outputs their values and indices as vectors. Thus `values[j]` is the -// `j`-th largest entry in `input`, and its index is `indices[j]`. -// -// For matrices (resp. higher rank input), computes the top `k` entries in each -// row (resp. vector along the last dimension). Thus, -// -// values.shape = indices.shape = input.shape[:-1] + [k] -// -// If two elements are equal, the lower-index element appears first. -// -// If `k` varies dynamically, use `TopKV2` below. -// -// Arguments: -// input: 1-D or higher with last dimension at least `k`. -// k: Number of top elements to look for along the last dimension (along each -// row for matrices). -// -// Returns The `k` largest elements along each last dimensional slice.The indices of `values` within the last dimension of `input`. -func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"k": k} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TopK", + Type: "Lu", Input: []tf.Input{ input, }, @@ -28596,30 +30275,30 @@ func QuantizedConv2DPerChannel(scope *Scope, input tf.Output, filter tf.Output, return op.Output(0), op.Output(1), op.Output(2) } -// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize. -type QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr func(optionalAttr) +// QuantizedDepthwiseConv2DWithBiasAttr is an optional argument to QuantizedDepthwiseConv2DWithBias. +type QuantizedDepthwiseConv2DWithBiasAttr func(optionalAttr) -// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType sets the optional out_type attribute to value. +// QuantizedDepthwiseConv2DWithBiasOutType sets the optional out_type attribute to value. // // value: The type of the output. -// If not specified, defaults to DT_QUINT8 -func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAttr { return func(m optionalAttr) { m["out_type"] = value } } -// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value. +// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value. // // value: List of dilation values. // If not specified, defaults to -func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { +func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr { return func(m optionalAttr) { m["dilations"] = value } } -// Computes quantized depthwise Conv2D with Bias, Relu and Requantize. +// Computes quantized depthwise Conv2D with Bias. // // Arguments: // input: The original input tensor. @@ -28629,13 +30308,11 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64 // max_input: The float value that the maximum quantized input value represents. // min_filter: The float value that the minimum quantized filter value represents. // max_filter: The float value that the maximum quantized filter value represents. -// min_freezed_output: The minimum float value of the output tensor. -// max_freezed_output: The maximum float value of the output tensor. // strides: List of stride values. // // // Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. -func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, min_freezed_output tf.Output, max_freezed_output tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { +func QuantizedDepthwiseConv2DWithBias(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { if scope.Err() != nil { return } @@ -28644,9 +30321,9 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(scope *Scope, input tf a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize", + Type: "QuantizedDepthwiseConv2DWithBias", Input: []tf.Input{ - input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, + input, filter, bias, min_input, max_input, min_filter, max_filter, }, Attrs: attrs, } @@ -28654,29 +30331,168 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(scope *Scope, input tf return op.Output(0), op.Output(1), op.Output(2) } -// Outputs a tensor containing the reduction across all input tensors. +// Greedily selects a subset of bounding boxes in descending order of score, // -// Outputs a tensor containing the reduction across all input tensors passed to ops -// within the same `shared_name. +// pruning away boxes that have high overlaps +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. N-by-n overlap values are supplied as square matrix, +// which allows for defining a custom overlap criterium (eg. intersection over union, +// intersection over area, etc.). // -// The graph should be constructed so if one op runs with shared_name value `c`, -// then `num_devices` ops will run with shared_name value `c`. Failure to do so -// will cause the graph execution to fail to complete. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: // -// input: the input to the reduction -// data: the value of the reduction across all `num_devices` devices. -// reduction: the reduction operation to perform. -// num_devices: The number of devices participating in this reduction. -// shared_name: Identifier that shared between ops of the same reduction. -func NcclAllReduce(scope *Scope, input tf.Output, reduction string, num_devices int64, shared_name string) (data tf.Output) { +// selected_indices = tf.image.non_max_suppression_with_overlaps( +// overlaps, scores, max_output_size, overlap_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// overlaps: A 2-D float tensor of shape `[num_boxes, num_boxes]` representing +// the n-by-n box overlap values. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// overlap_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionWithOverlaps(scope *Scope, overlaps tf.Output, scores tf.Output, max_output_size tf.Output, overlap_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"reduction": reduction, "num_devices": num_devices, "shared_name": shared_name} opspec := tf.OpSpec{ - Type: "NcclAllReduce", + Type: "NonMaxSuppressionWithOverlaps", Input: []tf.Input{ - input, + overlaps, scores, max_output_size, overlap_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softplus: `log(exp(features) + 1)`. +func Softplus(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softplus", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedDepthwiseConv2DWithBiasAndReluAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndRelu. +type QuantizedDepthwiseConv2DWithBiasAndReluAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DWithBiasAndReluOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes quantized depthwise Conv2D with Bias and Relu. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// bias: The original bias tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// strides: List of stride values. +// +// +// Returns The output tensor.The float value that the minimum quantized output value represents.The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2DWithBiasAndRelu(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2DWithBiasAndRelu", + Input: []tf.Input{ + input, filter, bias, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Adds a value to the current value of a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to +// see the incremented value or a subsequent newer one. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value by which the variable will be incremented. +// +// Returns the created operation. +func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignAddVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// Returns the element-wise sum of a list of tensors. +// +// `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not +// wait for all of its inputs to be ready before beginning to sum. This can +// save memory if inputs are ready at different times, since minimum temporary +// storage is proportional to the output size rather than the inputs size. +// +// Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable. +// +// Returns a `Tensor` of same shape and type as the elements of `inputs`. +// +// Arguments: +// inputs: A list of `Tensor` objects, each with same shape and type. +// shape: Shape of elements of `inputs`. +func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "AccumulateNV2", + Input: []tf.Input{ + tf.OutputList(inputs), }, Attrs: attrs, } @@ -28684,30 +30500,45 @@ func NcclAllReduce(scope *Scope, input tf.Output, reduction string, num_devices return op.Output(0) } -// AssertAttr is an optional argument to Assert. -type AssertAttr func(optionalAttr) +// GatherAttr is an optional argument to Gather. +type GatherAttr func(optionalAttr) -// AssertSummarize sets the optional summarize attribute to value. -// -// value: Print this many entries of each tensor. -// If not specified, defaults to 3 -func AssertSummarize(value int64) AssertAttr { +// GatherValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func GatherValidateIndices(value bool) GatherAttr { return func(m optionalAttr) { - m["summarize"] = value + m["validate_indices"] = value } } -// Asserts that the given condition is true. +// Gather slices from `params` according to `indices`. // -// If `condition` evaluates to false, print the list of tensors in `data`. -// `summarize` determines how many entries of the tensors to print. +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: // -// Arguments: -// condition: The condition to evaluate. -// data: The tensors to print out when condition is false. +// ```python +// # Scalar indices +// output[:, ..., :] = params[indices, :, ... :] // -// Returns the created operation. -func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) { +// # Vector indices +// output[i, :, ..., :] = params[indices[i], :, ... :] +// +// # Higher rank indices +// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// ``` +// +// If `indices` is a permutation and `len(indices) == params.shape[0]` then +// this operation will permute `params` accordingly. +// +// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in +// `indices` are always validated to be within range. If assigned to GPU, +// out-of-bound indices result in safe but unspecified behavior, which may include +// raising an error. +// +//
+// +//
+func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -28716,34 +30547,152 @@ func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...Ass a(attrs) } opspec := tf.OpSpec{ - Type: "Assert", + Type: "Gather", Input: []tf.Input{ - condition, tf.OutputList(data), + params, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// HashTableV2Attr is an optional argument to HashTableV2. +type HashTableV2Attr func(optionalAttr) + +// HashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func HashTableV2Container(value string) HashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// HashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func HashTableV2SharedName(value string) HashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// Creates a non-initialized hash table. +// +// This op creates a hash table, specifying the type of its keys and values. +// Before using the table you will have to initialize it. After initialization the +// table will be immutable. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "HashTableV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints. +type MergeV2CheckpointsAttr func(optionalAttr) + +// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value. +// +// value: see above. +// If not specified, defaults to true +func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr { + return func(m optionalAttr) { + m["delete_old_dirs"] = value + } +} + +// V2 format specific: merges the metadata files of sharded checkpoints. The +// +// result is one logical checkpoint, with one physical metadata file and renamed +// data files. +// +// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. +// +// If delete_old_dirs is true, attempts to delete recursively the dirname of each +// path in the input checkpoint_prefixes. This is useful when those paths are non +// user-facing temporary locations. +// +// Arguments: +// checkpoint_prefixes: prefixes of V2 checkpoints to merge. +// destination_prefix: scalar. The desired final prefix. Allowed to be the same +// as one of the checkpoint_prefixes. +// +// Returns the created operation. +func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MergeV2Checkpoints", + Input: []tf.Input{ + checkpoint_prefixes, destination_prefix, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// Reduces `input` from `num_devices` using `reduction` to a single device. -// -// Reduces `input` from `num_devices` using `reduction` to a single device. -// -// The graph should be constructed so that all inputs have a valid device -// assignment, and the op itself is assigned one of these devices. -// -// input: The input to the reduction. -// data: the value of the reduction across all `num_devices` devices. -// reduction: the reduction operation to perform. -func NcclReduce(scope *Scope, input []tf.Output, reduction string) (data tf.Output) { +// CastAttr is an optional argument to Cast. +type CastAttr func(optionalAttr) + +// CastTruncate sets the optional Truncate attribute to value. +// If not specified, defaults to false +func CastTruncate(value bool) CastAttr { + return func(m optionalAttr) { + m["Truncate"] = value + } +} + +// Cast x of type SrcT to y of DstT. +func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"reduction": reduction} + attrs := map[string]interface{}{"DstT": DstT} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "NcclReduce", + Type: "Cast", Input: []tf.Input{ - tf.OutputList(input), + x, }, Attrs: attrs, } @@ -28751,61 +30700,145 @@ func NcclReduce(scope *Scope, input []tf.Output, reduction string) (data tf.Outp return op.Output(0) } -// Returns 0 if x == 0, and x * log(y) otherwise, elementwise. -func Xlogy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// Computes the reciprocal of x element-wise. +// +// I.e., \\(y = 1 / x\\). +func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Xlogy", + Type: "Reciprocal", Input: []tf.Input{ - x, y, + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Sends `input` to all devices that are connected to the output. -// -// Sends `input` to all devices that are connected to the output. -// -// The graph should be constructed so that all ops connected to the output have a -// valid device assignment, and the op itself is assigned one of these devices. -// -// input: The input to the broadcast. -// output: The same as input. -// shape: The shape of the input tensor. -// -func NcclBroadcast(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { +// Computes the Gauss error function of `x` element-wise. +func Erf(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"shape": shape} opspec := tf.OpSpec{ - Type: "NcclBroadcast", + Type: "Erf", Input: []tf.Input{ - input, + x, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Add all input tensors element wise. +// Computes square of x element-wise. +// +// I.e., \\(y = x * x = x^2\\). +func Square(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Square", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp. +type ResourceApplyRMSPropAttr func(optionalAttr) + +// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, ms, and mom tensors is protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the RMSProp algorithm. +// +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom // // Arguments: -// inputs: Must all be the same size and shape. -func AddN(scope *Scope, inputs []tf.Output) (sum tf.Output) { +// var_: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "AddN", + Type: "ResourceApplyRMSProp", Input: []tf.Input{ - tf.OutputList(inputs), + var_, ms, mom, lr, rho, momentum, epsilon, grad, }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// TensorListStackAttr is an optional argument to TensorListStack. +type TensorListStackAttr func(optionalAttr) + +// TensorListStackNumElements sets the optional num_elements attribute to value. +// If not specified, defaults to -1 +func TensorListStackNumElements(value int64) TensorListStackAttr { + return func(m optionalAttr) { + m["num_elements"] = value + } +} + +// Stacks all tensors in the list. +// +// Requires that all tensors have the same shape. +// +// input_handle: the input list +// tensor: the gathered result +// num_elements: optional. If not -1, the number of elements in the list. +// +func TensorListStack(scope *Scope, input_handle tf.Output, element_shape tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorListStack", + Input: []tf.Input{ + input_handle, element_shape, + }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -28862,188 +30895,124 @@ func ResizeBilinearGrad(scope *Scope, grads tf.Output, original_image tf.Output, return op.Output(0) } -// SerializeManySparseAttr is an optional argument to SerializeManySparse. -type SerializeManySparseAttr func(optionalAttr) - -// SerializeManySparseOutType sets the optional out_type attribute to value. +// Computes square root of x element-wise. // -// value: The `dtype` to use for serialization; the supported types are `string` -// (default) and `variant`. -// If not specified, defaults to DT_STRING -func SerializeManySparseOutType(value tf.DataType) SerializeManySparseAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object. -// -// The `SparseTensor` must have rank `R` greater than 1, and the first dimension -// is treated as the minibatch dimension. Elements of the `SparseTensor` -// must be sorted in increasing order of this first dimension. The serialized -// `SparseTensor` objects going into each row of `serialized_sparse` will have -// rank `R-1`. -// -// The minibatch size `N` is extracted from `sparse_shape[0]`. -// -// Arguments: -// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. -// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. -// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. -func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeManySparseAttr) (serialized_sparse tf.Output) { +// I.e., \\(y = \sqrt{x} = x^{1/2}\\). +func Sqrt(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SerializeManySparse", + Type: "Sqrt", Input: []tf.Input{ - sparse_indices, sparse_values, sparse_shape, + x, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// EnqueueTPUEmbeddingSparseBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseBatch. -type EnqueueTPUEmbeddingSparseBatchAttr func(optionalAttr) - -// EnqueueTPUEmbeddingSparseBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// Rounds the values of a tensor to the nearest integer, element-wise. // -// value: The TPU device to use. Should be >= 0 and less than the number -// of TPU cores in the task on which the node is placed. +// Rounds half to even. Also known as bankers rounding. If you want to round +// according to the current system rounding mode use std::cint. +func Round(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Round", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadAndRemapMatrixAttr is an optional argument to LoadAndRemapMatrix. +type LoadAndRemapMatrixAttr func(optionalAttr) + +// LoadAndRemapMatrixMaxRowsInMemory sets the optional max_rows_in_memory attribute to value. +// +// value: The maximum number of rows to load from the checkpoint at +// once. If less than or equal to 0, the entire matrix will be loaded into +// memory. Setting this arg trades increased disk reads for lower memory usage. // If not specified, defaults to -1 -func EnqueueTPUEmbeddingSparseBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseBatchAttr { +func LoadAndRemapMatrixMaxRowsInMemory(value int64) LoadAndRemapMatrixAttr { return func(m optionalAttr) { - m["device_ordinal"] = value + m["max_rows_in_memory"] = value } } -// EnqueueTPUEmbeddingSparseBatchCombiners sets the optional combiners attribute to value. +// Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint // -// value: A list of string scalars, one for each embedding table that specify -// how to normalize the embedding activations after weighted summation. -// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have -// the sum of the weights be 0 for 'mean' or the sum of the squared weights be -// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for -// all tables. -// If not specified, defaults to <> -func EnqueueTPUEmbeddingSparseBatchCombiners(value []string) EnqueueTPUEmbeddingSparseBatchAttr { - return func(m optionalAttr) { - m["combiners"] = value - } -} - -// An op that enqueues TPUEmbedding input indices from a SparseTensor. +// at `ckpt_path` and potentially reorders its rows and columns using the +// specified remappings. // -// This Op eases the porting of code that uses embedding_lookup_sparse(), -// although some Python preprocessing of the SparseTensor arguments to -// embedding_lookup_sparse() is required to produce the arguments to this Op, -// since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training -// step. +// Most users should use one of the wrapper initializers (such as +// `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this +// function directly. // -// The tensors at corresponding positions in the three input lists -// must have the same shape, i.e. rank 1 with dim_size() equal to the total -// number of lookups into the table described by the corresponding table_id. +// The remappings are 1-D tensors with the following properties: +// +// * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output +// matrix will be initialized from the row corresponding to index +// `row_remapping[i]` in the old `Tensor` from the checkpoint. +// * `col_remapping` must have either 0 entries (indicating that no column +// reordering is needed) or `num_cols` entries. If specified, column `j` of the +// output matrix will be initialized from the column corresponding to index +// `col_remapping[j]` in the old `Tensor` from the checkpoint. +// * A value of -1 in either of the remappings signifies a "missing" entry. In that +// case, values from the `initializing_values` tensor will be used to fill that +// missing row or column. If `row_remapping` has `r` missing entries and +// `col_remapping` has `c` missing entries, then the following condition must be +// true: +// +// `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)` +// +// The remapping tensors can be generated using the GenerateVocabRemapping op. +// +// As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1], +// initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing +// the value from row i, column j of the old tensor in the checkpoint, the output +// matrix will look like the following: +// +// [[w(1, 0), w(1, 2), 0.5], +// [w(0, 0), w(0, 2), -0.5], +// [0.25, -0.25, 42]] // // Arguments: -// sample_indices: A list of rank 1 Tensors specifying the training example and -// feature to which the corresponding embedding_indices and aggregation_weights -// values belong. sample_indices[i] must equal b * nf + f, where nf is the -// number of features from the corresponding table, f is in [0, nf), and -// b is in [0, batch size). -// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. -// aggregation_weights: A list of rank 1 Tensors containing per sample -- i.e. per -// (training example, feature) -- aggregation weights. -// mode_override: A string input that overrides the mode specified in the -// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', -// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set -// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from +// which the old matrix `Tensor` will be loaded. +// old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint. +// row_remapping: An int `Tensor` of row remappings (generally created by +// `generate_vocab_remapping`). Even if no row remapping is needed, this must +// still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted +// index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`). +// col_remapping: An int `Tensor` of column remappings (generally created by +// `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping +// is to be done (e.g. column ordering is the same). +// initializing_values: A float `Tensor` containing values to fill in for cells +// in the output matrix that are not loaded from the checkpoint. Length must be +// exactly the same as the number of missing / new cells. +// num_rows: Number of rows (length of the 1st dimension) in the output matrix. +// num_cols: Number of columns (length of the 2nd dimension) in the output matrix. // -// Returns the created operation. -func EnqueueTPUEmbeddingSparseBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingSparseBatchAttr) (o *tf.Operation) { +// Returns Output matrix containing existing values loaded from the +// checkpoint, and with any missing values filled in from initializing_values. +func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Output, row_remapping tf.Output, col_remapping tf.Output, initializing_values tf.Output, num_rows int64, num_cols int64, optional ...LoadAndRemapMatrixAttr) (output_matrix tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"num_rows": num_rows, "num_cols": num_cols} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "EnqueueTPUEmbeddingSparseBatch", + Type: "LoadAndRemapMatrix", Input: []tf.Input{ - tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// BatchMatMulAttr is an optional argument to BatchMatMul. -type BatchMatMulAttr func(optionalAttr) - -// BatchMatMulAdjX sets the optional adj_x attribute to value. -// -// value: If `True`, adjoint the slices of `x`. Defaults to `False`. -// If not specified, defaults to false -func BatchMatMulAdjX(value bool) BatchMatMulAttr { - return func(m optionalAttr) { - m["adj_x"] = value - } -} - -// BatchMatMulAdjY sets the optional adj_y attribute to value. -// -// value: If `True`, adjoint the slices of `y`. Defaults to `False`. -// If not specified, defaults to false -func BatchMatMulAdjY(value bool) BatchMatMulAttr { - return func(m optionalAttr) { - m["adj_y"] = value - } -} - -// Multiplies slices of two tensors in batches. -// -// Multiplies all slices of `Tensor` `x` and `y` (each slice can be -// viewed as an element of a batch), and arranges the individual results -// in a single output tensor of the same batch size. Each of the -// individual slices can optionally be adjointed (to adjoint a matrix -// means to transpose and conjugate it) before multiplication by setting -// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. -// -// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` -// and `[..., r_y, c_y]`. -// -// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: -// -// r_o = c_x if adj_x else r_x -// c_o = r_y if adj_y else c_y -// -// It is computed as: -// -// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -// -// Arguments: -// x: 2-D or higher with shape `[..., r_x, c_x]`. -// y: 2-D or higher with shape `[..., r_y, c_y]`. -// -// Returns 3-D or higher with shape `[..., r_o, c_o]` -func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "BatchMatMul", - Input: []tf.Input{ - x, y, + ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values, }, Attrs: attrs, } @@ -29051,172 +31020,74 @@ func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMul return op.Output(0) } -// CastAttr is an optional argument to Cast. -type CastAttr func(optionalAttr) +// TensorArrayConcatV3Attr is an optional argument to TensorArrayConcatV3. +type TensorArrayConcatV3Attr func(optionalAttr) -// CastTruncate sets the optional Truncate attribute to value. -// If not specified, defaults to false -func CastTruncate(value bool) CastAttr { +// TensorArrayConcatV3ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. +// +// value: The expected shape of an element, if known, +// excluding the first dimension. Used to validate the shapes of +// TensorArray elements. If this shape is not fully specified, concatenating +// zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayConcatV3ElementShapeExcept0(value tf.Shape) TensorArrayConcatV3Attr { return func(m optionalAttr) { - m["Truncate"] = value + m["element_shape_except0"] = value } } -// Cast x of type SrcT to y of DstT. -func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y tf.Output) { +// Concat the elements from the TensorArray into value `value`. +// +// Takes `T` elements of shapes +// +// ``` +// (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...) +// ``` +// +// and concatenates them into a Tensor of shape: +// +// ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)``` +// +// All elements must have the same shape (excepting the first dimension). +// +// Arguments: +// handle: The handle to a TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. +// +// Returns All of the elements in the TensorArray, concatenated along the first +// axis.A vector of the row sizes of the original T elements in the +// value output. In the example above, this would be the values: +// `(n1, n2, ..., n(T-1))`. +func TensorArrayConcatV3(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV3Attr) (value tf.Output, lengths tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"DstT": DstT} + attrs := map[string]interface{}{"dtype": dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "Cast", + Type: "TensorArrayConcatV3", Input: []tf.Input{ - x, + handle, flow_in, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1) } -// Returns the min of x and y (i.e. x < y ? x : y) element-wise. +// Computes the gradient for the rsqrt of `x` wrt its input. // -// *NOTE*: `Minimum` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Minimum", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. -type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) - -// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. -// If not specified, defaults to -6 -func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["min"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. -// If not specified, defaults to 6 -func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["max"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. -// If not specified, defaults to 8 -func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["num_bits"] = value - } -} - -// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. -// If not specified, defaults to false -func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { - return func(m optionalAttr) { - m["narrow_range"] = value - } -} - -// Compute gradients for a FakeQuantWithMinMaxArgs operation. -// -// Arguments: -// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. -// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. -// -// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: -// `gradients * (inputs >= min && inputs <= max)`. -func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "FakeQuantWithMinMaxArgsGradient", - Input: []tf.Input{ - gradients, inputs, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Inverse 3D fast Fourier transform. -// -// Computes the inverse 3-dimensional discrete Fourier transform over the -// inner-most 3 dimensions of `input`. -// -// Arguments: -// input: A complex64 tensor. -// -// Returns A complex64 tensor of the same shape as `input`. The inner-most 3 -// dimensions of `input` are replaced with their inverse 3D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.ifftn with 3 dimensions. -// @end_compatibility -func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IFFT3D", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the reciprocal of x element-wise. -// -// I.e., \\(y = 1 / x\\). -func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Reciprocal", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the gradient for the inverse of `x` wrt its input. -// -// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` +// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy` // is the corresponding input gradient. -func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { +func RsqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ReciprocalGrad", + Type: "RsqrtGrad", Input: []tf.Input{ y, dy, }, @@ -29225,23 +31096,79 @@ func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { return op.Output(0) } -// Computes reciprocal of square root of x element-wise. +// Creates a dataset that batches input elements into a SparseTensor. // -// I.e., \\(y = 1 / \sqrt{x}\\). -func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) { +// Arguments: +// input_dataset: A handle to an input dataset. Must have a single component. +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// row_shape: A vector representing the dense shape of each row in the produced +// SparseTensor. The shape may be partially specified, using `-1` to indicate +// that a particular dimension should use the maximum size of all batch elements. +// +// +func ExperimentalDenseToSparseBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, row_shape tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "Rsqrt", + Type: "ExperimentalDenseToSparseBatchDataset", Input: []tf.Input{ - x, + input_dataset, batch_size, row_shape, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingADAMParametersGradAccumDebug. +type RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve ADAM embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the ADAM optimization algorithm.Parameter momenta updated by the ADAM optimization algorithm.Parameter velocities updated by the ADAM optimization algorithm.Parameter gradient_accumulators updated by the ADAM optimization algorithm. +func RetrieveTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + // Computes `exp(x) - 1` element-wise. // // i.e. `exp(x) - 1` or `e^(x) - 1`, where `x` is the input tensor. @@ -29271,28 +31198,127 @@ func Expm1(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// LoadTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to LoadTPUEmbeddingStochasticGradientDescentParameters. -type LoadTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) +// Scatter `updates` into an existing tensor according to `indices`. +// +// This operation creates a new tensor by applying sparse `updates` to the passed +// in `tensor`. +// This operation is very similar to `tf.scatter_nd`, except that the updates are +// scattered onto an existing tensor (as opposed to a zero-tensor). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// If `indices` contains duplicates, then their updates are accumulated (summed). +// +// **WARNING**: The order in which updates are applied is nondeterministic, so the +// output will be nondeterministic if `indices` contains duplicates -- because +// of some numerical approximation issues, numbers summed in different order +// may yield different results. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of scatter is to insert individual elements in a tensor by +// index. For example, say we want to insert 4 scattered elements in a rank-1 +// tensor with 8 elements. +// +//
+// +//
+// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// tensor = tf.ones([8], dtype=tf.int32) +// updated = tf.tensor_scatter_update(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [1, 11, 1, 10, 9, 1, 1, 12] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// tensor = tf.ones([4, 4, 4]) +// updated = tf.tensor_scatter_update(tensor, indices, updates) +// with tf.Session() as sess: +// print(sess.run(scatter)) +// ``` +// +// The resulting tensor would look like this: +// +// [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], +// [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor with the given shape and updates applied according +// to the indices. +func TensorScatterUpdate(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterUpdate", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// LoadTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug. +type LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. // If not specified, defaults to -1 // // REQUIRES: value >= -1 -func LoadTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { return func(m optionalAttr) { m["table_id"] = value } } -// LoadTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. // If not specified, defaults to "" -func LoadTPUEmbeddingStochasticGradientDescentParametersTableName(value string) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { return func(m optionalAttr) { m["table_name"] = value } } -// Load SGD embedding parameters. +// Load proximal Adagrad embedding parameters with debug support. // // An op that loads optimization parameters into HBM for embedding. Must be // preceded by a ConfigureTPUEmbeddingHost op that sets up the correct @@ -29301,12 +31327,14 @@ func LoadTPUEmbeddingStochasticGradientDescentParametersTableName(value string) // executed. // // Arguments: -// parameters: Value of parameters used in the stochastic gradient descent optimization algorithm. +// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the proximal Adagrad optimization algorithm. // // // // Returns the created operation. -func LoadTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, parameters tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingStochasticGradientDescentParametersAttr) (o *tf.Operation) { +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -29315,15 +31343,33 @@ func LoadTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, parameter a(attrs) } opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingStochasticGradientDescentParameters", + Type: "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", Input: []tf.Input{ - parameters, + parameters, accumulators, gradient_accumulators, }, Attrs: attrs, } return scope.AddOperation(opspec) } +// Returns x * y element-wise. +// +// *NOTE*: `Multiply` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Mul", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes natural logarithm of (1 + x) element-wise. // // I.e., \\(y = \log_e (1 + x)\\). @@ -29341,68 +31387,6 @@ func Log1p(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// SqueezeAttr is an optional argument to Squeeze. -type SqueezeAttr func(optionalAttr) - -// SqueezeAxis sets the optional axis attribute to value. -// -// value: If specified, only squeezes the dimensions listed. The dimension -// index starts at 0. It is an error to squeeze a dimension that is not 1. Must -// be in the range `[-rank(input), rank(input))`. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func SqueezeAxis(value []int64) SqueezeAttr { - return func(m optionalAttr) { - m["squeeze_dims"] = value - } -} - -// Removes dimensions of size 1 from the shape of a tensor. -// -// Given a tensor `input`, this operation returns a tensor of the same type with -// all dimensions of size 1 removed. If you don't want to remove all size 1 -// dimensions, you can remove specific size 1 dimensions by specifying -// `axis`. -// -// For example: -// -// ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t)) ==> [2, 3] -// ``` -// -// Or, to remove specific size 1 dimensions: -// -// ``` -// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] -// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] -// ``` -// -// Arguments: -// input: The `input` to squeeze. -// -// Returns Contains the same data as `input`, but has one or more dimensions of -// size 1 removed. -func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Squeeze", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes hyperbolic cosine of x element-wise. func Cosh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -29418,6 +31402,66 @@ func Cosh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// RestoreAttr is an optional argument to Restore. +type RestoreAttr func(optionalAttr) + +// RestorePreferredShard sets the optional preferred_shard attribute to value. +// +// value: Index of file to open first if multiple files match +// `file_pattern`. +// If not specified, defaults to -1 +func RestorePreferredShard(value int64) RestoreAttr { + return func(m optionalAttr) { + m["preferred_shard"] = value + } +} + +// Restores a tensor from checkpoint files. +// +// Reads a tensor stored in one or several files. If there are several files (for +// instance because a tensor was saved as slices), `file_pattern` may contain +// wildcard symbols (`*` and `?`) in the filename portion only, not in the +// directory portion. +// +// If a `file_pattern` matches several files, `preferred_shard` can be used to hint +// in which file the requested tensor is likely to be found. This op will first +// open the file at index `preferred_shard` in the list of matching files and try +// to restore tensors from that file. Only if some tensors or tensor slices are +// not found in that first file, then the Op opens all the files. Setting +// `preferred_shard` to match the value passed as the `shard` input +// of a matching `Save` Op may speed up Restore. This attribute only affects +// performance, not correctness. The default value -1 means files are processed in +// order. +// +// See also `RestoreSlice`. +// +// Arguments: +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// dt: The type of the tensor to be restored. +// +// Returns The restored tensor. +func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dt": dt} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Restore", + Input: []tf.Input{ + file_pattern, tensor_name, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes hyperbolic tangent of `x` element-wise. func Tanh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -29433,13 +31477,13 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Computes inverse hyperbolic sine of x element-wise. -func Asinh(scope *Scope, x tf.Output) (y tf.Output) { +// Computes inverse hyperbolic cosine of x element-wise. +func Acosh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Asinh", + Type: "Acosh", Input: []tf.Input{ x, }, @@ -29448,6 +31492,60 @@ func Asinh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// IteratorFromStringHandleAttr is an optional argument to IteratorFromStringHandle. +type IteratorFromStringHandleAttr func(optionalAttr) + +// IteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. +// +// value: If specified, defines the type of each tuple component in an +// element produced by the resulting iterator. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func IteratorFromStringHandleOutputTypes(value []tf.DataType) IteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_types"] = value + } +} + +// IteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. +// +// value: If specified, defines the shape of each tuple component in an +// element produced by the resulting iterator. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func IteratorFromStringHandleOutputShapes(value []tf.Shape) IteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_shapes"] = value + } +} + +// Converts the given string representing a handle to an iterator to a resource. +// +// Arguments: +// string_handle: A string representation of the given handle. +// +// Returns A handle to an iterator resource. +func IteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...IteratorFromStringHandleAttr) (resource_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IteratorFromStringHandle", + Input: []tf.Input{ + string_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes inverse hyperbolic tangent of x element-wise. func Atanh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -29463,112 +31561,21 @@ func Atanh(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Computes the gradient for the tanh of `x` wrt its input. -// -// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy` -// is the corresponding input gradient. -func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { +// Computes hyperbolic sine of x element-wise. +func Sinh(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "TanhGrad", + Type: "Sinh", Input: []tf.Input{ - y, dy, + x, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter. -type Conv2DBackpropFilterAttr func(optionalAttr) - -// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. -// If not specified, defaults to true -func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr { - return func(m optionalAttr) { - m["use_cudnn_on_gpu"] = value - } -} - -// Conv2DBackpropFilterExplicitPaddings sets the optional explicit_paddings attribute to value. -// -// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith -// dimension, the amount of padding inserted before and after the dimension is -// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If -// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. -// If not specified, defaults to <> -func Conv2DBackpropFilterExplicitPaddings(value []int64) Conv2DBackpropFilterAttr { - return func(m optionalAttr) { - m["explicit_paddings"] = value - } -} - -// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Conv2DBackpropFilterDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of convolution with respect to the filter. -// -// Arguments: -// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. -// filter_sizes: An integer vector representing the tensor shape of `filter`, -// where `filter` is a 4-D -// `[filter_height, filter_width, in_channels, out_channels]` tensor. -// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. -// Gradients w.r.t. the output of the convolution. -// strides: The stride of the sliding window for each dimension of the input -// of the convolution. Must be in the same order as the dimension specified with -// format. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape -// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. -// the `filter` input of the convolution. -func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Conv2DBackpropFilter", - Input: []tf.Input{ - input, filter_sizes, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the log of the absolute value of `Gamma(x)` element-wise. func Lgamma(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -29584,21 +31591,6 @@ func Lgamma(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Computes the Gauss error function of `x` element-wise. -func Erf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Erf", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the complementary error function of `x` element-wise. func Erfc(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -29614,23 +31606,6 @@ func Erfc(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Computes sigmoid of `x` element-wise. -// -// Specifically, `y = 1 / (1 + exp(-x))`. -func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Sigmoid", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // DecodeRawAttr is an optional argument to DecodeRaw. type DecodeRawAttr func(optionalAttr) @@ -29738,122 +31713,41 @@ func SigmoidGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { return op.Output(0) } -// Computes the mean along segments of a tensor. +// Computes the matrix logarithm of one or more square matrices: // -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. // -// Computes a tensor such that -// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is -// over `j` such that `segment_ids[j] == i` and `N` is the total number of -// values summed. +// \\(log(exp(A)) = A\\) // -// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// This op is only defined for complex matrices. If A is positive-definite and +// real, then casting to a complex matrix, taking the logarithm and casting back +// to a real matrix will give the correct result. // -//
-// -//
-// -// For example: -// -// ``` -// c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) -// tf.segment_mean(c, tf.constant([0, 0, 1])) -// # ==> [[2.5, 2.5, 2.5, 2.5], -// # [5, 6, 7, 8]] -// ``` +// This function computes the matrix logarithm using the Schur-Parlett algorithm. +// Details of the algorithm can be found in Section 11.6.2 of: +// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008. +// ISBN 978-0-898716-46-7. // +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the exponential for all input submatrices `[..., :, :]`. // // Arguments: +// input: Shape is `[..., M, M]`. // -// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s -// first dimension. Values should be sorted and can be repeated. +// Returns Shape is `[..., M, M]`. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +// @compatibility(scipy) +// Equivalent to scipy.linalg.logm +// @end_compatibility +func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SegmentMean", - Input: []tf.Input{ - data, segment_ids, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the value stored in an Optional variant or raises an error if none exists. -func OptionalGetValue(scope *Scope, optional tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "OptionalGetValue", - Input: []tf.Input{ - optional, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if components, idx, err = makeOutputList(op, idx, "components"); err != nil { - scope.UpdateErr("OptionalGetValue", err) - return - } - return components -} - -// MaxPool3DAttr is an optional argument to MaxPool3D. -type MaxPool3DAttr func(optionalAttr) - -// MaxPool3DDataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func MaxPool3DDataFormat(value string) MaxPool3DAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs 3D max pooling on the input. -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool3D", + Type: "MatrixLogarithm", Input: []tf.Input{ input, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -29874,37 +31768,56 @@ func Sin(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Returns the truth value of (x < y) element-wise. +// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. +type ResourceApplyFtrlAttr func(optionalAttr) + +// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value. // -// *NOTE*: `Less` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { + return func(m optionalAttr) { + m["use_locking"] = value } - opspec := tf.OpSpec{ - Type: "Less", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) } -// Computes cos of x element-wise. -func Cos(scope *Scope, x tf.Output) (y tf.Output) { +// Update '*var' according to the Ftrl-proximal scheme. +// +// accum_new = accum + grad * grad +// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regulariation. Must be a scalar. +// l2: L2 regulariation. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "Cos", - Input: []tf.Input{ - x, - }, + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) } - op := scope.AddOperation(opspec) - return op.Output(0) + opspec := tf.OpSpec{ + Type: "ResourceApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, lr, l1, l2, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) } // Computes tan of x element-wise. @@ -29954,61 +31867,47 @@ func Asin(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// QueueDequeueV2Attr is an optional argument to QueueDequeueV2. -type QueueDequeueV2Attr func(optionalAttr) - -// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value. -// -// value: If the queue is empty, this operation will block for up to -// timeout_ms milliseconds. -// Note: This option is not supported yet. -// If not specified, defaults to -1 -func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr { - return func(m optionalAttr) { - m["timeout_ms"] = value - } -} - -// Dequeues a tuple of one or more tensors from the given queue. -// -// This operation has k outputs, where k is the number of components -// in the tuples stored in the given queue, and output i is the ith -// component of the dequeued tuple. -// -// N.B. If the queue is empty, this operation will block until an element -// has been dequeued (or 'timeout_ms' elapses, if specified). +// Extract `patches` from `images` and put them in the "depth" output dimension. // // Arguments: -// handle: The handle to a queue. -// component_types: The type of each component in a tuple. +// images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`. +// ksizes: The size of the sliding window for each dimension of `images`. +// strides: 1-D of length 4. How far the centers of two consecutive patches are in +// the images. Must be: `[1, stride_rows, stride_cols, 1]`. +// rates: 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the +// input stride, specifying how far two consecutive patch samples are in the +// input. Equivalent to extracting patches with +// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by +// subsampling them spatially by a factor of `rates`. This is equivalent to +// `rate` in dilated (a.k.a. Atrous) convolutions. +// padding: The type of padding algorithm to use. // -// Returns One or more tensors that were dequeued as a tuple. -func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) { +// We specify the size-related attributes as: +// +// ```python +// ksizes = [1, ksize_rows, ksize_cols, 1] +// strides = [1, strides_rows, strides_cols, 1] +// rates = [1, rates_rows, rates_cols, 1] +// ``` +// +// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * +// ksize_cols * depth]` containing image patches with size +// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note +// `out_rows` and `out_cols` are the dimensions of the output patches. +func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"component_types": component_types} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding} opspec := tf.OpSpec{ - Type: "QueueDequeueV2", + Type: "ExtractImagePatches", Input: []tf.Input{ - handle, + images, }, Attrs: attrs, } op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if components, idx, err = makeOutputList(op, idx, "components"); err != nil { - scope.UpdateErr("QueueDequeueV2", err) - return - } - return components + return op.Output(0) } // Creates a dataset that concatenates `input_dataset` with `another_dataset`. @@ -30028,6 +31927,61 @@ func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset t return op.Output(0) } +// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage. +type CropAndResizeGradImageAttr func(optionalAttr) + +// CropAndResizeGradImageMethod sets the optional method attribute to value. +// +// value: A string specifying the interpolation method. Only 'bilinear' is +// supported for now. +// If not specified, defaults to "bilinear" +func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// Computes the gradient of the crop_and_resize op wrt the input image tensor. +// +// Arguments: +// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` +// containing the original image size. Both `image_height` and `image_width` need +// to be positive. +// +// +// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResizeGradImage", + Input: []tf.Input{ + grads, boxes, box_ind, image_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes acos of x element-wise. func Acos(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -30075,208 +32029,6 @@ func Atan(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// CudnnRNNCanonicalToParamsAttr is an optional argument to CudnnRNNCanonicalToParams. -type CudnnRNNCanonicalToParamsAttr func(optionalAttr) - -// CudnnRNNCanonicalToParamsRnnMode sets the optional rnn_mode attribute to value. -// If not specified, defaults to "lstm" -func CudnnRNNCanonicalToParamsRnnMode(value string) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["rnn_mode"] = value - } -} - -// CudnnRNNCanonicalToParamsInputMode sets the optional input_mode attribute to value. -// If not specified, defaults to "linear_input" -func CudnnRNNCanonicalToParamsInputMode(value string) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["input_mode"] = value - } -} - -// CudnnRNNCanonicalToParamsDirection sets the optional direction attribute to value. -// If not specified, defaults to "unidirectional" -func CudnnRNNCanonicalToParamsDirection(value string) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["direction"] = value - } -} - -// CudnnRNNCanonicalToParamsDropout sets the optional dropout attribute to value. -// If not specified, defaults to 0 -func CudnnRNNCanonicalToParamsDropout(value float32) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["dropout"] = value - } -} - -// CudnnRNNCanonicalToParamsSeed sets the optional seed attribute to value. -// If not specified, defaults to 0 -func CudnnRNNCanonicalToParamsSeed(value int64) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// CudnnRNNCanonicalToParamsSeed2 sets the optional seed2 attribute to value. -// If not specified, defaults to 0 -func CudnnRNNCanonicalToParamsSeed2(value int64) CudnnRNNCanonicalToParamsAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Converts CudnnRNN params from canonical form to usable form. -// -// Writes a set of weights into the opaque params buffer so they can be used in -// upcoming training or inferences. -// -// Note that the params buffer may not be compatible across different GPUs. So any -// save and restoration should be converted to and from the canonical weights and -// biases. -// -// num_layers: Specifies the number of layers in the RNN model. -// num_units: Specifies the size of the hidden state. -// input_size: Specifies the size of the input state. -// weights: the canonical form of weights that can be used for saving -// and restoration. They are more likely to be compatible across different -// generations. -// biases: the canonical form of biases that can be used for saving -// and restoration. They are more likely to be compatible across different -// generations. -// num_params: number of parameter sets for all layers. -// Each layer may contain multiple parameter sets, with each set consisting of -// a weight matrix and a bias vector. -// rnn_mode: Indicates the type of the RNN model. -// input_mode: Indicate whether there is a linear projection between the input and -// The actual computation before the first layer. 'skip_input' is only allowed -// when input_size == num_units; 'auto_select' implies 'skip_input' when -// input_size == num_units; otherwise, it implies 'linear_input'. -// direction: Indicates whether a bidirectional model will be used. -// dir = (direction == bidirectional) ? 2 : 1 -// dropout: dropout probability. When set to 0., dropout is disabled. -// seed: the 1st part of a seed to initialize dropout. -// seed2: the 2nd part of a seed to initialize dropout. -func CudnnRNNCanonicalToParams(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsAttr) (params tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CudnnRNNCanonicalToParams", - Input: []tf.Input{ - num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PrintAttr is an optional argument to Print. -type PrintAttr func(optionalAttr) - -// PrintMessage sets the optional message attribute to value. -// -// value: A string, prefix of the error message. -// If not specified, defaults to "" -func PrintMessage(value string) PrintAttr { - return func(m optionalAttr) { - m["message"] = value - } -} - -// PrintFirstN sets the optional first_n attribute to value. -// -// value: Only log `first_n` number of times. -1 disables logging. -// If not specified, defaults to -1 -func PrintFirstN(value int64) PrintAttr { - return func(m optionalAttr) { - m["first_n"] = value - } -} - -// PrintSummarize sets the optional summarize attribute to value. -// -// value: Only print this many entries of each tensor. -// If not specified, defaults to 3 -func PrintSummarize(value int64) PrintAttr { - return func(m optionalAttr) { - m["summarize"] = value - } -} - -// Prints a list of tensors. -// -// Passes `input` through to `output` and prints `data` when evaluating. -// -// Arguments: -// input: The tensor passed to `output` -// data: A list of tensors to print out when op is evaluated. -// -// Returns = The unmodified `input` tensor -func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Print", - Input: []tf.Input{ - input, tf.OutputList(data), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that contains the unique elements of `input_dataset`. -func ExperimentalUniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalUniqueDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Pop the element at the top of the stack. -// -// Arguments: -// handle: The handle to a stack. -// elem_type: The type of the elem that is popped. -// -// Returns The tensor that is popped from the top of the stack. -func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"elem_type": elem_type} - opspec := tf.OpSpec{ - Type: "StackPopV2", - Input: []tf.Input{ - handle, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the Bessel i0e function of `x` element-wise. // // Exponentially scaled modified Bessel function of order 0 defined as @@ -30317,248 +32069,6 @@ func BesselI1e(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Returns the next representable value of `x1` in the direction of `x2`, element-wise. -// -// This operation returns the same result as the C++ std::nextafter function. -// -// It can also return a subnormal number. -// -// @compatibility(cpp) -// Equivalent to C++ std::nextafter function. -// @end_compatibility -func NextAfter(scope *Scope, x1 tf.Output, x2 tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NextAfter", - Input: []tf.Input{ - x1, x2, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns which elements of x are NaN. -// -// @compatibility(numpy) -// Equivalent to np.isnan -// @end_compatibility -func IsNan(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsNan", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CombinedNonMaxSuppressionAttr is an optional argument to CombinedNonMaxSuppression. -type CombinedNonMaxSuppressionAttr func(optionalAttr) - -// CombinedNonMaxSuppressionPadPerClass sets the optional pad_per_class attribute to value. -// -// value: If false, the output nmsed boxes, scores and classes -// are padded/clipped to `max_total_size`. If true, the -// output nmsed boxes, scores and classes are padded to be of length -// `max_size_per_class`*`num_classes`, unless it exceeds `max_total_size` in -// which case it is clipped to `max_total_size`. Defaults to false. -// If not specified, defaults to false -func CombinedNonMaxSuppressionPadPerClass(value bool) CombinedNonMaxSuppressionAttr { - return func(m optionalAttr) { - m["pad_per_class"] = value - } -} - -// CombinedNonMaxSuppressionClipBoxes sets the optional clip_boxes attribute to value. -// -// value: If true, assume the box coordinates are between [0, 1] and clip the output boxes -// if they fall beyond [0, 1]. If false, do not do clipping and output the box -// coordinates as it is. -// If not specified, defaults to true -func CombinedNonMaxSuppressionClipBoxes(value bool) CombinedNonMaxSuppressionAttr { - return func(m optionalAttr) { - m["clip_boxes"] = value - } -} - -// Greedily selects a subset of bounding boxes in descending order of score, -// -// This operation performs non_max_suppression on the inputs per batch, across -// all classes. -// Prunes away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system. Also note that -// this algorithm is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is the final boxes, scores and classes tensor -// returned after performing non_max_suppression. -// -// Arguments: -// boxes: A 4-D float tensor of shape `[batch_size, num_boxes, q, 4]`. If `q` is 1 then -// same boxes are used for all classes otherwise, if `q` is equal to number of -// classes, class-specific boxes are used. -// scores: A 3-D float tensor of shape `[batch_size, num_boxes, num_classes]` -// representing a single score corresponding to each box (each row of boxes). -// max_output_size_per_class: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression per class -// max_total_size: A scalar representing maximum number of boxes retained over all classes. -// iou_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too much with respect to IOU. -// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove -// boxes based on score. -// -// Returns A [batch_size, max_detections, 4] float32 tensor -// containing the non-max suppressed boxes.A [batch_size, max_detections] float32 tensor -// containing the scores for the boxes.A [batch_size, max_detections] float32 tensor -// containing the classes for the boxes.A [batch_size] int32 tensor indicating the number of -// valid detections per batch item. Only the top num_detections[i] entries in -// nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the -// entries are zero paddings. -func CombinedNonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size_per_class tf.Output, max_total_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...CombinedNonMaxSuppressionAttr) (nmsed_boxes tf.Output, nmsed_scores tf.Output, nmsed_classes tf.Output, valid_detections tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CombinedNonMaxSuppression", - Input: []tf.Input{ - boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// Removes keys and its associated values from a table. -// -// The tensor `keys` must of the same type as the keys of the table. Keys not -// already in the table are silently ignored. -// -// Arguments: -// table_handle: Handle to the table. -// keys: Any shape. Keys of the elements to remove. -// -// Returns the created operation. -func LookupTableRemoveV2(scope *Scope, table_handle tf.Output, keys tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LookupTableRemoveV2", - Input: []tf.Input{ - table_handle, keys, - }, - } - return scope.AddOperation(opspec) -} - -// Returns which elements of x are Inf. -// -// @compatibility(numpy) -// Equivalent to np.isinf -// @end_compatibility -func IsInf(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "IsInf", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// UniformCandidateSamplerAttr is an optional argument to UniformCandidateSampler. -type UniformCandidateSamplerAttr func(optionalAttr) - -// UniformCandidateSamplerSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func UniformCandidateSamplerSeed(value int64) UniformCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// UniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func UniformCandidateSamplerSeed2(value int64) UniformCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Generates labels for candidate sampling with a uniform distribution. -// -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. -// -// For each batch, this op picks a single set of sampled candidate labels. -// -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. -// -// Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to randomly sample. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. -// range_max: The sampler will sample integers from the interval [0, range_max). -// -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func UniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...UniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "UniformCandidateSampler", - Input: []tf.Input{ - true_classes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Returns which elements of x are finite. // // @compatibility(numpy) @@ -30578,51 +32088,40 @@ func IsFinite(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// TensorListConcatAttr is an optional argument to TensorListConcat. -type TensorListConcatAttr func(optionalAttr) - -// TensorListConcatElementShape sets the optional element_shape attribute to value. -// If not specified, defaults to -func TensorListConcatElementShape(value tf.Shape) TensorListConcatAttr { - return func(m optionalAttr) { - m["element_shape"] = value - } -} - -// Concats all tensors in the list along the 0th dimension. +// This op consumes a lock created by `MutexLock`. // -// Requires that all tensors have the same shape except the first dimension. +// This op exists to consume a tensor created by `MutexLock` (other than +// direct control dependencies). It should be the only that consumes the tensor, +// and will raise an error if it is not. Its only purpose is to keep the +// mutex lock tensor alive until it is consumed by this op. // -// input_handle: The input list. -// tensor: The concated result. -// lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. +// **NOTE**: This operation must run on the same device as its input. This may +// be enforced via the `colocate_with` mechanism. // -func TensorListConcat(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListConcatAttr) (tensor tf.Output, lengths tf.Output) { +// Arguments: +// mutex_lock: A tensor returned by `MutexLock`. +// +// Returns the created operation. +func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"element_dtype": element_dtype} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "TensorListConcat", + Type: "ConsumeMutexLock", Input: []tf.Input{ - input_handle, + mutex_lock, }, - Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return scope.AddOperation(opspec) } -// Returns element-wise largest integer not greater than x. -func Floor(scope *Scope, x tf.Output) (y tf.Output) { +// Returns element-wise smallest integer not less than x. +func Ceil(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Floor", + Type: "Ceil", Input: []tf.Input{ x, }, @@ -30631,103 +32130,71 @@ func Floor(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// HashTableV2Attr is an optional argument to HashTableV2. -type HashTableV2Attr func(optionalAttr) +// CropAndResizeAttr is an optional argument to CropAndResize. +type CropAndResizeAttr func(optionalAttr) -// HashTableV2Container sets the optional container attribute to value. +// CropAndResizeMethod sets the optional method attribute to value. // -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func HashTableV2Container(value string) HashTableV2Attr { +// value: A string specifying the sampling method for resizing. It can be either +// `"bilinear"` or `"nearest"` and default to `"bilinear"`. Currently two sampling +// methods are supported: Bilinear and Nearest Neighbor. +// If not specified, defaults to "bilinear" +func CropAndResizeMethod(value string) CropAndResizeAttr { return func(m optionalAttr) { - m["container"] = value + m["method"] = value } } -// HashTableV2SharedName sets the optional shared_name attribute to value. +// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value. // -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func HashTableV2SharedName(value string) HashTableV2Attr { +// value: Value used for extrapolation, when applicable. +// If not specified, defaults to 0 +func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr { return func(m optionalAttr) { - m["shared_name"] = value + m["extrapolation_value"] = value } } -// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// Extracts crops from the input image tensor and resizes them. // -// value: If true and shared_name is empty, the table is shared -// using the node name. -// If not specified, defaults to false -func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// Creates a non-initialized hash table. +// Extracts crops from the input image tensor and resizes them using bilinear +// sampling or nearest neighbor sampling (possibly with aspect ratio change) to a +// common output size specified by `crop_size`. This is more general than the +// `crop_to_bounding_box` op which extracts a fixed size slice from the input image +// and does not allow resizing or aspect ratio change. // -// This op creates a hash table, specifying the type of its keys and values. -// Before using the table you will have to initialize it. After initialization the -// table will be immutable. +// Returns a tensor with `crops` from the input `image` at positions defined at the +// bounding box locations in `boxes`. The cropped boxes are all resized (with +// bilinear or nearest neighbor interpolation) to a fixed +// `size = [crop_height, crop_width]`. The result is a 4-D tensor +// `[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned. +// In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical +// results to using `tf.image.resize_bilinear()` or +// `tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with +// `align_corners=True`. // // Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. +// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +// Both `image_height` and `image_width` need to be positive. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All +// cropped image patches are resized to this size. The aspect ratio of the image +// content is not preserved. Both `crop_height` and `crop_width` need to be +// positive. // -// Returns Handle to a table. -func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "HashTableV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TridiagonalSolveAttr is an optional argument to TridiagonalSolve. -type TridiagonalSolveAttr func(optionalAttr) - -// TridiagonalSolvePartialPivoting sets the optional partial_pivoting attribute to value. -// -// value: Whether to apply partial pivoting. Partial pivoting makes the procedure more -// stable, but slower. -// If not specified, defaults to true -func TridiagonalSolvePartialPivoting(value bool) TridiagonalSolveAttr { - return func(m optionalAttr) { - m["partial_pivoting"] = value - } -} - -// Solves tridiagonal systems of equations. -// -// Solves tridiagonal systems of equations. -// Supports batch dimensions and multiple right-hand sides per each left-hand -// side. -// On CPU, solution is computed via Gaussian elimination with or without partial -// pivoting, depending on `partial_pivoting` attribute. On GPU, Nvidia's cuSPARSE -// library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv -// -// Arguments: -// diagonals: Tensor of shape `[..., 3, M]` whose innermost 2 dimensions represent the -// tridiagonal matrices with three rows being the superdiagonal, diagonals, and -// subdiagonals, in order. The last element of the superdiagonal and the first -// element of the subdiagonal is ignored. -// rhs: Tensor of shape `[..., M, K]`, representing K right-hand sides per each -// left-hand side. -// -// Returns Tensor of shape `[..., M, K]` containing the solutions -func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional ...TridiagonalSolveAttr) (output tf.Output) { +// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) { if scope.Err() != nil { return } @@ -30736,9 +32203,9 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional a(attrs) } opspec := tf.OpSpec{ - Type: "TridiagonalSolve", + Type: "CropAndResize", Input: []tf.Input{ - diagonals, rhs, + image, boxes, box_ind, crop_size, }, Attrs: attrs, } @@ -30746,117 +32213,67 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional return op.Output(0) } -// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. -type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) - -// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// Returns element-wise integer closest to x. // -// value: If `True`, updating of the var, mg, ms, and mom tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the centered RMSProp algorithm. +// If the result is midway between two representable values, +// the even representable is chosen. +// For example: // -// The centered RMSProp algorithm uses an estimate of the centered second moment -// (i.e., the variance) for normalization, as opposed to regular RMSProp, which -// uses the (uncentered) second moment. This often helps with training, but is -// slightly more expensive in terms of computation and memory. -// -// Note that in dense implementation of this algorithm, mg, ms, and mom will -// update even if the grad is zero, but in this sparse implementation, mg, ms, -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// mean_grad = decay * mean_grad + (1-decay) * gradient -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) -// -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom -// -// Arguments: -// var_: Should be from a Variable(). -// mg: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. -// -// Returns the created operation. -func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { +// ``` +// rint(-1.5) ==> -2.0 +// rint(0.5000001) ==> 1.0 +// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] +// ``` +func Rint(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "ResourceSparseApplyCenteredRMSProp", + Type: "Rint", Input: []tf.Input{ - var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, + x, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints. -type MergeV2CheckpointsAttr func(optionalAttr) - -// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value. +// Returns x - y element-wise. // -// value: see above. -// If not specified, defaults to true -func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr { - return func(m optionalAttr) { - m["delete_old_dirs"] = value - } -} - -// V2 format specific: merges the metadata files of sharded checkpoints. The -// -// result is one logical checkpoint, with one physical metadata file and renamed -// data files. -// -// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. -// -// If delete_old_dirs is true, attempts to delete recursively the dirname of each -// path in the input checkpoint_prefixes. This is useful when those paths are non -// user-facing temporary locations. -// -// Arguments: -// checkpoint_prefixes: prefixes of V2 checkpoints to merge. -// destination_prefix: scalar. The desired final prefix. Allowed to be the same -// as one of the checkpoint_prefixes. -// -// Returns the created operation. -func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) { +// *NOTE*: `Subtract` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) + opspec := tf.OpSpec{ + Type: "Sub", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the number of nodes in a tree +// +// Arguments: +// tree_handle: Handle to the tree resource. +// +// Returns The size of the tree. +func TensorForestTreeSize(scope *Scope, tree_handle tf.Output) (tree_size tf.Output) { + if scope.Err() != nil { + return } opspec := tf.OpSpec{ - Type: "MergeV2Checkpoints", + Type: "TensorForestTreeSize", Input: []tf.Input{ - checkpoint_prefixes, destination_prefix, + tree_handle, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN. @@ -30895,44 +32312,88 @@ func Div(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// AllAttr is an optional argument to All. -type AllAttr func(optionalAttr) - -// AllKeepDims sets the optional keep_dims attribute to value. +// Converts a flat index or array of flat indices into a tuple of // -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func AllKeepDims(value bool) AllAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the "logical and" of elements across dimensions of a tensor. +// coordinate arrays. // -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. +// @compatibility(numpy) +// Equivalent to np.unravel_index +// @end_compatibility // // Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. +// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the +// flattened version of an array of dimensions dims. +// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling +// indices. // -// Returns The reduced tensor. -func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (output tf.Output) { +// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the +// same shape as the indices array. +func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "All", + Type: "UnravelIndex", Input: []tf.Input{ - input, axis, + indices, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Execute a sub graph on a remote processor. +// +// The graph specifications(such as graph itself, input tensors and output names) +// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo +// as serialized_remote_fused_graph_execute_info. +// The specifications will be passed to a dedicated registered +// remote fused graph executor. The executor will send the graph specifications +// to a remote processor and execute that graph. The execution results +// will be passed to consumer nodes as outputs of this node. +// +// Arguments: +// inputs: Arbitrary number of tensors with arbitrary data types +// +// serialized_remote_fused_graph_execute_info: Serialized protocol buffer +// of RemoteFusedGraphExecuteInfo which contains graph specifications. +// +// Returns Arbitrary number of tensors with arbitrary data types +func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} + opspec := tf.OpSpec{ + Type: "RemoteFusedGraphExecute", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RemoteFusedGraphExecute", err) + return + } + return outputs +} + +// A dataset that splits the elements of its input into multiple elements. +func ExperimentalUnbatchDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalUnbatchDataset", + Input: []tf.Input{ + input_dataset, }, Attrs: attrs, } @@ -30940,28 +32401,21 @@ func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (ou return op.Output(0) } -// BoostedTreesCreateQuantileStreamResourceAttr is an optional argument to BoostedTreesCreateQuantileStreamResource. -type BoostedTreesCreateQuantileStreamResourceAttr func(optionalAttr) +// ShapeNAttr is an optional argument to ShapeN. +type ShapeNAttr func(optionalAttr) -// BoostedTreesCreateQuantileStreamResourceMaxElements sets the optional max_elements attribute to value. -// -// value: int; The maximum number of data points that can be fed to the stream. -// If not specified, defaults to 1099511627776 -func BoostedTreesCreateQuantileStreamResourceMaxElements(value int64) BoostedTreesCreateQuantileStreamResourceAttr { +// ShapeNOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeNOutType(value tf.DataType) ShapeNAttr { return func(m optionalAttr) { - m["max_elements"] = value + m["out_type"] = value } } -// Create the Resource for Quantile Streams. +// Returns shape of tensors. // -// Arguments: -// quantile_stream_resource_handle: resource; Handle to quantile stream resource. -// epsilon: float; The required approximation error of the stream resource. -// num_streams: int; The number of streams managed by the resource that shares the same epsilon. -// -// Returns the created operation. -func BoostedTreesCreateQuantileStreamResource(scope *Scope, quantile_stream_resource_handle tf.Output, epsilon tf.Output, num_streams tf.Output, optional ...BoostedTreesCreateQuantileStreamResourceAttr) (o *tf.Operation) { +// This operation returns N 1-D integer tensors representing shape of `input[i]s`. +func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) { if scope.Err() != nil { return } @@ -30970,58 +32424,42 @@ func BoostedTreesCreateQuantileStreamResource(scope *Scope, quantile_stream_reso a(attrs) } opspec := tf.OpSpec{ - Type: "BoostedTreesCreateQuantileStreamResource", + Type: "ShapeN", Input: []tf.Input{ - quantile_stream_resource_handle, epsilon, num_streams, + tf.OutputList(input), }, Attrs: attrs, } - return scope.AddOperation(opspec) -} - -// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA. -type ResourceApplyAdagradDAAttr func(optionalAttr) - -// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value. -// -// value: If True, updating of the var and accum tensors will be protected by -// a lock; otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the proximal adagrad scheme. -// -// Arguments: -// var_: Should be from a Variable(). -// gradient_accumulator: Should be from a Variable(). -// gradient_squared_accumulator: Should be from a Variable(). -// grad: The gradient. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// global_step: Training step number. Must be a scalar. -// -// Returns the created operation. -func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) { + op := scope.AddOperation(opspec) if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("ShapeN", err) + return + } + return output +} + +// Returns 0 if the denominator is zero. +// +// +// *NOTE*: `DivNoNan` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func DivNoNan(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return } opspec := tf.OpSpec{ - Type: "ResourceApplyAdagradDA", + Type: "DivNoNan", Input: []tf.Input{ - var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, + x, y, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // Returns x // y element-wise. @@ -31042,6 +32480,23 @@ func FloorDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } +// Generate a sharded filename. The filename is printf formatted as +// +// %s-%05d-of-%05d, basename, shard, num_shards. +func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ShardedFilename", + Input: []tf.Input{ + basename, shard, num_shards, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Returns x / y element-wise for integer types. // // Truncation designates that negative numbers will round fractional quantities @@ -31065,26 +32520,6 @@ func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Returns x / y element-wise for real types. -// -// If `x` and `y` are reals, this will return the floating-point division. -// -// *NOTE*: `Div` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RealDiv", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Scatter `updates` into a new tensor according to `indices`. // // Creates a new tensor by applying sparse `updates` to individual values or @@ -31191,94 +32626,6 @@ func ScatterNd(scope *Scope, indices tf.Output, updates tf.Output, shape tf.Outp return op.Output(0) } -// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. -type ComputeAccidentalHitsAttr func(optionalAttr) - -// ComputeAccidentalHitsSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Computes the ids of the positions in sampled_candidates that match true_labels. -// -// When doing log-odds NCE, the result of this op should be passed through a -// SparseToDense op, then added to the logits of the sampled candidates. This has -// the effect of 'removing' the sampled labels that match the true labels by -// making the classifier sure that they are sampled labels. -// -// Arguments: -// true_classes: The true_classes output of UnpackSparseLabels. -// sampled_candidates: The sampled_candidates output of CandidateSampler. -// num_true: Number of true labels per context. -// -// Returns A vector of indices corresponding to rows of true_candidates.A vector of IDs of positions in sampled_candidates that match a true_label -// for the row with the corresponding index in indices.A vector of the same length as indices and ids, in which each element -// is -FLOAT_MAX. -func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_true": num_true} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ComputeAccidentalHits", - Input: []tf.Input{ - true_classes, sampled_candidates, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// Produces the average pool of the input tensor for quantized types. -// -// Arguments: -// input: 4-D with shape `[batch, height, width, channels]`. -// min_input: The float value that the lowest quantized input value represents. -// max_input: The float value that the highest quantized input value represents. -// ksize: The size of the window for each dimension of the input tensor. -// The length must be 4 to match the number of dimensions of the input. -// strides: The stride of the sliding window for each dimension of the input -// tensor. The length must be 4 to match the number of dimensions of the input. -// padding: The type of padding algorithm to use. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - opspec := tf.OpSpec{ - Type: "QuantizedAvgPool", - Input: []tf.Input{ - input, min_input, max_input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Returns (x - y)(x - y) element-wise. // // *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting @@ -31297,53 +32644,101 @@ func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear. -type QuantizedResizeBilinearAttr func(optionalAttr) +// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap. +type TakeManySparseFromTensorsMapAttr func(optionalAttr) -// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value. // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { +// value: The container name for the `SparseTensorsMap` read by this op. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr { return func(m optionalAttr) { - m["align_corners"] = value + m["container"] = value } } -// QuantizedResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func QuantizedResizeBilinearHalfPixelCenters(value bool) QuantizedResizeBilinearAttr { +// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` read by this op. +// It should not be blank; rather the `shared_name` or unique Operation name +// of the Op that created the original `SparseTensorsMap` should be used. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr { return func(m optionalAttr) { - m["half_pixel_centers"] = value + m["shared_name"] = value } } -// Resize quantized `images` to `size` using quantized bilinear interpolation. +// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. // -// Input images and output images must be quantized types. +// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where +// `N` is the minibatch size and the rows correspond to the output handles of +// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the +// original `SparseTensor` objects that went into the given input ops must all +// match. When the final `SparseTensor` is created, it has rank one +// higher than the ranks of the incoming `SparseTensor` objects +// (they have been concatenated along a new row dimension on the left). +// +// The output `SparseTensor` object's shape values for all dimensions but the +// first are the max across the input `SparseTensor` objects' shape values +// for the corresponding dimensions. Its first shape value is `N`, the minibatch +// size. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the handles represent an input, which is a `[2, 3]` matrix +// representing two original `SparseTensor` objects: +// +// ``` +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// ``` +// +// and +// +// ``` +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// ``` +// +// then the final `SparseTensor` will be: +// +// ``` +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// ``` // // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. +// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. +// Shape: `[N]`. +// dtype: The `dtype` of the `SparseTensor` objects stored in the +// `SparseTensorsMap`. // -// -// -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) { +// Returns 2-D. The `indices` of the minibatch `SparseTensor`.1-D. The `values` of the minibatch `SparseTensor`.1-D. The `shape` of the minibatch `SparseTensor`. +func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} + attrs := map[string]interface{}{"dtype": dtype} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedResizeBilinear", + Type: "TakeManySparseFromTensorsMap", Input: []tf.Input{ - images, size, min, max, + sparse_handles, }, Attrs: attrs, } @@ -31351,25 +32746,33 @@ func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min return op.Output(0), op.Output(1), op.Output(2) } -// Returns element-wise integer closest to x. -// -// If the result is midway between two representable values, -// the even representable is chosen. -// For example: -// -// ``` -// rint(-1.5) ==> -2.0 -// rint(0.5000001) ==> 1.0 -// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] -// ``` -func Rint(scope *Scope, x tf.Output) (y tf.Output) { +// Returns 0 if x == 0, and x * log(y) otherwise, elementwise. +func Xlogy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Rint", + Type: "Xlogy", Input: []tf.Input{ - x, + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the min of x and y (i.e. x < y ? x : y) element-wise. +// +// *NOTE*: `Minimum` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Minimum", + Input: []tf.Input{ + x, y, }, } op := scope.AddOperation(opspec) @@ -31397,108 +32800,6 @@ func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Computes the Eigen Decomposition of a batch of square self-adjoint matrices. -// -// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead. -// -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices, with the same constraints as the single matrix -// SelfAdjointEig. -// -// The result is a [..., M+1, M] matrix with [..., 0,:] containing the -// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues -// are sorted in non-decreasing order. -// -// Arguments: -// input: Shape is `[..., M, M]`. -// -// Returns Shape is `[..., M+1, M]`. -func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SelfAdjointEig", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount). -// -// For each entry in `x`, calculates the number of `1` (on) bits in the binary -// representation of that entry. -// -// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into -// `int32` or `int64` and perform the bitcount on the result, than to feed in -// 8- or 16-bit inputs and then aggregate the resulting counts. -func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "PopulationCount", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is -// -// true, this follows Python semantics in that the result here is consistent -// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. -// -// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "FloorMod", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Selects the k nearest centers for each point. -// -// Rows of points are assumed to be input points. Rows of centers are assumed to be -// the list of candidate centers. For each point, the k centers that have least L2 -// distance to it are computed. -// -// Arguments: -// points: Matrix of shape (n, d). Rows are assumed to be input points. -// centers: Matrix of shape (m, d). Rows are assumed to be centers. -// k: Number of nearest centers to return for each point. If k is larger than m, then -// only m centers are returned. -// -// Returns Matrix of shape (n, min(m, k)). Each row contains the indices of the centers -// closest to the corresponding point, ordered by increasing distance.Matrix of shape (n, min(m, k)). Each row contains the squared L2 distance to the -// corresponding center in nearest_center_indices. -func NearestNeighbors(scope *Scope, points tf.Output, centers tf.Output, k tf.Output) (nearest_center_indices tf.Output, nearest_center_distances tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "NearestNeighbors", - Input: []tf.Input{ - points, centers, k, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - // Returns element-wise remainder of division. This emulates C semantics in that // // the result here is consistent with a truncating divide. E.g. `truncate(x / y) * @@ -31520,165 +32821,44 @@ func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } -// Real-valued fast Fourier transform. -// -// Computes the 1-dimensional discrete Fourier transform of a real-valued signal -// over the inner-most dimension of `input`. -// -// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the -// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term, -// followed by the `fft_length / 2` positive-frequency terms. -// -// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the -// corresponding dimension of `input`, the dimension is cropped. If it is larger, -// the dimension is padded with zeros. -// -// Arguments: -// input: A float32 tensor. -// fft_length: An int32 tensor of shape [1]. The FFT length. -// -// Returns A complex64 tensor of the same rank as `input`. The inner-most -// dimension of `input` is replaced with the `fft_length / 2 + 1` unique -// frequency components of its 1D Fourier transform. -// -// @compatibility(numpy) -// Equivalent to np.fft.rfft -// @end_compatibility -func RFFT(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RFFT", - Input: []tf.Input{ - input, fft_length, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. +type ResourceSparseApplyAdagradAttr func(optionalAttr) -// EnqueueTPUEmbeddingSparseTensorBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseTensorBatch. -type EnqueueTPUEmbeddingSparseTensorBatchAttr func(optionalAttr) - -// EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal sets the optional device_ordinal attribute to value. -// -// value: The TPU device to use. Should be >= 0 and less than the number -// of TPU cores in the task on which the node is placed. -// If not specified, defaults to -1 -func EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { - return func(m optionalAttr) { - m["device_ordinal"] = value - } -} - -// EnqueueTPUEmbeddingSparseTensorBatchCombiners sets the optional combiners attribute to value. -// -// value: A list of string scalars, one for each embedding table that specify -// how to normalize the embedding activations after weighted summation. -// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have -// the sum of the weights be 0 for 'mean' or the sum of the squared weights be -// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for -// all tables. -// If not specified, defaults to <> -func EnqueueTPUEmbeddingSparseTensorBatchCombiners(value []string) EnqueueTPUEmbeddingSparseTensorBatchAttr { - return func(m optionalAttr) { - m["combiners"] = value - } -} - -// EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths sets the optional max_sequence_lengths attribute to value. -// If not specified, defaults to <> -func EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths(value []int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { - return func(m optionalAttr) { - m["max_sequence_lengths"] = value - } -} - -// Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). -// -// sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond -// to the ith feature. table_ids[i] indicates which embedding table to look up ith -// feature. -// -// The tensors at corresponding positions in the three input lists (sample_indices, -// embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 -// with dim_size() equal to the total number of lookups into the table described by -// the corresponding feature. -// -// Arguments: -// sample_indices: A list of rank 1 Tensors specifying the training example to -// which the corresponding embedding_indices and aggregation_weights values -// belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse(). -// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. -// It corresponds to sp_ids.values in embedding_lookup_sparse(). -// aggregation_weights: A list of rank 1 Tensors containing per training example -// aggregation weights. It corresponds to sp_weights.values in -// embedding_lookup_sparse(). -// mode_override: A string input that overrides the mode specified in the -// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', -// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set -// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. -// table_ids: A list of integers specifying the identifier of the embedding table -// (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the -// corresponding input. The ith input is looked up using table_ids[i]. The size -// of the table_ids list must be equal to that of sample_indices, -// embedding_indices and aggregation_weights. -// -// Returns the created operation. -func EnqueueTPUEmbeddingSparseTensorBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, table_ids []int64, optional ...EnqueueTPUEmbeddingSparseTensorBatchAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"table_ids": table_ids} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EnqueueTPUEmbeddingSparseTensorBatch", - Input: []tf.Input{ - tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. -type ResourceApplyFtrlAttr func(optionalAttr) - -// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value. +// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. // // value: If `True`, updating of the var and accum tensors will be protected // by a lock; otherwise the behavior is undefined, but may exhibit less // contention. // If not specified, defaults to false -func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { +func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { return func(m optionalAttr) { m["use_locking"] = value } } -// Update '*var' according to the Ftrl-proximal scheme. +// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. // -// accum_new = accum + grad * grad -// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var -// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 -// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 -// accum = accum_new +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) // // Arguments: // var_: Should be from a Variable(). // accum: Should be from a Variable(). -// linear: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. // grad: The gradient. -// lr: Scaling factor. Must be a scalar. -// l1: L1 regulariation. Must be a scalar. -// l2: L2 regulariation. Must be a scalar. -// lr_power: Scaling factor. Must be a scalar. +// indices: A vector of indices into the first dimension of var and accum. // // Returns the created operation. -func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) { +func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -31687,15 +32867,43 @@ func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf. a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceApplyFtrl", + Type: "ResourceSparseApplyAdagrad", Input: []tf.Input{ - var_, accum, linear, grad, lr, l1, l2, lr_power, + var_, accum, lr, grad, indices, }, Attrs: attrs, } return scope.AddOperation(opspec) } +// Compute the upper regularized incomplete Gamma function `Q(a, x)`. +// +// The upper regularized incomplete Gamma function is defined as: +// +// \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\) +// +// where +// +// \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\) +// +// is the upper incomplete Gama function. +// +// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete +// Gamma function. +func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Igammac", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Compute the lower regularized incomplete Gamma function `P(a, x)`. // // The lower regularized incomplete Gamma function is defined as: @@ -31725,671 +32933,17 @@ func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { return op.Output(0) } -// Compute the Hurwitz zeta function \\(\zeta(x, q)\\). +// Deprecated. Use TensorArrayScatterV3 // -// The Hurwitz zeta function is defined as: -// -// -// \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\) -func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) { +// DEPRECATED at GraphDef version 26: Use TensorArrayScatterV3 +func TensorArrayScatterV2(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Zeta", + Type: "TensorArrayScatterV2", Input: []tf.Input{ - x, q, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Compute the polygamma function \\(\psi^{(n)}(x)\\). -// -// The polygamma function is defined as: -// -// -// \\(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\\) -// -// where \\(\psi(x)\\) is the digamma function. -// The polygamma function is defined only for non-negative integer orders \\a\\. -func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Polygamma", - Input: []tf.Input{ - a, x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of (x > y) element-wise. -// -// *NOTE*: `Greater` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Greater", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that uses a custom thread pool to compute `input_dataset`. -// -// Arguments: -// -// num_threads: Identifies the number of threads to use for the private threadpool. -// -// -func ExperimentalPrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalPrivateThreadPoolDataset", - Input: []tf.Input{ - input_dataset, num_threads, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of (x >= y) element-wise. -// -// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "GreaterEqual", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the sum along sparse segments of a tensor. -// -// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is -// misisng, the `output` tensor at that position will be zeroed. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/sparse#Segmentation) -// for an explanation of segments. -// -// For example: -// -// ```python -// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) -// -// tf.sparse_segment_sum_with_num_segments( -// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3) -// # => [[0 0 0 0] -// # [0 0 0 0] -// # [0 0 0 0]] -// -// tf.sparse_segment_sum_with_num_segments(c, -// tf.constant([0, 1]), -// tf.constant([0, 2], -// num_segments=4)) -// # => [[ 1 2 3 4] -// # [ 0 0 0 0] -// # [-1 -2 -3 -4] -// # [ 0 0 0 0]] -// ``` -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. -// num_segments: Should equal the number of distinct segment IDs. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `num_segments`. -func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSumWithNumSegments", - Input: []tf.Input{ - data, indices, segment_ids, num_segments, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Reshapes a tensor. -// -// Given `tensor`, this operation returns a tensor that has the same values -// as `tensor` with shape `shape`. -// -// If one component of `shape` is the special value -1, the size of that dimension -// is computed so that the total size remains constant. In particular, a `shape` -// of `[-1]` flattens into 1-D. At most one component of `shape` can be -1. -// -// If `shape` is 1-D or higher, then the operation returns a tensor with shape -// `shape` filled with the values of `tensor`. In this case, the number of elements -// implied by `shape` must be the same as the number of elements in `tensor`. -// -// For example: -// -// ``` -// # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] -// # tensor 't' has shape [9] -// reshape(t, [3, 3]) ==> [[1, 2, 3], -// [4, 5, 6], -// [7, 8, 9]] -// -// # tensor 't' is [[[1, 1], [2, 2]], -// # [[3, 3], [4, 4]]] -// # tensor 't' has shape [2, 2, 2] -// reshape(t, [2, 4]) ==> [[1, 1, 2, 2], -// [3, 3, 4, 4]] -// -// # tensor 't' is [[[1, 1, 1], -// # [2, 2, 2]], -// # [[3, 3, 3], -// # [4, 4, 4]], -// # [[5, 5, 5], -// # [6, 6, 6]]] -// # tensor 't' has shape [3, 2, 3] -// # pass '[-1]' to flatten 't' -// reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6] -// -// # -1 can also be used to infer the shape -// -// # -1 is inferred to be 9: -// reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], -// [4, 4, 4, 5, 5, 5, 6, 6, 6]] -// # -1 is inferred to be 2: -// reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], -// [4, 4, 4, 5, 5, 5, 6, 6, 6]] -// # -1 is inferred to be 3: -// reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1], -// [2, 2, 2], -// [3, 3, 3]], -// [[4, 4, 4], -// [5, 5, 5], -// [6, 6, 6]]] -// -// # tensor 't' is [7] -// # shape `[]` reshapes to a scalar -// reshape(t, []) ==> 7 -// ``` -// -// Arguments: -// -// shape: Defines the shape of the output tensor. -func Reshape(scope *Scope, tensor tf.Output, shape tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Reshape", - Input: []tf.Input{ - tensor, shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// EncodeProtoAttr is an optional argument to EncodeProto. -type EncodeProtoAttr func(optionalAttr) - -// EncodeProtoDescriptorSource sets the optional descriptor_source attribute to value. -// If not specified, defaults to "local://" -func EncodeProtoDescriptorSource(value string) EncodeProtoAttr { - return func(m optionalAttr) { - m["descriptor_source"] = value - } -} - -// The op serializes protobuf messages provided in the input tensors. -// -// The types of the tensors in `values` must match the schema for the -// fields specified in `field_names`. All the tensors in `values` must -// have a common shape prefix, *batch_shape*. -// -// The `sizes` tensor specifies repeat counts for each field. The repeat -// count (last dimension) of a each tensor in `values` must be greater -// than or equal to corresponding repeat count in `sizes`. -// -// A `message_type` name must be provided to give context for the field -// names. The actual message descriptor can be looked up either in the -// linked-in descriptor pool or a filename provided by the caller using -// the `descriptor_source` attribute. -// -// The `descriptor_source` attribute selects a source of protocol -// descriptors to consult when looking up `message_type`. This may be a -// filename containing a serialized `FileDescriptorSet` message, -// or the special value `local://`, in which case only descriptors linked -// into the code will be searched; the filename can be on any filesystem -// accessible to TensorFlow. -// -// You can build a `descriptor_source` file using the `--descriptor_set_out` -// and `--include_imports` options to the protocol compiler `protoc`. -// -// The `local://` database only covers descriptors linked into the -// code via C++ libraries, not Python imports. You can link in a proto descriptor -// by creating a cc_library target with alwayslink=1. -// -// There are a few special cases in the value mapping: -// -// Submessage and group fields must be pre-serialized as TensorFlow strings. -// -// TensorFlow lacks support for unsigned int64s, so they must be -// represented as `tf.int64` with the same twos-complement bit pattern -// (the obvious way). -// -// Unsigned int32 values can be represented exactly with `tf.int64`, or -// with sign wrapping if the input is of type `tf.int32`. -// -// Arguments: -// sizes: Tensor of int32 with shape `[batch_shape, len(field_names)]`. -// values: List of tensors containing values for the corresponding field. -// field_names: List of strings containing proto field names. -// message_type: Name of the proto message type to decode. -// -// Returns Tensor of serialized protos with shape `batch_shape`. -func EncodeProto(scope *Scope, sizes tf.Output, values []tf.Output, field_names []string, message_type string, optional ...EncodeProtoAttr) (bytes tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"field_names": field_names, "message_type": message_type} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "EncodeProto", - Input: []tf.Input{ - sizes, tf.OutputList(values), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MapClearAttr is an optional argument to MapClear. -type MapClearAttr func(optionalAttr) - -// MapClearCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapClearCapacity(value int64) MapClearAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapClearMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapClearMemoryLimit(value int64) MapClearAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapClearContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapClearContainer(value string) MapClearAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapClearSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapClearSharedName(value string) MapClearAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op removes all elements in the underlying container. -// -// Returns the created operation. -func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapClear", - - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2. -type QueueEnqueueManyV2Attr func(optionalAttr) - -// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value. -// -// value: If the queue is too full, this operation will block for up -// to timeout_ms milliseconds. -// Note: This option is not supported yet. -// If not specified, defaults to -1 -func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr { - return func(m optionalAttr) { - m["timeout_ms"] = value - } -} - -// Enqueues zero or more tuples of one or more tensors in the given queue. -// -// This operation slices each component tensor along the 0th dimension to -// make multiple queue elements. All of the tuple components must have the -// same size in the 0th dimension. -// -// The components input has k elements, which correspond to the components of -// tuples stored in the given queue. -// -// N.B. If the queue is full, this operation will block until the given -// elements have been enqueued (or 'timeout_ms' elapses, if specified). -// -// Arguments: -// handle: The handle to a queue. -// components: One or more tensors from which the enqueued tensors should -// be taken. -// -// Returns the created operation. -func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QueueEnqueueManyV2", - Input: []tf.Input{ - handle, tf.OutputList(components), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdagradParametersGradAccumDebug. -type RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve Adagrad embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the Adagrad optimization algorithm.Parameter accumulators updated by the Adagrad optimization algorithm.Parameter gradient_accumulators updated by the Adagrad optimization algorithm. -func RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// StatelessMultinomialAttr is an optional argument to StatelessMultinomial. -type StatelessMultinomialAttr func(optionalAttr) - -// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value. -// If not specified, defaults to DT_INT64 -func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr { - return func(m optionalAttr) { - m["output_dtype"] = value - } -} - -// Draws samples from a multinomial distribution. -// -// Arguments: -// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` -// represents the unnormalized log probabilities for all classes. -// num_samples: 0-D. Number of independent samples to draw for each row slice. -// seed: 2 seeds (shape [2]). -// -// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` -// contains the drawn class labels with range `[0, num_classes)`. -func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatelessMultinomial", - Input: []tf.Input{ - logits, num_samples, seed, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of (x == y) element-wise. -// -// *NOTE*: `Equal` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -// -// ```python -// x = tf.constant([2, 4]) -// y = tf.constant(2) -// tf.math.equal(x, y) ==> array([True, False]) -// -// x = tf.constant([2, 4]) -// y = tf.constant([2, 4]) -// tf.math.equal(x, y) ==> array([True, True]) -// ``` -func Equal(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Equal", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ApproximateEqualAttr is an optional argument to ApproximateEqual. -type ApproximateEqualAttr func(optionalAttr) - -// ApproximateEqualTolerance sets the optional tolerance attribute to value. -// If not specified, defaults to 1e-05 -func ApproximateEqualTolerance(value float32) ApproximateEqualAttr { - return func(m optionalAttr) { - m["tolerance"] = value - } -} - -// Returns the truth value of abs(x-y) < tolerance element-wise. -func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ApproximateEqual", - Input: []tf.Input{ - x, y, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Says whether the targets are in the top `K` predictions. -// -// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the -// prediction for the target class is among the top `k` predictions among -// all predictions for example `i`. Note that the behavior of `InTopK` differs -// from the `TopK` op in its handling of ties; if multiple classes have the -// same prediction value and straddle the top-`k` boundary, all of those -// classes are considered to be in the top `k`. -// -// More formally, let -// -// \\(predictions_i\\) be the predictions for all classes for example `i`, -// \\(targets_i\\) be the target class for example `i`, -// \\(out_i\\) be the output for example `i`, -// -// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ -// -// Arguments: -// predictions: A `batch_size` x `classes` tensor. -// targets: A `batch_size` vector of class ids. -// k: Number of top elements to look at for computing precision. -// -// Returns Computed precision at `k` as a `bool Tensor`. -func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "InTopKV2", - Input: []tf.Input{ - predictions, targets, k, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes hyperbolic sine of x element-wise. -func Sinh(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Sinh", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of NOT x element-wise. -func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LogicalNot", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a Dataset that returns pseudorandom numbers. -// -// Arguments: -// seed: A scalar seed for the random number generator. If either seed or -// seed2 is set to be non-zero, the random number generator is seeded -// by the given seed. Otherwise, a random seed is used. -// seed2: A second scalar seed to avoid seed collision. -// -// -func ExperimentalRandomDataset(scope *Scope, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalRandomDataset", - Input: []tf.Input{ - seed, seed2, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the truth value of x AND y element-wise. -// -// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LogicalAnd", - Input: []tf.Input{ - x, y, + handle, indices, value, flow_in, }, } op := scope.AddOperation(opspec) @@ -32476,55 +33030,423 @@ func QuantizedMatMulWithBias(scope *Scope, a tf.Output, b tf.Output, bias tf.Out return op.Output(0), op.Output(1), op.Output(2) } -// Restores tensors from a V2 checkpoint. +// Computes arctangent of `y/x` element-wise, respecting signs of the arguments. // -// For backward compatibility with the V1 format, this Op currently allows -// restoring from a V1 checkpoint as well: -// - This Op first attempts to find the V2 index file pointed to by "prefix", and -// if found proceed to read it as a V2 checkpoint; -// - Otherwise the V1 read path is invoked. -// Relying on this behavior is not recommended, as the ability to fall back to read -// V1 might be deprecated and eventually removed. -// -// By default, restores the named tensors in full. If the caller wishes to restore -// specific slices of stored tensors, "shape_and_slices" should be non-empty -// strings and correspondingly well-formed. -// -// Callers must ensure all the named tensors are indeed stored in the checkpoint. -// -// Arguments: -// prefix: Must have a single element. The prefix of a V2 checkpoint. -// tensor_names: shape {N}. The names of the tensors to be restored. -// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. -// Empty strings indicate that they are non-partitioned tensors. -// dtypes: shape {N}. The list of expected dtype for the tensors. Must match -// those stored in the checkpoint. -// -// Returns shape {N}. The restored tensors, whose shapes are read from the -// checkpoint directly. -func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { +// This is the angle \( \theta \in [-\pi, \pi] \) such that +// \[ x = r \cos(\theta) \] +// and +// \[ y = r \sin(\theta) \] +// where \(r = \sqrt(x^2 + y^2) \). +func Atan2(scope *Scope, y tf.Output, x tf.Output) (z tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} opspec := tf.OpSpec{ - Type: "RestoreV2", + Type: "Atan2", Input: []tf.Input{ - prefix, tensor_names, shape_and_slices, + y, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x < y) element-wise. +// +// *NOTE*: `Less` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Less", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniqueWithCountsV2Attr is an optional argument to UniqueWithCountsV2. +type UniqueWithCountsV2Attr func(optionalAttr) + +// UniqueWithCountsV2OutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func UniqueWithCountsV2OutIdx(value tf.DataType) UniqueWithCountsV2Attr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Finds unique elements along an axis of a tensor. +// +// This operation either returns a tensor `y` containing unique elements +// along the `axis` of a tensor. The returned unique elements is sorted +// in the same order as they occur along `axis` in `x`. +// This operation also returns a tensor `idx` and a tensor `count` +// that are the same size as the number of the elements in `x` along the +// `axis` dimension. The `idx` contains the index in the unique output `y` +// and the `count` contains the count in the unique output `y`. +// In other words, for an `1-D` tensor `x` with `axis = None: +// +// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` +// +// For example: +// +// ``` +// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] +// y, idx, count = unique_with_counts(x) +// y ==> [1, 2, 4, 7, 8] +// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +// count ==> [2, 1, 3, 1, 2] +// ``` +// +// For an `2-D` tensor `x` with `axis = 0`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx, count = unique_with_counts(x, axis=0) +// y ==> [[1, 0, 0], +// [2, 0, 0]] +// idx ==> [0, 0, 1] +// count ==> [2, 1] +// ``` +// +// For an `2-D` tensor `x` with `axis = 1`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx, count = unique_with_counts(x, axis=1) +// y ==> [[1, 0], +// [1, 0], +// [2, 0]] +// idx ==> [0, 1, 1] +// count ==> [1, 2] +// ``` +// +// Arguments: +// x: A `Tensor`. +// axis: A `Tensor` of type `int32` (default: None). The axis of the Tensor to +// find the unique elements. +// +// Returns A `Tensor`. Unique elements along the `axis` of `Tensor` x.A 1-D Tensor. Has the same type as x that contains the index of each +// value of x in the output y.A 1-D Tensor. The count of each value of x in the output y. +func UniqueWithCountsV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueWithCountsV2Attr) (y tf.Output, idx tf.Output, count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UniqueWithCountsV2", + Input: []tf.Input{ + x, axis, }, Attrs: attrs, } op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatefulStandardNormalV2Attr is an optional argument to StatefulStandardNormalV2. +type StatefulStandardNormalV2Attr func(optionalAttr) + +// StatefulStandardNormalV2Dtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulStandardNormalV2Dtype(value tf.DataType) StatefulStandardNormalV2Attr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns A tensor of the specified shape filled with random normal values. +func StatefulStandardNormalV2(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulStandardNormalV2Attr) (output tf.Output) { if scope.Err() != nil { return } - var idx int - var err error - if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { - scope.UpdateErr("RestoreV2", err) + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulStandardNormalV2", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x <= y) element-wise. +// +// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { return } - return tensors + opspec := tf.OpSpec{ + Type: "LessEqual", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the minimum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the minimum such that: +// +// \\(output_i = \min_{j...} data_[j...]\\) where min is over tuples `j...` such +// that `segment_ids[j...] == i`. +// +// If the minimum is empty for a given segment ID `i`, it outputs the largest +// possible value for the specific numeric type, +// `output[i] = numeric_limits::max()`. +// +// For example: +// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 1, 2, 2, 1], +// # [5, 6, 7, 8]] +// ``` +// +// If the given segment ID `i` is negative, then the corresponding value is +// dropped, and will not be included in the result. +// +// Arguments: +// +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentMin", + Input: []tf.Input{ + data, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x >= y) element-wise. +// +// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GreaterEqual", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x == y) element-wise. +// +// *NOTE*: `Equal` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// ```python +// x = tf.constant([2, 4]) +// y = tf.constant(2) +// tf.math.equal(x, y) ==> array([True, False]) +// +// x = tf.constant([2, 4]) +// y = tf.constant([2, 4]) +// tf.math.equal(x, y) ==> array([True, True]) +// ``` +func Equal(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Equal", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains `rate` elements from the `input_dataset`. +// +// Arguments: +// +// rate: A scalar representing the sample rate of elements from the `input_dataset` +// that should be taken. +// seed: A scalar representing seed of random number generator. +// seed2: A scalar representing seed2 of random number generator. +// +// +func SamplingDataset(scope *Scope, input_dataset tf.Output, rate tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SamplingDataset", + Input: []tf.Input{ + input_dataset, rate, seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x != y) element-wise. +// +// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func NotEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NotEqual", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ApproximateEqualAttr is an optional argument to ApproximateEqual. +type ApproximateEqualAttr func(optionalAttr) + +// ApproximateEqualTolerance sets the optional tolerance attribute to value. +// If not specified, defaults to 1e-05 +func ApproximateEqualTolerance(value float32) ApproximateEqualAttr { + return func(m optionalAttr) { + m["tolerance"] = value + } +} + +// Returns the truth value of abs(x-y) < tolerance element-wise. +func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ApproximateEqual", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a Dataset that returns pseudorandom numbers. +// +// Arguments: +// seed: A scalar seed for the random number generator. If either seed or +// seed2 is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// +// +func ExperimentalRandomDataset(scope *Scope, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalRandomDataset", + Input: []tf.Input{ + seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of x AND y element-wise. +// +// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogicalAnd", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Return the shape of s0 op s1 with broadcast. +// +// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the +// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. +func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastArgs", + Input: []tf.Input{ + s0, s1, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } // Returns the truth value of x OR y element-wise. @@ -32545,6 +33467,492 @@ func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } +// Computes the sign and the log of the absolute value of the determinant of +// +// one or more square matrices. +// +// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions +// form square matrices. The outputs are two tensors containing the signs and +// absolute values of the log determinants for all N input submatrices +// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant). +// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU +// is the LU decomposition of the input and P is the corresponding +// permutation matrix. +// +// Arguments: +// input: Shape is `[N, M, M]`. +// +// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants +// of the N input matrices. Shape is `[N]`. +func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogMatrixDeterminant", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ArgMinAttr is an optional argument to ArgMin. +type ArgMinAttr func(optionalAttr) + +// ArgMinOutputType sets the optional output_type attribute to value. +// If not specified, defaults to DT_INT64 +func ArgMinOutputType(value tf.DataType) ArgMinAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Returns the index with the smallest value across dimensions of a tensor. +// +// Note that in case of ties the identity of the return value is not guaranteed. +// +// Usage: +// ```python +// import tensorflow as tf +// a = [1, 10, 26.9, 2.8, 166.32, 62.3] +// b = tf.math.argmin(input = a) +// c = tf.keras.backend.eval(b) +// # c = 0 +// # here a[0] = 1 which is the smallest element of a across axis 0 +// ``` +// +// Arguments: +// +// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. +// Describes which dimension of the input Tensor to reduce across. For vectors, +// use dimension = 0. +func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ArgMin", + Input: []tf.Input{ + input, dimension, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes reciprocal of square root of x element-wise. +// +// I.e., \\(y = 1 / \sqrt{x}\\). +func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Rsqrt", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Draw bounding boxes on a batch of images. +// +// Outputs a copy of `images` but draws on top of the pixels zero or more bounding +// boxes specified by the locations in `boxes`. The coordinates of the each +// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, if an image is 100 x 200 pixels (height x width) and the bounding +// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of +// the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). +// +// Parts of the bounding box may fall outside the image. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. +// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding +// boxes. +// colors: 2-D. A list of RGBA colors to cycle through for the boxes. +// +// Returns 4-D with the same shape as `images`. The batch of input images with +// bounding boxes drawn on the images. +func DrawBoundingBoxesV2(scope *Scope, images tf.Output, boxes tf.Output, colors tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DrawBoundingBoxesV2", + Input: []tf.Input{ + images, boxes, colors, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Sends `input` to all devices that are connected to the output. +// +// Sends `input` to all devices that are connected to the output. +// +// The graph should be constructed so that all ops connected to the output have a +// valid device assignment, and the op itself is assigned one of these devices. +// +// input: The input to the broadcast. +// output: The same as input. +// shape: The shape of the input tensor. +// +func NcclBroadcast(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "NcclBroadcast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ImagAttr is an optional argument to Imag. +type ImagAttr func(optionalAttr) + +// ImagTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ImagTout(value tf.DataType) ImagAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the imaginary part of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the imaginary part of each element in `input`. All +// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part returned by this operation. +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.imag(input) ==> [4.75, 5.75] +// ``` +func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Imag", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatMulAttr is an optional argument to MatMul. +type MatMulAttr func(optionalAttr) + +// MatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, "a" is transposed before multiplication. +// If not specified, defaults to false +func MatMulTransposeA(value bool) MatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// MatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, "b" is transposed before multiplication. +// If not specified, defaults to false +func MatMulTransposeB(value bool) MatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// Multiply the matrix "a" by the matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of +// "a" (after being transposed if transpose_a is true) must match the +// outer dimension of "b" (after being transposed if transposed_b is +// true). +// +// *Note*: The default kernel implementation for MatMul on GPUs uses +// cublas. +func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the mean along sparse segments of a tensor. +// +// See `tf.sparse.segment_sum` for usage examples. +// +// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first +// dimension, selecting a subset of dimension 0, specified by `indices`. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMean", + Input: []tf.Input{ + data, indices, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for SparseSegmentSqrtN. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentSqrtN op. +// indices: indices passed to the corresponding SparseSegmentSqrtN op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op. +func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtNGrad", + Input: []tf.Input{ + grad, indices, segment_ids, output_dim0, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AnyAttr is an optional argument to Any. +type AnyAttr func(optionalAttr) + +// AnyKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AnyKeepDims(value bool) AnyAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical or" of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Any", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EuclideanNormAttr is an optional argument to EuclideanNorm. +type EuclideanNormAttr func(optionalAttr) + +// EuclideanNormKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func EuclideanNormKeepDims(value bool) EuclideanNormAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the euclidean norm of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func EuclideanNorm(scope *Scope, input tf.Output, axis tf.Output, optional ...EuclideanNormAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EuclideanNorm", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ProdAttr is an optional argument to Prod. +type ProdAttr func(optionalAttr) + +// ProdKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func ProdKeepDims(value bool) ProdAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the product of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Prod", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MinAttr is an optional argument to Min. +type MinAttr func(optionalAttr) + +// MinKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MinKeepDims(value bool) MinAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the minimum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Min", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Selects elements from `x` or `y`, depending on `condition`. // // The `x`, and `y` tensors must all have the same shape, and the @@ -32608,85 +34016,45 @@ func Select(scope *Scope, condition tf.Output, x tf.Output, y tf.Output) (output return op.Output(0) } -// SparseMatMulAttr is an optional argument to SparseMatMul. -type SparseMatMulAttr func(optionalAttr) - -// SparseMatMulTransposeA sets the optional transpose_a attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeA(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// SparseMatMulTransposeB sets the optional transpose_b attribute to value. -// If not specified, defaults to false -func SparseMatMulTransposeB(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["a_is_sparse"] = value - } -} - -// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. -// If not specified, defaults to false -func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { - return func(m optionalAttr) { - m["b_is_sparse"] = value - } -} - -// Multiply matrix "a" by matrix "b". +// JPEG encode input image with provided compression quality. // -// The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not -// `SparseTensor`s. This op is optimized for the case where at least one of "a" or -// "b" is sparse, in the sense that they have a large proportion of zero values. -// The breakeven for using this versus a dense matrix multiply on one platform was -// 30% zero values in the sparse matrix. +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// `quality` is an int32 jpeg compression quality value between 0 and 100. // -// The gradient computation of this operation will only take advantage of sparsity -// in the input gradient when that gradient comes from a Relu. -func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { +// +// Arguments: +// images: Images to adjust. At least 3-D. +// quality: An int quality to encode to. +// +// Returns 0-D. JPEG-encoded image. +func EncodeJpegVariableQuality(scope *Scope, images tf.Output, quality tf.Output) (contents tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "SparseMatMul", + Type: "EncodeJpegVariableQuality", Input: []tf.Input{ - a, b, + images, quality, }, - Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// EuclideanNormAttr is an optional argument to EuclideanNorm. -type EuclideanNormAttr func(optionalAttr) +// MaxAttr is an optional argument to Max. +type MaxAttr func(optionalAttr) -// EuclideanNormKeepDims sets the optional keep_dims attribute to value. +// MaxKeepDims sets the optional keep_dims attribute to value. // // value: If true, retain reduced dimensions with length 1. // If not specified, defaults to false -func EuclideanNormKeepDims(value bool) EuclideanNormAttr { +func MaxKeepDims(value bool) MaxAttr { return func(m optionalAttr) { m["keep_dims"] = value } } -// Computes the euclidean norm of elements across dimensions of a tensor. +// Computes the maximum of elements across dimensions of a tensor. // // Reduces `input` along the dimensions given in `axis`. Unless // `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in @@ -32699,7 +34067,7 @@ func EuclideanNormKeepDims(value bool) EuclideanNormAttr { // `[-rank(input), rank(input))`. // // Returns The reduced tensor. -func EuclideanNorm(scope *Scope, input tf.Output, axis tf.Output, optional ...EuclideanNormAttr) (output tf.Output) { +func Max(scope *Scope, input tf.Output, axis tf.Output, optional ...MaxAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -32708,7 +34076,7 @@ func EuclideanNorm(scope *Scope, input tf.Output, axis tf.Output, optional ...Eu a(attrs) } opspec := tf.OpSpec{ - Type: "EuclideanNorm", + Type: "Max", Input: []tf.Input{ input, axis, }, @@ -32718,79 +34086,32 @@ func EuclideanNorm(scope *Scope, input tf.Output, axis tf.Output, optional ...Eu return op.Output(0) } -// RetrieveTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to RetrieveTPUEmbeddingMDLAdagradLightParameters. -type RetrieveTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) +// EnqueueTPUEmbeddingIntegerBatchAttr is an optional argument to EnqueueTPUEmbeddingIntegerBatch. +type EnqueueTPUEmbeddingIntegerBatchAttr func(optionalAttr) -// RetrieveTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. +// EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. // If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingMDLAdagradLightParametersTableId(value int64) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { +func EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingIntegerBatchAttr { return func(m optionalAttr) { - m["table_id"] = value + m["device_ordinal"] = value } } -// RetrieveTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingMDLAdagradLightParametersTableName(value string) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve MDL Adagrad Light embedding parameters. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the MDL Adagrad Light optimization algorithm.Parameter accumulators updated by the MDL Adagrad Light optimization algorithm.Parameter weights updated by the MDL Adagrad Light optimization algorithm.Parameter benefits updated by the MDL Adagrad Light optimization algorithm. -func RetrieveTPUEmbeddingMDLAdagradLightParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMDLAdagradLightParametersAttr) (parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingMDLAdagradLightParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// MeanAttr is an optional argument to Mean. -type MeanAttr func(optionalAttr) - -// MeanKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func MeanKeepDims(value bool) MeanAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the mean of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. +// An op that enqueues a list of input batch tensors to TPUEmbedding. // // Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. +// batch: A list of 1D tensors, one for each embedding table, containing the +// indices into the tables. +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. // -// Returns The reduced tensor. -func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) (output tf.Output) { +// Returns the created operation. +func EnqueueTPUEmbeddingIntegerBatch(scope *Scope, batch []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingIntegerBatchAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -32799,247 +34120,37 @@ func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) ( a(attrs) } opspec := tf.OpSpec{ - Type: "Mean", + Type: "EnqueueTPUEmbeddingIntegerBatch", Input: []tf.Input{ - input, axis, + tf.OutputList(batch), mode_override, }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0) + return scope.AddOperation(opspec) } -// ProdAttr is an optional argument to Prod. -type ProdAttr func(optionalAttr) - -// ProdKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func ProdKeepDims(value bool) ProdAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the product of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Prod", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SpaceToDepthAttr is an optional argument to SpaceToDepth. -type SpaceToDepthAttr func(optionalAttr) - -// SpaceToDepthDataFormat sets the optional data_format attribute to value. -// If not specified, defaults to "NHWC" -func SpaceToDepthDataFormat(value string) SpaceToDepthAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// SpaceToDepth for tensors of type T. -// -// Rearranges blocks of spatial data, into depth. More specifically, -// this op outputs a copy of the input tensor where values from the `height` -// and `width` dimensions are moved to the `depth` dimension. -// The attr `block_size` indicates the input block size. -// -// * Non-overlapping blocks of size `block_size x block size` are rearranged -// into depth at each location. -// * The depth of the output tensor is `block_size * block_size * input_depth`. -// * The Y, X coordinates within each block of the input become the high order -// component of the output channel index. -// * The input tensor's height and width must be divisible by block_size. -// -// The `data_format` attr specifies the layout of the input and output tensors -// with the following options: -// "NHWC": `[ batch, height, width, channels ]` -// "NCHW": `[ batch, channels, height, width ]` -// "NCHW_VECT_C": -// `qint8 [ batch, channels / 4, height, width, 4 ]` -// -// It is useful to consider the operation as transforming a 6-D Tensor. -// e.g. for data_format = NHWC, -// Each element in the input tensor can be specified via 6 coordinates, -// ordered by decreasing memory layout significance as: -// n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates -// within the output image, bX, bY means coordinates -// within the input block, iC means input channels). -// The output would be a transpose to the following layout: -// n,oY,oX,bY,bX,iC -// -// This operation is useful for resizing the activations between convolutions -// (but keeping all data), e.g. instead of pooling. It is also useful for training -// purely convolutional models. -// -// For example, given an input of shape `[1, 2, 2, 1]`, data_format = "NHWC" and -// block_size = 2: -// -// ``` -// x = [[[[1], [2]], -// [[3], [4]]]] -// ``` -// -// This operation will output a tensor of shape `[1, 1, 1, 4]`: -// -// ``` -// [[[[1, 2, 3, 4]]]] -// ``` -// -// Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`, -// the corresponding output will have a single element (i.e. width and height are -// both 1) and will have a depth of 4 channels (1 * block_size * block_size). -// The output element shape is `[1, 1, 4]`. -// -// For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g. -// -// ``` -// x = [[[[1, 2, 3], [4, 5, 6]], -// [[7, 8, 9], [10, 11, 12]]]] -// ``` -// -// This operation, for block_size of 2, will return the following tensor of shape -// `[1, 1, 1, 12]` -// -// ``` -// [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] -// ``` -// -// Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2: -// -// ``` -// x = [[[[1], [2], [5], [6]], -// [[3], [4], [7], [8]], -// [[9], [10], [13], [14]], -// [[11], [12], [15], [16]]]] -// ``` -// -// the operator will return the following tensor of shape `[1 2 2 4]`: -// -// ``` -// x = [[[[1, 2, 3, 4], -// [5, 6, 7, 8]], -// [[9, 10, 11, 12], -// [13, 14, 15, 16]]]] -// ``` -// -// Arguments: -// -// block_size: The size of the spatial block. -func SpaceToDepth(scope *Scope, input tf.Output, block_size int64, optional ...SpaceToDepthAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"block_size": block_size} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SpaceToDepth", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MinAttr is an optional argument to Min. -type MinAttr func(optionalAttr) - -// MinKeepDims sets the optional keep_dims attribute to value. -// -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func MinKeepDims(value bool) MinAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the minimum of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. -// -// Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. -// -// Returns The reduced tensor. -func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Min", - Input: []tf.Input{ - input, axis, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the sum along segments of a tensor. +// Computes the product along segments of a tensor. // // Read // [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) // for an explanation of segments. // // Computes a tensor such that -// \\(output_i = \sum_j data_j\\) where sum is over `j` such +// \\(output_i = \prod_j data_j\\) where the product is over `j` such // that `segment_ids[j] == i`. // -// If the sum is empty for a given segment ID `i`, `output[i] = 0`. +// If the product is empty for a given segment ID `i`, `output[i] = 1`. // //
-// +// //
// // For example: // // ``` // c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) -// tf.segment_sum(c, tf.constant([0, 0, 1])) -// # ==> [[5, 5, 5, 5], +// tf.segment_prod(c, tf.constant([0, 0, 1])) +// # ==> [[4, 6, 6, 4], // # [5, 6, 7, 8]] // ``` // @@ -33051,12 +34162,12 @@ func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (ou // // Returns Has same shape as data, except for dimension 0 which // has size `k`, the number of segments. -func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { +func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SegmentSum", + Type: "SegmentProd", Input: []tf.Input{ data, segment_ids, }, @@ -33112,136 +34223,35 @@ func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf. return op.Output(0) } -// Store the input tensor in the state of the current session. -// -// Arguments: -// value: The tensor to be stored. -// -// Returns The handle for the tensor stored in the session state, represented -// as a string. -func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "GetSessionHandle", - Input: []tf.Input{ - value, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Advance the counter of a counter-based RNG. -// -// The state of the RNG after -// `rng_skip(n)` will be the same as that after `stateful_uniform([n])` -// (or any other distribution). The actual increment added to the -// counter is an unspecified implementation detail. -// -// Arguments: -// resource: The handle of the resource variable that stores the state of the RNG. -// algorithm: The RNG algorithm. -// delta: The amount of advancement. -// -// Returns the created operation. -func RngSkip(scope *Scope, resource tf.Output, algorithm tf.Output, delta tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RngSkip", - Input: []tf.Input{ - resource, algorithm, delta, - }, - } - return scope.AddOperation(opspec) -} - -// LoadTPUEmbeddingRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingRMSPropParameters. -type LoadTPUEmbeddingRMSPropParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingRMSPropParametersTableId(value int64) LoadTPUEmbeddingRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingRMSPropParametersTableName(value string) LoadTPUEmbeddingRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load RMSProp embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the RMSProp optimization algorithm. -// ms: Value of ms used in the RMSProp optimization algorithm. -// mom: Value of mom used in the RMSProp optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingRMSPropParameters", - Input: []tf.Input{ - parameters, ms, mom, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Computes the minimum along segments of a tensor. +// Computes the sum along segments of a tensor. // // Read // [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) // for an explanation of segments. // -// This operator is similar to the unsorted segment sum operator found -// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). -// Instead of computing the sum over segments, it computes the minimum such that: +// Computes a tensor such that +// \\(output[i] = \sum_{j...} data[j...]\\) where the sum is over tuples `j...` such +// that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids` +// need not be sorted and need not cover all values in the full +// range of valid values. // -// \\(output_i = \min_{j...} data_[j...]\\) where min is over tuples `j...` such -// that `segment_ids[j...] == i`. +// If the sum is empty for a given segment ID `i`, `output[i] = 0`. +// If the given segment ID `i` is negative, the value is dropped and will not be +// added to the sum of the segment. // -// If the minimum is empty for a given segment ID `i`, it outputs the largest -// possible value for the specific numeric type, -// `output[i] = numeric_limits::max()`. +// `num_segments` should equal the number of distinct segment IDs. // -// For example: +//
+// +//
// // ``` python // c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) -// tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2) -// # ==> [[ 1, 2, 2, 1], +// tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 5, 5, 5, 5], // # [5, 6, 7, 8]] // ``` // -// If the given segment ID `i` is negative, then the corresponding value is -// dropped, and will not be included in the result. // // Arguments: // @@ -33251,12 +34261,12 @@ func LoadTPUEmbeddingRMSPropParameters(scope *Scope, parameters tf.Output, ms tf // Returns Has same shape as data, except for the first `segment_ids.rank` // dimensions, which are replaced with a single dimension which has size // `num_segments`. -func UnsortedSegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { +func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "UnsortedSegmentMin", + Type: "UnsortedSegmentSum", Input: []tf.Input{ data, segment_ids, num_segments, }, @@ -33265,51 +34275,60 @@ func UnsortedSegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output, num return op.Output(0) } -// PrintV2Attr is an optional argument to PrintV2. -type PrintV2Attr func(optionalAttr) - -// PrintV2OutputStream sets the optional output_stream attribute to value. +// Computes the maximum along segments of a tensor. // -// value: A string specifying the output stream or logging level to print to. -// If not specified, defaults to "stderr" -func PrintV2OutputStream(value string) PrintV2Attr { - return func(m optionalAttr) { - m["output_stream"] = value - } -} - -// PrintV2End sets the optional end attribute to value. -// If not specified, defaults to "\n" -func PrintV2End(value string) PrintV2Attr { - return func(m optionalAttr) { - m["end"] = value - } -} - -// Prints a string scalar. +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the maximum such that: +// +// \\(output_i = \max_{j...} data[j...]\\) where max is over tuples `j...` such +// that `segment_ids[j...] == i`. +// +// If the maximum is empty for a given segment ID `i`, it outputs the smallest +// possible value for the specific numeric type, +// `output[i] = numeric_limits::lowest()`. +// +// If the given segment ID `i` is negative, then the corresponding value is +// dropped, and will not be included in the result. +// +//
+// +//
+// +// For example: +// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 4, 3, 3, 4], +// # [5, 6, 7, 8]] +// ``` // -// Prints a string scalar to the desired output_stream. // // Arguments: -// input: The string scalar to print. // -// Returns the created operation. -func PrintV2(scope *Scope, input tf.Output, optional ...PrintV2Attr) (o *tf.Operation) { +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "PrintV2", + Type: "UnsortedSegmentMax", Input: []tf.Input{ - input, + data, segment_ids, num_segments, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // Computes the product along segments of a tensor. @@ -33362,78 +34381,6 @@ func UnsortedSegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output, nu return op.Output(0) } -// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. -type AllCandidateSamplerAttr func(optionalAttr) - -// AllCandidateSamplerSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Generates labels for candidate sampling with a learned unigram distribution. -// -// See explanations of candidate sampling and the data formats at -// go/candidate-sampling. -// -// For each batch, this op picks a single set of sampled candidate labels. -// -// The advantages of sampling candidates per-batch are simplicity and the -// possibility of efficient dense matrix multiplication. The disadvantage is that -// the sampled candidates must be chosen independently of the context and of the -// true labels. -// -// Arguments: -// true_classes: A batch_size * num_true matrix, in which each row contains the -// IDs of the num_true target_classes in the corresponding original label. -// num_true: Number of true labels per context. -// num_sampled: Number of candidates to produce. -// unique: If unique is true, we sample with rejection, so that all sampled -// candidates in a batch are unique. This requires some approximation to -// estimate the post-rejection sampling probabilities. -// -// Returns A vector of length num_sampled, in which each element is -// the ID of a sampled candidate.A batch_size * num_true matrix, representing -// the number of times each candidate is expected to occur in a batch -// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled -// candidate representing the number of times the candidate is expected -// to occur in a batch of sampled candidates. If unique=true, then this is a -// probability. -func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AllCandidateSampler", - Input: []tf.Input{ - true_classes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Computes the sum along sparse segments of a tensor. // // Read @@ -33489,83 +34436,116 @@ func SparseSegmentSum(scope *Scope, data tf.Output, indices tf.Output, segment_i // Computes the mean along sparse segments of a tensor. // -// See `tf.sparse.segment_sum` for usage examples. +// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is +// misisng, the `output` tensor at that position will be zeroed. // -// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first -// dimension, selecting a subset of dimension 0, specified by `indices`. +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. // // Arguments: // // indices: A 1-D tensor. Has same rank as `segment_ids`. // segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. // -// Returns Has same shape as data, except for dimension 0 which -// has size `k`, the number of segments. -func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { +// Returns Has same shape as data, except for dimension 0 which has size +// `num_segments`. +func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSegmentMean", + Type: "SparseSegmentMeanWithNumSegments", Input: []tf.Input{ - data, indices, segment_ids, + data, indices, segment_ids, num_segments, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// OrderedMapSizeAttr is an optional argument to OrderedMapSize. -type OrderedMapSizeAttr func(optionalAttr) +// Conv2DBackpropInputAttr is an optional argument to Conv2DBackpropInput. +type Conv2DBackpropInputAttr func(optionalAttr) -// OrderedMapSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// Conv2DBackpropInputUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DBackpropInputUseCudnnOnGpu(value bool) Conv2DBackpropInputAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DBackpropInputExplicitPaddings sets the optional explicit_paddings attribute to value. // -// REQUIRES: value >= 0 -func OrderedMapSizeCapacity(value int64) OrderedMapSizeAttr { +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DBackpropInputExplicitPaddings(value []int64) Conv2DBackpropInputAttr { return func(m optionalAttr) { - m["capacity"] = value + m["explicit_paddings"] = value } } -// OrderedMapSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// Conv2DBackpropInputDataFormat sets the optional data_format attribute to value. // -// REQUIRES: value >= 0 -func OrderedMapSizeMemoryLimit(value int64) OrderedMapSizeAttr { +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr { return func(m optionalAttr) { - m["memory_limit"] = value + m["data_format"] = value } } -// OrderedMapSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapSizeContainer(value string) OrderedMapSizeAttr { +// Conv2DBackpropInputDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr { return func(m optionalAttr) { - m["container"] = value + m["dilations"] = value } } -// OrderedMapSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapSizeSharedName(value string) OrderedMapSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of elements in the underlying container. -func OrderedMapSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapSizeAttr) (size tf.Output) { +// Computes the gradients of convolution with respect to the input. +// +// Arguments: +// input_sizes: An integer vector representing the shape of `input`, +// where `input` is a 4-D `[batch, height, width, channels]` tensor. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. Must be in the same order as the dimension specified with +// format. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient +// w.r.t. the input of the convolution. +func Conv2DBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropInputAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{"strides": strides, "padding": padding} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "OrderedMapSize", - + Type: "Conv2DBackpropInput", + Input: []tf.Input{ + input_sizes, filter, out_backprop, + }, Attrs: attrs, } op := scope.AddOperation(opspec) @@ -33600,59 +34580,6 @@ func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment return op.Output(0) } -// Does nothing. Serves as a control trigger for scheduling. -// -// Only useful as a placeholder for control edges. -// -// Returns the created operation. -func ControlTrigger(scope *Scope) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ControlTrigger", - } - return scope.AddOperation(opspec) -} - -// Batch normalization. -// -// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() -// -// This op is deprecated. Prefer `tf.nn.batch_normalization`. -// -// Arguments: -// t: A 4D input Tensor. -// m: A 1D mean Tensor with size matching the last dimension of t. -// This is the first output from tf.nn.moments, -// or a saved moving average thereof. -// v: A 1D variance Tensor with size matching the last dimension of t. -// This is the second output from tf.nn.moments, -// or a saved moving average thereof. -// beta: A 1D beta Tensor with size matching the last dimension of t. -// An offset to be added to the normalized tensor. -// gamma: A 1D gamma Tensor with size matching the last dimension of t. -// If "scale_after_normalization" is true, this tensor will be multiplied -// with the normalized tensor. -// variance_epsilon: A small float number to avoid dividing by 0. -// scale_after_normalization: A bool indicating whether the resulted tensor -// needs to be multiplied with gamma. -func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} - opspec := tf.OpSpec{ - Type: "BatchNormWithGlobalNormalization", - Input: []tf.Input{ - t, m, v, beta, gamma, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Computes the sum along sparse segments of a tensor divided by the sqrt of N. // // N is the size of the segment being reduced. @@ -33686,30 +34613,6 @@ func SparseSegmentSqrtNWithNumSegments(scope *Scope, data tf.Output, indices tf. return op.Output(0) } -// Computes gradients for SparseSegmentSqrtN. -// -// Returns tensor "output" with same shape as grad, except for dimension 0 whose -// value is output_dim0. -// -// Arguments: -// grad: gradient propagated to the SparseSegmentSqrtN op. -// indices: indices passed to the corresponding SparseSegmentSqrtN op. -// segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op. -// output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op. -func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSqrtNGrad", - Input: []tf.Input{ - grad, indices, segment_ids, output_dim0, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Generates values in an interval. // // A sequence of `num` evenly-spaced values are generated beginning at `start`. @@ -33742,31 +34645,138 @@ func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (out return op.Output(0) } -// RealAttr is an optional argument to Real. -type RealAttr func(optionalAttr) - -// RealTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_FLOAT -func RealTout(value tf.DataType) RealAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Returns the real part of a complex number. +// Creates a sequence of numbers. // -// Given a tensor `input` of complex numbers, this operation returns a tensor of -// type `float` that is the real part of each element in `input`. All elements in -// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real -// part returned by this operation and *b* is the imaginary part. +// This operation creates a sequence of numbers that begins at `start` and +// extends by increments of `delta` up to but not including `limit`. // // For example: // // ``` -// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.real(input) ==> [-2.25, 3.25] +// # 'start' is 3 +// # 'limit' is 18 +// # 'delta' is 3 +// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] // ``` -func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) { +// +// Arguments: +// start: 0-D (scalar). First entry in the sequence. +// limit: 0-D (scalar). Upper limit of sequence, exclusive. +// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. +// +// Returns 1-D. +func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Range", + Input: []tf.Input{ + start, limit, delta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reverses specific dimensions of a tensor. +// +// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions +// of `tensor`, this operation reverses each dimension i of `tensor` where +// `dims[i]` is `True`. +// +// `tensor` can have up to 8 dimensions. The number of dimensions +// of `tensor` must equal the number of elements in `dims`. In other words: +// +// `rank(tensor) = size(dims)` +// +// For example: +// +// ``` +// # tensor 't' is [[[[ 0, 1, 2, 3], +// # [ 4, 5, 6, 7], +// # [ 8, 9, 10, 11]], +// # [[12, 13, 14, 15], +// # [16, 17, 18, 19], +// # [20, 21, 22, 23]]]] +// # tensor 't' shape is [1, 2, 3, 4] +// +// # 'dims' is [False, False, False, True] +// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], +// [ 7, 6, 5, 4], +// [ 11, 10, 9, 8]], +// [[15, 14, 13, 12], +// [19, 18, 17, 16], +// [23, 22, 21, 20]]]] +// +// # 'dims' is [False, True, False, False] +// reverse(t, dims) ==> [[[[12, 13, 14, 15], +// [16, 17, 18, 19], +// [20, 21, 22, 23] +// [[ 0, 1, 2, 3], +// [ 4, 5, 6, 7], +// [ 8, 9, 10, 11]]]] +// +// # 'dims' is [False, False, True, False] +// reverse(t, dims) ==> [[[[8, 9, 10, 11], +// [4, 5, 6, 7], +// [0, 1, 2, 3]] +// [[20, 21, 22, 23], +// [16, 17, 18, 19], +// [12, 13, 14, 15]]]] +// ``` +// +// Arguments: +// tensor: Up to 8-D. +// dims: 1-D. The dimensions to reverse. +// +// Returns The same shape as `tensor`. +func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reverse", + Input: []tf.Input{ + tensor, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad. +type ResizeNearestNeighborGradAttr func(optionalAttr) + +// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and grad tensors are +// aligned. Defaults to false. +// If not specified, defaults to false +func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeNearestNeighborGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeNearestNeighborGradHalfPixelCenters(value bool) ResizeNearestNeighborGradAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Computes the gradient of nearest neighbor interpolation. +// +// Arguments: +// grads: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The +// original input size. +// +// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients +// with respect to the input image. +func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -33775,9 +34785,9 @@ func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output a(attrs) } opspec := tf.OpSpec{ - Type: "Real", + Type: "ResizeNearestNeighborGrad", Input: []tf.Input{ - input, + grads, size, }, Attrs: attrs, } @@ -33785,111 +34795,49 @@ func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output return op.Output(0) } -// Returns the complex conjugate of a complex number. +// AngleAttr is an optional argument to Angle. +type AngleAttr func(optionalAttr) + +// AngleTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func AngleTout(value tf.DataType) AngleAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the argument of a complex number. // // Given a tensor `input` of complex numbers, this operation returns a tensor of -// complex numbers that are the complex conjugate of each element in `input`. The -// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the -// real part and *b* is the imaginary part. +// type `float` that is the argument of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part. // -// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// The argument returned by this operation is of the form \\(atan2(b, a)\\). // // For example: // // ``` // # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] -// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] +// tf.angle(input) ==> [2.0132, 1.056] // ``` -func Conj(scope *Scope, input tf.Output) (output tf.Output) { +// +// @compatibility(numpy) +// Equivalent to np.angle. +// @end_compatibility +func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Output) { if scope.Err() != nil { return } - opspec := tf.OpSpec{ - Type: "Conj", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2. -type PaddingFIFOQueueV2Attr func(optionalAttr) - -// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value. -// -// value: The shape of each component in a value. The length of this attr must -// be either 0 or the same as the length of component_types. -// Shapes of fixed rank but variable size are allowed by setting -// any shape dimension to -1. In this case, the inputs' shape may vary along -// the given dimension, and DequeueMany will pad the given dimension with -// zeros up to the maximum shape of all elements in the given batch. -// If the length of this attr is 0, different queue elements may have -// different ranks and shapes, but only one element may be dequeued at a time. -// If not specified, defaults to <> -// -// REQUIRES: len(value) >= 0 -func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr { - return func(m optionalAttr) { - m["shapes"] = value - } -} - -// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value. -// -// value: The upper bound on the number of elements in this queue. -// Negative numbers mean no limit. -// If not specified, defaults to -1 -func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// PaddingFIFOQueueV2Container sets the optional container attribute to value. -// -// value: If non-empty, this queue is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this queue will be shared under the given name -// across multiple sessions. -// If not specified, defaults to "" -func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// A queue that produces elements in first-in first-out order. -// -// Variable-size shapes are allowed by setting the corresponding shape dimensions -// to 0 in the shape attr. In this case DequeueMany will pad up to the maximum -// size of any given element in the minibatch. See below for details. -// -// Arguments: -// component_types: The type of each component in a value. -// -// Returns The handle to the queue. -func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"component_types": component_types} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "PaddingFIFOQueueV2", - + Type: "Angle", + Input: []tf.Input{ + input, + }, Attrs: attrs, } op := scope.AddOperation(opspec) @@ -33921,6 +34869,197 @@ func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) { return op.Output(0) } +// Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// mean_gradients: A tensor with shape=[logits_dimension] with mean of gradients for a first node. +// mean_hessians: A tensor with shape=[logits_dimension] mean of hessians for a first node. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// +// Returns Bool, whether to continue bias centering. +func BoostedTreesCenterBias(scope *Scope, tree_ensemble_handle tf.Output, mean_gradients tf.Output, mean_hessians tf.Output, l1 tf.Output, l2 tf.Output) (continue_centering tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCenterBias", + Input: []tf.Input{ + tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// HistogramFixedWidthAttr is an optional argument to HistogramFixedWidth. +type HistogramFixedWidthAttr func(optionalAttr) + +// HistogramFixedWidthDtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_INT32 +func HistogramFixedWidthDtype(value tf.DataType) HistogramFixedWidthAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Return histogram of values. +// +// Given the tensor `values`, this operation returns a rank 1 histogram counting +// the number of entries in `values` that fall into every bin. The bins are +// equal width and determined by the arguments `value_range` and `nbins`. +// +// ```python +// # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) +// nbins = 5 +// value_range = [0.0, 5.0] +// new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] +// +// with tf.get_default_session() as sess: +// hist = tf.histogram_fixed_width(new_values, value_range, nbins=5) +// variables.global_variables_initializer().run() +// sess.run(hist) => [2, 1, 1, 0, 2] +// ``` +// +// Arguments: +// values: Numeric `Tensor`. +// value_range: Shape [2] `Tensor` of same `dtype` as `values`. +// values <= value_range[0] will be mapped to hist[0], +// values >= value_range[1] will be mapped to hist[-1]. +// nbins: Scalar `int32 Tensor`. Number of histogram bins. +// +// Returns A 1-D `Tensor` holding histogram of values. +func HistogramFixedWidth(scope *Scope, values tf.Output, value_range tf.Output, nbins tf.Output, optional ...HistogramFixedWidthAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "HistogramFixedWidth", + Input: []tf.Input{ + values, value_range, nbins, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A container for an iterator resource. +// +// Arguments: +// handle: A handle to the iterator to delete. +// deleter: A variant deleter. +// +// Returns the created operation. +func DeleteIterator(scope *Scope, handle tf.Output, deleter tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeleteIterator", + Input: []tf.Input{ + handle, deleter, + }, + } + return scope.AddOperation(opspec) +} + +// Computes the matrix square root of one or more square matrices: +// +// matmul(sqrtm(A), sqrtm(A)) = A +// +// The input matrix should be invertible. If the input matrix is real, it should +// have no eigenvalues which are real and negative (pairs of complex conjugate +// eigenvalues are allowed). +// +// The matrix square root is computed by first reducing the matrix to +// quasi-triangular form with the real Schur decomposition. The square root +// of the quasi-triangular matrix is then computed directly. Details of +// the algorithm can be found in: Nicholas J. Higham, "Computing real +// square roots of a real matrix", Linear Algebra Appl., 1987. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the matrix square root for all input submatrices `[..., :, :]`. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M, M]`. +// +// @compatibility(scipy) +// Equivalent to scipy.linalg.sqrtm +// @end_compatibility +func MatrixSquareRoot(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixSquareRoot", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Rolls the elements of a tensor along an axis. +// +// The elements are shifted positively (towards larger indices) by the offset of +// `shift` along the dimension of `axis`. Negative `shift` values will shift +// elements in the opposite direction. Elements that roll passed the last position +// will wrap around to the first and vice versa. Multiple shifts along multiple +// axes may be specified. +// +// For example: +// +// ``` +// # 't' is [0, 1, 2, 3, 4] +// roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2] +// +// # shifting along multiple dimensions +// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +// roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] +// +// # shifting along the same axis multiple times +// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +// roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] +// ``` +// +// Arguments: +// +// shift: Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which +// elements are shifted positively (towards larger indices) along the dimension +// specified by `axis[i]`. Negative shifts will roll the elements in the opposite +// direction. +// axis: Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift +// `shift[i]` should occur. If the same axis is referenced more than once, the +// total shift for that axis will be the sum of all the shifts that belong to that +// axis. +// +// Returns Has the same shape and size as the input. The elements are shifted +// positively (towards larger indices) by the offsets of `shift` along the +// dimensions of `axis`. +func Roll(scope *Scope, input tf.Output, shift tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Roll", + Input: []tf.Input{ + input, shift, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // CumsumAttr is an optional argument to Cumsum. type CumsumAttr func(optionalAttr) @@ -34000,233 +35139,143 @@ func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) ( return op.Output(0) } -// Assigns sparse updates to the variable referenced by `resource`. +// Interleave the values from the `data` tensors into a single tensor. // -// This operation computes +// Builds a merged tensor such that // -// # Scalar indices -// ref[indices, ...] = updates[...] +// ```python +// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] +// ``` // -// # Vector indices (for each i) -// ref[indices[i], ...] = updates[i, ...] +// For example, if each `indices[m]` is scalar or vector, we have // -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] +// ```python +// # Scalar indices: +// merged[indices[m], ...] = data[m][...] // -// Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// # Vector indices: +// merged[indices[m][i], ...] = data[m][i, ...] +// ``` // -// Returns the created operation. -func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +// Each `data[i].shape` must start with the corresponding `indices[i].shape`, +// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we +// must have `data[i].shape = indices[i].shape + constant`. In terms of this +// `constant`, the output shape is +// +// merged.shape = [max(indices)] + constant +// +// Values are merged in order, so if an index appears in both `indices[m][i]` and +// `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the +// merged result. If you do not need this guarantee, ParallelDynamicStitch might +// perform better on some devices. +// +// For example: +// +// ```python +// indices[0] = 6 +// indices[1] = [4, 1] +// indices[2] = [[5, 2], [0, 3]] +// data[0] = [61, 62] +// data[1] = [[41, 42], [11, 12]] +// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] +// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], +// [51, 52], [61, 62]] +// ``` +// +// This method can be used to merge partitions created by `dynamic_partition` +// as illustrated on the following example: +// +// ```python +// # Apply function (increments x_i) on elements for which a certain condition +// # apply (x_i != -1 in this example). +// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4]) +// condition_mask=tf.not_equal(x,tf.constant(-1.)) +// partitioned_data = tf.dynamic_partition( +// x, tf.cast(condition_mask, tf.int32) , 2) +// partitioned_data[1] = partitioned_data[1] + 1.0 +// condition_indices = tf.dynamic_partition( +// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2) +// x = tf.dynamic_stitch(condition_indices, partitioned_data) +// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain +// # unchanged. +// ``` +// +//
+// +//
+func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ResourceScatterUpdate", + Type: "DynamicStitch", Input: []tf.Input{ - resource, indices, updates, + tf.OutputList(indices), tf.OutputList(data), }, } - return scope.AddOperation(opspec) -} - -// CumprodAttr is an optional argument to Cumprod. -type CumprodAttr func(optionalAttr) - -// CumprodExclusive sets the optional exclusive attribute to value. -// -// value: If `True`, perform exclusive cumprod. -// If not specified, defaults to false -func CumprodExclusive(value bool) CumprodAttr { - return func(m optionalAttr) { - m["exclusive"] = value - } -} - -// CumprodReverse sets the optional reverse attribute to value. -// -// value: A `bool` (default: False). -// If not specified, defaults to false -func CumprodReverse(value bool) CumprodAttr { - return func(m optionalAttr) { - m["reverse"] = value - } -} - -// Compute the cumulative product of the tensor `x` along `axis`. -// -// By default, this op performs an inclusive cumprod, which means that the first -// element of the input is identical to the first element of the output: -// -// ```python -// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] -// ``` -// -// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is -// performed instead: -// -// ```python -// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] -// ``` -// -// By setting the `reverse` kwarg to `True`, the cumprod is performed in the -// opposite direction: -// -// ```python -// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] -// ``` -// -// This is more efficient than using separate `tf.reverse` ops. -// -// The `reverse` and `exclusive` kwargs can also be combined: -// -// ```python -// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] -// ``` -// -// Arguments: -// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, -// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, -// `complex128`, `qint8`, `quint8`, `qint32`, `half`. -// axis: A `Tensor` of type `int32` (default: 0). Must be in the range -// `[-rank(x), rank(x))`. -func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Cumprod", - Input: []tf.Input{ - x, axis, - }, - Attrs: attrs, - } op := scope.AddOperation(opspec) return op.Output(0) } -// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. -type StatelessRandomUniformAttr func(optionalAttr) +// QuantizedMatMulAttr is an optional argument to QuantizedMatMul. +type QuantizedMatMulAttr func(optionalAttr) -// StatelessRandomUniformDtype sets the optional dtype attribute to value. -// -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs deterministic pseudorandom random values from a uniform distribution. -// -// The generated values follow a uniform distribution in the range `[0, 1)`. The -// lower bound 0 is included in the range, while the upper bound 1 is excluded. -// -// The outputs are a deterministic function of `shape` and `seed`. -// -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). -// -// Returns Random values with specified shape. -func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StatelessRandomUniform", - Input: []tf.Input{ - shape, seed, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug. -type RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) - -// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Retrieve RMSProp embedding parameters with debug support. -// -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. -// -// Returns Parameter parameters updated by the RMSProp optimization algorithm.Parameter ms updated by the RMSProp optimization algorithm.Parameter mom updated by the RMSProp optimization algorithm.Parameter gradient_accumulators updated by the RMSProp optimization algorithm. -func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3) -} - -// QuantizedMulAttr is an optional argument to QuantizedMul. -type QuantizedMulAttr func(optionalAttr) - -// QuantizedMulToutput sets the optional Toutput attribute to value. +// QuantizedMatMulToutput sets the optional Toutput attribute to value. // If not specified, defaults to DT_QINT32 -func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr { +func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr { return func(m optionalAttr) { m["Toutput"] = value } } -// Returns x * y element-wise, working on quantized buffers. +// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulTactivation sets the optional Tactivation attribute to value. +// +// value: The type of output produced by activation function +// following this operation. +// If not specified, defaults to DT_QUINT8 +func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["Tactivation"] = value + } +} + +// Perform a quantized matrix multiplication of `a` by the matrix `b`. +// +// The inputs must be two-dimensional matrices and the inner dimension of +// `a` (after being transposed if `transpose_a` is non-zero) must match the +// outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). // // Arguments: -// -// -// min_x: The float value that the lowest quantized `x` value represents. -// max_x: The float value that the highest quantized `x` value represents. -// min_y: The float value that the lowest quantized `y` value represents. -// max_y: The float value that the highest quantized `y` value represents. +// a: Must be a two-dimensional tensor. +// b: Must be a two-dimensional tensor. +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. // // Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -// -// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about -// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { +func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { if scope.Err() != nil { return } @@ -34235,9 +35284,9 @@ func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x a(attrs) } opspec := tf.OpSpec{ - Type: "QuantizedMul", + Type: "QuantizedMatMul", Input: []tf.Input{ - x, y, min_x, max_x, min_y, max_y, + a, b, min_a, max_a, min_b, max_b, }, Attrs: attrs, } @@ -34324,87 +35373,124 @@ func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf return op.Output(0), op.Output(1), op.Output(2) } -// A container for an iterator resource. +// Provides the time since epoch in seconds. // -// Arguments: -// handle: A handle to the iterator to delete. -// deleter: A variant deleter. +// Returns the timestamp as a `float64` for seconds since the Unix epoch. // -// Returns the created operation. -func DeleteIterator(scope *Scope, handle tf.Output, deleter tf.Output) (o *tf.Operation) { +// Note: the timestamp is computed when the op is executed, not when it is added +// to the graph. +func Timestamp(scope *Scope) (ts tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "DeleteIterator", - Input: []tf.Input{ - handle, deleter, - }, - } - return scope.AddOperation(opspec) -} - -// Computes the absolute value of a tensor. -// -// Given a tensor `x`, this operation returns a tensor containing the absolute -// value of each element in `x`. For example, if x is an input element and y is -// an output element, this operation computes \\(y = |x|\\). -func Abs(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Abs", - Input: []tf.Input{ - x, - }, + Type: "Timestamp", } op := scope.AddOperation(opspec) return op.Output(0) } -// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`. -// -// Each comparison returns a boolean `true` (if `input_value > threshold`) -// or and `false` otherwise. -// -// This operation is useful for Locality-Sensitive-Hashing (LSH) and other -// algorithms that use hashing approximations of cosine and `L2` distances; -// codes can be generated from an input via: -// -// ```python -// codebook_size = 50 -// codebook_bits = codebook_size * 32 -// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits], -// dtype=x.dtype, -// initializer=tf.orthogonal_initializer()) -// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.) -// codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32 -// # now codes has shape x.shape[:-1] + [codebook_size] -// ``` -// -// **NOTE**: Currently, the innermost dimension of the tensor must be divisible -// by 8. -// -// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is -// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`. +// QuantizedReluXAttr is an optional argument to QuantizedReluX. +type QuantizedReluXAttr func(optionalAttr) + +// QuantizedReluXOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)` // // Arguments: -// input: Values to compare against `threshold` and bitpack. -// threshold: Threshold to compare against. // -// Returns The bitpacked comparisons. -func CompareAndBitpack(scope *Scope, input tf.Output, threshold tf.Output) (output tf.Output) { +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns Has the same output shape as "features".The float value that the lowest quantized value represents.The float value that the highest quantized value represents. +func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedReluX", + Input: []tf.Input{ + features, max_value, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes a range that covers the actual values present in a quantized tensor. +// +// Given a quantized tensor described by `(input, input_min, input_max)`, outputs a +// range that covers the actual values present in that tensor. This op is typically +// used to produce the `requested_output_min` and `requested_output_max` for +// `Requantize`. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// +// Returns The computed min output.the computed max output. +func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "CompareAndBitpack", + Type: "RequantizationRange", Input: []tf.Input{ - input, threshold, + input, input_min, input_max, }, } op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Bucketizes 'input' based on 'boundaries'. +// +// For example, if the inputs are +// boundaries = [0, 10, 100] +// input = [[-5, 10000] +// [150, 10] +// [5, 100]] +// +// then the output will be +// output = [[0, 3] +// [3, 2] +// [1, 3]] +// +// Arguments: +// input: Any shape of Tensor contains with int or float type. +// boundaries: A sorted list of floats gives the boundary of the buckets. +// +// Returns Same shape with 'input', each value of input replaced with bucket index. +// +// @compatibility(numpy) +// Equivalent to np.digitize. +// @end_compatibility +func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"boundaries": boundaries} + opspec := tf.OpSpec{ + Type: "Bucketize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) return op.Output(0) } @@ -34437,67 +35523,336 @@ func ClipByValue(scope *Scope, t tf.Output, clip_value_min tf.Output, clip_value return op.Output(0) } -// Looks up keys in a table, outputs the corresponding values. +// Computes requantization range per channel. // -// The tensor `keys` must of the same type as the keys of the table. -// The output `values` is of the type of the table values. +// Arguments: +// input: The original input tensor. +// input_min: The minimum value of the input tensor +// input_max: The maximum value of the input tensor. +// clip_value_max: The maximum value of the output that needs to be clipped. +// Example: set this to 6 for Relu6. // -// The scalar `default_value` is the value output for keys not present in the -// table. It must also be of the same type as the table values. +// Returns The minimum value of the final output tensorThe maximum value of the final output tensor. +func RequantizationRangePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, clip_value_max float32) (output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"clip_value_max": clip_value_max} + opspec := tf.OpSpec{ + Type: "RequantizationRangePerChannel", + Input: []tf.Input{ + input, input_min, input_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StridedSliceGradAttr is an optional argument to StridedSliceGrad. +type StridedSliceGradAttr func(optionalAttr) + +// StridedSliceGradBeginMask sets the optional begin_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["begin_mask"] = value + } +} + +// StridedSliceGradEndMask sets the optional end_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradEndMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["end_mask"] = value + } +} + +// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["ellipsis_mask"] = value + } +} + +// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["new_axis_mask"] = value + } +} + +// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["shrink_axis_mask"] = value + } +} + +// Returns the gradient of `StridedSlice`. +// +// Since `StridedSlice` cuts out pieces of its `input` which is size +// `shape`, its gradient will have the same shape (which is passed here +// as `shape`). The gradient will be zero in any element that the slice +// does not select. +// +// Arguments are the same as StridedSliceGrad with the exception that +// `dy` is the input gradient to be propagated and `shape` is the +// shape of `StridedSlice`'s `input`. +func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StridedSliceGrad", + Input: []tf.Input{ + shape, begin, end, strides, dy, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Saves tensors in V2 checkpoint format. +// +// By default, saves the named tensors in full. If the caller wishes to save +// specific slices of full tensors, "shape_and_slices" should be non-empty strings +// and correspondingly well-formed. +// +// Arguments: +// prefix: Must have a single element. The prefix of the V2 checkpoint to which we +// write the tensors. +// tensor_names: shape {N}. The names of the tensors to be saved. +// shape_and_slices: shape {N}. The slice specs of the tensors to be saved. +// Empty strings indicate that they are non-partitioned tensors. +// tensors: `N` tensors to save. +// +// Returns the created operation. +func SaveV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, tensors []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SaveV2", + Input: []tf.Input{ + prefix, tensor_names, shape_and_slices, tf.OutputList(tensors), + }, + } + return scope.AddOperation(opspec) +} + +// Returns the next representable value of `x1` in the direction of `x2`, element-wise. +// +// This operation returns the same result as the C++ std::nextafter function. +// +// It can also return a subnormal number. +// +// @compatibility(cpp) +// Equivalent to C++ std::nextafter function. +// @end_compatibility +func NextAfter(scope *Scope, x1 tf.Output, x2 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NextAfter", + Input: []tf.Input{ + x1, x2, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SerializeManySparseAttr is an optional argument to SerializeManySparse. +type SerializeManySparseAttr func(optionalAttr) + +// SerializeManySparseOutType sets the optional out_type attribute to value. +// +// value: The `dtype` to use for serialization; the supported types are `string` +// (default) and `variant`. +// If not specified, defaults to DT_STRING +func SerializeManySparseOutType(value tf.DataType) SerializeManySparseAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object. +// +// The `SparseTensor` must have rank `R` greater than 1, and the first dimension +// is treated as the minibatch dimension. Elements of the `SparseTensor` +// must be sorted in increasing order of this first dimension. The serialized +// `SparseTensor` objects going into each row of `serialized_sparse` will have +// rank `R-1`. +// +// The minibatch size `N` is extracted from `sparse_shape[0]`. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. +// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. +func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeManySparseAttr) (serialized_sparse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SerializeManySparse", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Determine the script codes of a given tensor of Unicode integer code points. +// +// This operation converts Unicode code points to script codes corresponding to +// each code point. Script codes correspond to International Components for +// Unicode (ICU) UScriptCode values. See http://icu-project.org/apiref/icu4c/uscript_8h.html. +// Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will +// match input shape. +// +// Arguments: +// input: A Tensor of int32 Unicode code points. +// +// Returns A Tensor of int32 script codes corresponding to each input code point. +func UnicodeScript(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnicodeScript", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Updates the table to associates keys with values. +// +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. // // Arguments: // table_handle: Handle to the table. // keys: Any shape. Keys to look up. +// values: Values to associate with keys. // -// -// Returns Same shape as `keys`. Values found in the table, or `default_values` -// for missing keys. -func LookupTableFindV2(scope *Scope, table_handle tf.Output, keys tf.Output, default_value tf.Output) (values tf.Output) { +// Returns the created operation. +func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "LookupTableFindV2", + Type: "LookupTableInsertV2", Input: []tf.Input{ - table_handle, keys, default_value, + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// Deprecated, use python implementation tf.linalg.matrix_exponential. +// +// DEPRECATED at GraphDef version 27: Use Python implementation tf.linalg.matrix_exponential instead. +func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixExponential", + Input: []tf.Input{ + input, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Makes its input available to the next iteration. +// Return the reduction indices for computing gradients of s0 op s1 with broadcast. +// +// This is typically used by gradient computations for a broadcasting operation. +func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastGradientArgs", + Input: []tf.Input{ + s0, s1, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Pads a tensor with mirrored values. +// +// This operation pads a `input` with mirrored values according to the `paddings` +// you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is +// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many values to add before the contents of `input` in that dimension, and +// `paddings[D, 1]` indicates how many values to add after the contents of `input` +// in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater +// than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true +// (if false, respectively). +// +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 2, 3], [4, 5, 6]]. +// # 'paddings' is [[1, 1]], [2, 2]]. +// # 'mode' is SYMMETRIC. +// # rank of 't' is 2. +// pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2] +// [2, 1, 1, 2, 3, 3, 2] +// [5, 4, 4, 5, 6, 6, 5] +// [5, 4, 4, 5, 6, 6, 5]] +// ``` // // Arguments: -// data: The tensor to be made available to the next iteration. +// input: The input tensor to be padded. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// mode: Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions +// do not include the borders, while in symmetric mode the padded regions +// do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings` +// is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and +// it is `[1, 2, 3, 3, 2]` in symmetric mode. // -// Returns The same tensor as `data`. -func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { +// Returns The padded tensor. +func MirrorPad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"mode": mode} opspec := tf.OpSpec{ - Type: "NextIteration", + Type: "MirrorPad", Input: []tf.Input{ - data, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the reciprocal of x element-wise. -// -// I.e., \\(y = 1 / x\\). -func Inv(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Inv", - Input: []tf.Input{ - x, + input, paddings, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -34523,102 +35878,190 @@ func LookupTableSizeV2(scope *Scope, table_handle tf.Output) (size tf.Output) { return op.Output(0) } -// Draw bounding boxes on a batch of images. +// Gives a guarantee to the TF runtime that the input tensor is a constant. // -// Outputs a copy of `images` but draws on top of the pixels zero or more bounding -// boxes specified by the locations in `boxes`. The coordinates of the each -// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The -// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and -// height of the underlying image. +// The runtime is then free to make optimizations based on this. // -// For example, if an image is 100 x 200 pixels (height x width) and the bounding -// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of -// the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates). +// Only accepts value typed tensors as inputs and rejects resource variable handles +// as input. // -// Parts of the bounding box may fall outside the image. -// -// Arguments: -// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. -// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding -// boxes. -// -// Returns 4-D with the same shape as `images`. The batch of input images with -// bounding boxes drawn on the images. -func DrawBoundingBoxes(scope *Scope, images tf.Output, boxes tf.Output) (output tf.Output) { +// Returns the input tensor without modification. +func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "DrawBoundingBoxes", + Type: "GuaranteeConst", Input: []tf.Input{ - images, boxes, + input, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// MutableHashTableV2Attr is an optional argument to MutableHashTableV2. -type MutableHashTableV2Attr func(optionalAttr) - -// MutableHashTableV2Container sets the optional container attribute to value. +// Replaces the contents of the table with the specified keys and values. // -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutableHashTableV2Container(value string) MutableHashTableV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MutableHashTableV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// -// value: If true and shared_name is empty, the table is shared -// using the node name. -// If not specified, defaults to false -func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// Creates an empty hash table. -// -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a scalar. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. // // Arguments: -// key_dtype: Type of the table keys. -// value_dtype: Type of the table values. +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// values: Values to associate with keys. // -// Returns Handle to a table. -func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) { +// Returns the created operation. +func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + opspec := tf.OpSpec{ + Type: "LookupTableImportV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// Writes contents to the file at input filename. Creates file and recursively +// +// creates directory if not existing. +// +// Arguments: +// filename: scalar. The name of the file to which we write the contents. +// contents: scalar. The content to be written to the output file. +// +// Returns the created operation. +func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "WriteFile", + Input: []tf.Input{ + filename, contents, + }, + } + return scope.AddOperation(opspec) +} + +// UniformCandidateSamplerAttr is an optional argument to UniformCandidateSampler. +type UniformCandidateSamplerAttr func(optionalAttr) + +// UniformCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func UniformCandidateSamplerSeed(value int64) UniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// UniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func UniformCandidateSamplerSeed2(value int64) UniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a uniform distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns A vector of length num_sampled, in which each element is +// the ID of a sampled candidate.A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func UniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...UniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MutableHashTableV2", - + Type: "UniformCandidateSampler", + Input: []tf.Input{ + true_classes, + }, Attrs: attrs, } op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes the minimum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such +// that `segment_ids[j] == i`. +// +// If the min is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_min(c, tf.constant([0, 0, 1])) +// # ==> [[1, 2, 2, 1], +// # [5, 6, 7, 8]] +// ``` +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMin", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) return op.Output(0) } @@ -34691,120 +36134,59 @@ func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtyp return op.Output(0) } -// LoadTPUEmbeddingAdagradParametersAttr is an optional argument to LoadTPUEmbeddingAdagradParameters. -type LoadTPUEmbeddingAdagradParametersAttr func(optionalAttr) - -// LoadTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 +// Looks up keys in a table, outputs the corresponding values. // -// REQUIRES: value >= -1 -func LoadTPUEmbeddingAdagradParametersTableId(value int64) LoadTPUEmbeddingAdagradParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingAdagradParametersTableName(value string) LoadTPUEmbeddingAdagradParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load Adagrad embedding parameters. +// The tensor `keys` must of the same type as the keys of the table. +// The output `values` is of the type of the table values. // -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. +// The scalar `default_value` is the value output for keys not present in the +// table. It must also be of the same type as the table values. // // Arguments: -// parameters: Value of parameters used in the Adagrad optimization algorithm. -// accumulators: Value of accumulators used in the Adagrad optimization algorithm. +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. // // -// -// Returns the created operation. -func LoadTPUEmbeddingAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersAttr) (o *tf.Operation) { +// Returns Same shape as `keys`. Values found in the table, or `default_values` +// for missing keys. +func LookupTableFindV2(scope *Scope, table_handle tf.Output, keys tf.Output, default_value tf.Output) (values tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingAdagradParameters", + Type: "LookupTableFindV2", Input: []tf.Input{ - parameters, accumulators, + table_handle, keys, default_value, }, - Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// NonMaxSuppressionV5Attr is an optional argument to NonMaxSuppressionV5. -type NonMaxSuppressionV5Attr func(optionalAttr) +// AssertAttr is an optional argument to Assert. +type AssertAttr func(optionalAttr) -// NonMaxSuppressionV5PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. +// AssertSummarize sets the optional summarize attribute to value. // -// value: If true, the output `selected_indices` is padded to be of length -// `max_output_size`. Defaults to false. -// If not specified, defaults to false -func NonMaxSuppressionV5PadToMaxOutputSize(value bool) NonMaxSuppressionV5Attr { +// value: Print this many entries of each tensor. +// If not specified, defaults to 3 +func AssertSummarize(value int64) AssertAttr { return func(m optionalAttr) { - m["pad_to_max_output_size"] = value + m["summarize"] = value } } -// Greedily selects a subset of bounding boxes in descending order of score, +// Asserts that the given condition is true. // -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes with score less than -// `score_threshold` are removed. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system and more -// generally is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression_v2( -// boxes, scores, max_output_size, iou_threshold, score_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) -// This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. -// Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score -// of other overlapping boxes instead of directly causing them to be pruned. -// To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be -// larger than 0. +// If `condition` evaluates to false, print the list of tensors in `data`. +// `summarize` determines how many entries of the tensors to print. // // Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// iou_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too much with respect to IOU. -// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove -// boxes based on score. -// soft_nms_sigma: A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et -// al (c.f. https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which -// is default), we fall back to standard (hard) NMS. +// condition: The condition to evaluate. +// data: The tensors to print out when condition is false. // -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`.A 1-D float tensor of shape `[M]` representing the corresponding -// scores for each selected box, where `M <= max_output_size`. Scores only differ -// from corresponding input scores when using Soft NMS (i.e. when -// `soft_nms_sigma>0`)A 0-D integer tensor representing the number of valid elements in -// `selected_indices`, with the valid elements appearing first. -func NonMaxSuppressionV5(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, soft_nms_sigma tf.Output, optional ...NonMaxSuppressionV5Attr) (selected_indices tf.Output, selected_scores tf.Output, valid_outputs tf.Output) { +// Returns the created operation. +func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -34813,235 +36195,56 @@ func NonMaxSuppressionV5(scope *Scope, boxes tf.Output, scores tf.Output, max_ou a(attrs) } opspec := tf.OpSpec{ - Type: "NonMaxSuppressionV5", + Type: "Assert", Input: []tf.Input{ - boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, + condition, tf.OutputList(data), }, Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) + return scope.AddOperation(opspec) } -// Outputs the single element from the given dataset. +// PrintV2Attr is an optional argument to PrintV2. +type PrintV2Attr func(optionalAttr) + +// PrintV2OutputStream sets the optional output_stream attribute to value. +// +// value: A string specifying the output stream or logging level to print to. +// If not specified, defaults to "stderr" +func PrintV2OutputStream(value string) PrintV2Attr { + return func(m optionalAttr) { + m["output_stream"] = value + } +} + +// PrintV2End sets the optional end attribute to value. +// If not specified, defaults to "\n" +func PrintV2End(value string) PrintV2Attr { + return func(m optionalAttr) { + m["end"] = value + } +} + +// Prints a string scalar. +// +// Prints a string scalar to the desired output_stream. // // Arguments: -// dataset: A handle to a dataset that contains a single element. -// -// -// -// Returns The components of the single element of `input`. -func DatasetToSingleElement(scope *Scope, dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "DatasetToSingleElement", - Input: []tf.Input{ - dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if components, idx, err = makeOutputList(op, idx, "components"); err != nil { - scope.UpdateErr("DatasetToSingleElement", err) - return - } - return components -} - -// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2. -type MutableDenseHashTableV2Attr func(optionalAttr) - -// MutableDenseHashTableV2Container sets the optional container attribute to value. -// -// value: If non-empty, this table is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this table is shared under the given name across -// multiple sessions. -// If not specified, defaults to "" -func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. -// If not specified, defaults to false -func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["use_node_name_sharing"] = value - } -} - -// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value. -// -// value: The shape of each value. -// If not specified, defaults to <> -func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["value_shape"] = value - } -} - -// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value. -// -// value: The initial number of hash table buckets. Must be a power -// to 2. -// If not specified, defaults to 131072 -func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["initial_num_buckets"] = value - } -} - -// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value. -// -// value: The maximum ratio between number of entries and number of -// buckets before growing the table. Must be between 0 and 1. -// If not specified, defaults to 0.8 -func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { - return func(m optionalAttr) { - m["max_load_factor"] = value - } -} - -// Creates an empty hash table that uses tensors as the backing store. -// -// It uses "open addressing" with quadratic reprobing to resolve -// collisions. -// -// This op creates a mutable hash table, specifying the type of its keys and -// values. Each value must be a scalar. Data can be inserted into the table using -// the insert operations. It does not support the initialization operation. -// -// Arguments: -// empty_key: The key used to represent empty key buckets internally. Must not -// be used in insert or lookup operations. -// -// value_dtype: Type of the table values. -// -// Returns Handle to a table. -func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, deleted_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"value_dtype": value_dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MutableDenseHashTableV2", - Input: []tf.Input{ - empty_key, deleted_key, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that changes the batch size. -// -// Creates a dataset that changes the batch size of the dataset to current batch -// size // num_workers. -// -// Arguments: -// input_dataset: A variant tensor representing the input dataset. -// num_workers: A scalar representing the number of workers to distribute this batch across. As -// a result of this transformation the current batch size would end up being -// divided by this parameter. -// -// -func ExperimentalRebatchDataset(scope *Scope, input_dataset tf.Output, num_workers tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalRebatchDataset", - Input: []tf.Input{ - input_dataset, num_workers, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2. -type InitializeTableFromTextFileV2Attr func(optionalAttr) - -// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value. -// -// value: Number of elements of the file, use -1 if unknown. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr { - return func(m optionalAttr) { - m["vocab_size"] = value - } -} - -// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value. -// -// value: Delimiter to separate fields in a line. -// If not specified, defaults to "\t" -func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr { - return func(m optionalAttr) { - m["delimiter"] = value - } -} - -// Initializes a table from a text file. -// -// It inserts one key-value pair into the table for each line of the file. -// The key and value is extracted from the whole line content, elements from the -// split line based on `delimiter` or the line number (starting from zero). -// Where to extract the key and value from a line is specified by `key_index` and -// `value_index`. -// -// - A value of -1 means use the line number(starting from zero), expects `int64`. -// - A value of -2 means use the whole line content, expects `string`. -// - A value >= 0 means use the index (starting at zero) of the split line based -// on `delimiter`. -// -// Arguments: -// table_handle: Handle to a table which will be initialized. -// filename: Filename of a vocabulary text file. -// key_index: Column index in a line to get the table `key` values from. -// value_index: Column index that represents information of a line to get the table -// `value` values from. +// input: The string scalar to print. // // Returns the created operation. -func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) { +func PrintV2(scope *Scope, input tf.Output, optional ...PrintV2Attr) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "InitializeTableFromTextFileV2", + Type: "PrintV2", Input: []tf.Input{ - table_handle, filename, + input, }, Attrs: attrs, } @@ -35108,29 +36311,25 @@ func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr return op.Output(0) } -// Returns the element-wise sum of a list of tensors. -// -// `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not -// wait for all of its inputs to be ready before beginning to sum. This can -// save memory if inputs are ready at different times, since minimum temporary -// storage is proportional to the output size rather than the inputs size. -// -// Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable. -// -// Returns a `Tensor` of same shape and type as the elements of `inputs`. +// Increments variable pointed to by 'resource' until it reaches 'limit'. // // Arguments: -// inputs: A list of `Tensor` objects, each with same shape and type. -// shape: Shape of elements of `inputs`. -func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Output) { +// resource: Should be from a scalar `Variable` node. +// limit: If incrementing ref would bring it above limit, instead generates an +// 'OutOfRange' error. +// +// +// Returns A copy of the input before increment. If nothing else modifies the +// input, the values produced will all be distinct. +func ResourceCountUpTo(scope *Scope, resource tf.Output, limit int64, T tf.DataType) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"shape": shape} + attrs := map[string]interface{}{"limit": limit, "T": T} opspec := tf.OpSpec{ - Type: "AccumulateNV2", + Type: "ResourceCountUpTo", Input: []tf.Input{ - tf.OutputList(inputs), + resource, }, Attrs: attrs, } @@ -35138,6 +36337,26 @@ func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Out return op.Output(0) } +// Resizes the list. +// +// +// input_handle: the input list +// size: size of the output list +// +func TensorListResize(scope *Scope, input_handle tf.Output, size tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListResize", + Input: []tf.Input{ + input_handle, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Outputs a `Summary` protocol buffer with scalar values. // // The input `tags` and `values` must have the same shape. The generated summary @@ -35162,147 +36381,311 @@ func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.O return op.Output(0) } -// Returns the element-wise max of two SparseTensors. +// Saves the input tensors to disk. // -// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// The size of `tensor_names` must match the number of tensors in `data`. `data[i]` +// is written to `filename` with name `tensor_names[i]`. +// +// See also `SaveSlices`. // // Arguments: -// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, in the canonical lexicographic ordering. -// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. -// a_shape: 1-D. Shape of the input SparseTensor. -// b_indices: counterpart to `a_indices` for the other operand. -// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. -// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// filename: Must have a single element. The name of the file to which we write +// the tensor. +// tensor_names: Shape `[N]`. The names of the tensors to be saved. +// data: `N` tensors to save. // -// Returns 2-D. The indices of the output SparseTensor.1-D. The values of the output SparseTensor. -func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { +// Returns the created operation. +func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SparseSparseMaximum", + Type: "Save", Input: []tf.Input{ - a_indices, a_values, a_shape, b_indices, b_values, b_shape, + filename, tensor_names, tf.OutputList(data), }, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) + return scope.AddOperation(opspec) } -// Provides the time since epoch in seconds. +// RetrieveTPUEmbeddingAdadeltaParametersAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParameters. +type RetrieveTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 // -// Returns the timestamp as a `float64` for seconds since the Unix epoch. +// REQUIRES: value >= -1 +func RetrieveTPUEmbeddingAdadeltaParametersTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// Retrieve Adadelta embedding parameters. // -// Note: the timestamp is computed when the op is executed, not when it is added -// to the graph. -func Timestamp(scope *Scope) (ts tf.Output) { +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the Adadelta optimization algorithm.Parameter accumulators updated by the Adadelta optimization algorithm.Parameter updates updated by the Adadelta optimization algorithm. +func RetrieveTPUEmbeddingAdadeltaParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "Timestamp", + Type: "RetrieveTPUEmbeddingAdadeltaParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MutableHashTableV2Attr is an optional argument to MutableHashTableV2. +type MutableHashTableV2Attr func(optionalAttr) + +// MutableHashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableV2Container(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableV2", + + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// An Op to permute tensors across replicated TPU instances. +// Outputs a `Summary` protocol buffer with a histogram. // -// Each instance supplies its own input. +// The generated +// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) +// has one summary value containing a histogram for `values`. // -// For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing -// source_target_pairs=`[[0,1],[1,2],[2,3],[3,0]]` gets the outputs: -// `[D, A, B, C]`. +// This op reports an `InvalidArgument` error if any value is not finite. // // Arguments: -// input: The local input to be permuted. Currently only supports float and -// bfloat16. -// source_target_pairs: A tensor with shape [num_pairs, 2]. +// tag: Scalar. Tag to use for the `Summary.Value`. +// values: Any shape. Values to use to build the histogram. // -// Returns The permuted input. -func CollectivePermute(scope *Scope, input tf.Output, source_target_pairs tf.Output) (output tf.Output) { +// Returns Scalar. Serialized `Summary` protocol buffer. +func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "CollectivePermute", + Type: "HistogramSummary", Input: []tf.Input{ - input, source_target_pairs, + tag, values, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// Returns a list list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`. +// Merges summaries. // -// tensor: The tensor to put on the list. -// input_handle: The old list. -// output_handle: A list with the elements of the old list followed by tensor. +// This op creates a +// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) +// protocol buffer that contains the union of all the values in the input +// summaries. +// +// When the Op is run, it reports an `InvalidArgument` error if multiple values +// in the summaries to merge use the same tag. +// +// Arguments: +// inputs: Can be of any shape. Each must contain serialized `Summary` protocol +// buffers. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MergeSummary", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates and returns an empty tensor list. +// +// All list elements must be tensors of dtype element_dtype and shape compatible +// with element_shape. +// +// handle: an empty tensor list. // element_dtype: the type of elements in the list. // element_shape: a shape compatible with that of elements in the list. -func TensorListPushBack(scope *Scope, input_handle tf.Output, tensor tf.Output) (output_handle tf.Output) { +func EmptyTensorList(scope *Scope, element_shape tf.Output, max_num_elements tf.Output, element_dtype tf.DataType) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "TensorListPushBack", + Type: "EmptyTensorList", Input: []tf.Input{ - input_handle, tensor, + element_shape, max_num_elements, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Returns the number of tensors in the input tensor list. +// ShapeAttr is an optional argument to Shape. +type ShapeAttr func(optionalAttr) + +// ShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeOutType(value tf.DataType) ShapeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns the shape of a tensor. // -// input_handle: the input list -// length: the number of tensors in the list -func TensorListLength(scope *Scope, input_handle tf.Output) (length tf.Output) { +// This operation returns a 1-D integer tensor representing the shape of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorListLength", + Type: "Shape", Input: []tf.Input{ - input_handle, + input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Returns a batched matrix tensor with new batched diagonal values. +// ResourceGatherAttr is an optional argument to ResourceGather. +type ResourceGatherAttr func(optionalAttr) + +// ResourceGatherBatchDims sets the optional batch_dims attribute to value. +// If not specified, defaults to 0 +func ResourceGatherBatchDims(value int64) ResourceGatherAttr { + return func(m optionalAttr) { + m["batch_dims"] = value + } +} + +// ResourceGatherValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func ResourceGatherValidateIndices(value bool) ResourceGatherAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Gather slices from the variable pointed to by `resource` according to `indices`. // -// Given `input` and `diagonal`, this operation returns a tensor with the -// same shape and values as `input`, except for the main diagonal of the -// innermost matrices. These will be overwritten by the values in `diagonal`. +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: // -// The output is computed as follows: +// ```python +// # Scalar indices +// output[:, ..., :] = params[indices, :, ... :] // -// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has -// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a -// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where: +// # Vector indices +// output[i, :, ..., :] = params[indices[i], :, ... :] // -// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`. -// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`. -// -// Arguments: -// input: Rank `k+1`, where `k >= 1`. -// diagonal: Rank `k`, where `k >= 1`. -// -// Returns Rank `k+1`, with `output.shape = input.shape`. -func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) { +// # Higher rank indices +// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// ``` +func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "MatrixSetDiag", + Type: "ResourceGather", Input: []tf.Input{ - input, diagonal, + resource, indices, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -35332,37 +36715,51 @@ func TensorListPopBack(scope *Scope, input_handle tf.Output, element_shape tf.Ou return op.Output(0), op.Output(1) } -// TensorListStackAttr is an optional argument to TensorListStack. -type TensorListStackAttr func(optionalAttr) +// ResizeBicubicAttr is an optional argument to ResizeBicubic. +type ResizeBicubicAttr func(optionalAttr) -// TensorListStackNumElements sets the optional num_elements attribute to value. -// If not specified, defaults to -1 -func TensorListStackNumElements(value int64) TensorListStackAttr { +// ResizeBicubicAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr { return func(m optionalAttr) { - m["num_elements"] = value + m["align_corners"] = value } } -// Stacks all tensors in the list. +// ResizeBicubicHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBicubicHalfPixelCenters(value bool) ResizeBicubicAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using bicubic interpolation. // -// Requires that all tensors have the same shape. +// Input images can be of different types but output images are always float. // -// input_handle: the input list -// tensor: the gathered result -// num_elements: optional. If not -1, the number of elements in the list. +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. // -func TensorListStack(scope *Scope, input_handle tf.Output, element_shape tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) { +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"element_dtype": element_dtype} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "TensorListStack", + Type: "ResizeBicubic", Input: []tf.Input{ - input_handle, element_shape, + images, size, }, Attrs: attrs, } @@ -35458,6 +36855,54 @@ func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output return op.Output(0), op.Output(1), op.Output(2) } +// MaxPoolGradAttr is an optional argument to MaxPoolGrad. +type MaxPoolGradAttr func(optionalAttr) + +// MaxPoolGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradDataFormat(value string) MaxPoolGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients w.r.t. the input to `max_pool`. +func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a TensorList which, when stacked, has the value of `tensor`. // // Each tensor in the result list corresponds to one row of the input tensor. @@ -35478,23 +36923,59 @@ func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Outpu return op.Output(0) } -// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// RandomCropAttr is an optional argument to RandomCrop. +type RandomCropAttr func(optionalAttr) + +// RandomCropSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomCropSeed(value int64) RandomCropAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomCropSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomCropSeed2(value int64) RandomCropAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Randomly crop `image`. +// +// DEPRECATED at GraphDef version 8: Random crop is now pure Python +// +// `size` is a 1-D int64 tensor with 2 elements representing the crop height and +// width. The values must be non negative. +// +// This Op picks a random location in `image` and crops a `height` by `width` +// rectangle from that location. The random location is picked so the cropped +// area will fit inside the original image. // // Arguments: -// serialized: A scalar string containing a serialized TensorProto proto. -// out_type: The type of the serialized tensor. The provided type must match the -// type of the serialized tensor and no implicit conversion will take place. +// image: 3-D of shape `[height, width, channels]`. +// size: 1-D of length 2 containing: `crop_height`, `crop_width`.. // -// Returns A Tensor of type `out_type`. -func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { +// Returns 3-D of shape `[crop_height, crop_width, channels].` +func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"out_type": out_type} + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "ParseTensor", + Type: "RandomCrop", Input: []tf.Input{ - serialized, + image, size, }, Attrs: attrs, } @@ -35522,6 +37003,152 @@ func TensorListElementShape(scope *Scope, input_handle tf.Output, shape_type tf. return op.Output(0) } +// Returns the complex conjugate of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// complex numbers that are the complex conjugate of each element in `input`. The +// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the +// real part and *b* is the imaginary part. +// +// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] +// ``` +func Conj(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Conj", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor. +type ResizeNearestNeighborAttr func(optionalAttr) + +// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeNearestNeighborHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeNearestNeighborHalfPixelCenters(value bool) ResizeNearestNeighborAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using nearest neighbor interpolation. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeNearestNeighbor", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap. +type AddSparseToTensorsMapAttr func(optionalAttr) + +// AddSparseToTensorsMapContainer sets the optional container attribute to value. +// +// value: The container name for the `SparseTensorsMap` created by this op. +// If not specified, defaults to "" +func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` created by this op. +// If blank, the new Operation's unique name is used. +// If not specified, defaults to "" +func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Add a `SparseTensor` to a `SparseTensorsMap` return its handle. +// +// A `SparseTensor` is represented by three tensors: `sparse_indices`, +// `sparse_values`, and `sparse_shape`. +// +// This operator takes the given `SparseTensor` and adds it to a container +// object (a `SparseTensorsMap`). A unique key within this container is generated +// in the form of an `int64`, and this is the value that is returned. +// +// The `SparseTensor` can then be read out as part of a minibatch by passing +// the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure +// the correct `SparseTensorsMap` is accessed, ensure that the same +// `container` and `shared_name` are passed to that Op. If no `shared_name` +// is provided here, instead use the *name* of the Operation created by calling +// `AddSparseToTensorsMap` as the `shared_name` passed to +// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the `SparseTensor`. +// sparse_values: 1-D. The `values` of the `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the `SparseTensor`. +// +// Returns 0-D. The handle of the `SparseTensor` now stored in the +// `SparseTensorsMap`. +func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AddSparseToTensorsMap", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // List of the given size with empty elements. // // element_shape: the shape of the future elements of the list @@ -35544,49 +37171,23 @@ func TensorListReserve(scope *Scope, element_shape tf.Output, num_elements tf.Ou return op.Output(0) } -// Creates a TensorList by indexing into a Tensor. +// Creates a Tensor by indexing into the TensorList. // -// Each member of the TensorList corresponds to one row of the input tensor, +// Each row in the produced Tensor corresponds to the element in the TensorList // specified by the given index (see `tf.gather`). // -// tensor: The input tensor. +// input_handle: The input tensor list. // indices: The indices used to index into the list. -// element_shape: The shape of the elements in the list (can be less specified than -// the shape of the tensor). -// num_elements: The size of the output list. Must be large enough to accommodate -// the largest index in indices. If -1, the list is just large enough to include -// the largest index in indices. -// output_handle: The TensorList. -func TensorListScatterV2(scope *Scope, tensor tf.Output, indices tf.Output, element_shape tf.Output, num_elements tf.Output) (output_handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorListScatterV2", - Input: []tf.Input{ - tensor, indices, element_shape, num_elements, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns the item in the list with the given index. -// -// input_handle: the list -// index: the position in the list from which an element will be retrieved -// item: the element at that position -// -// -func TensorListGetItem(scope *Scope, input_handle tf.Output, index tf.Output, element_shape tf.Output, element_dtype tf.DataType) (item tf.Output) { +// values: The tensor. +func TensorListGather(scope *Scope, input_handle tf.Output, indices tf.Output, element_shape tf.Output, element_dtype tf.DataType) (values tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"element_dtype": element_dtype} opspec := tf.OpSpec{ - Type: "TensorListGetItem", + Type: "TensorListGather", Input: []tf.Input{ - input_handle, index, element_shape, + input_handle, indices, element_shape, }, Attrs: attrs, } @@ -35594,21 +37195,48 @@ func TensorListGetItem(scope *Scope, input_handle tf.Output, index tf.Output, el return op.Output(0) } -// Resizes the list. +// PreventGradientAttr is an optional argument to PreventGradient. +type PreventGradientAttr func(optionalAttr) + +// PreventGradientMessage sets the optional message attribute to value. // +// value: Will be printed in the error when anyone tries to differentiate +// this operation. +// If not specified, defaults to "" +func PreventGradientMessage(value string) PreventGradientAttr { + return func(m optionalAttr) { + m["message"] = value + } +} + +// An identity op that triggers an error if a gradient is requested. // -// input_handle: the input list -// size: size of the output list +// When executed in a graph, this op outputs its input tensor as-is. // -func TensorListResize(scope *Scope, input_handle tf.Output, size tf.Output) (output_handle tf.Output) { +// When building ops to compute gradients, the TensorFlow gradient system +// will return an error when trying to lookup the gradient of this op, +// because no gradient must ever be registered for this function. This +// op exists to prevent subtle bugs from silently returning unimplemented +// gradients in some corner cases. +// +// Arguments: +// input: any tensor. +// +// Returns the same input tensor. +func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "TensorListResize", + Type: "PreventGradient", Input: []tf.Input{ - input_handle, size, + input, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -35661,122 +37289,46 @@ func TensorListScatterIntoExistingList(scope *Scope, input_handle tf.Output, ten return op.Output(0) } -// FusedBatchNormGradV3Attr is an optional argument to FusedBatchNormGradV3. -type FusedBatchNormGradV3Attr func(optionalAttr) - -// FusedBatchNormGradV3Epsilon sets the optional epsilon attribute to value. +// Removes keys and its associated values from a table. // -// value: A small float number added to the variance of x. -// If not specified, defaults to 0.0001 -func FusedBatchNormGradV3Epsilon(value float32) FusedBatchNormGradV3Attr { - return func(m optionalAttr) { - m["epsilon"] = value - } -} - -// FusedBatchNormGradV3DataFormat sets the optional data_format attribute to value. -// -// value: The data format for y_backprop, x, x_backprop. -// Either "NHWC" (default) or "NCHW". -// If not specified, defaults to "NHWC" -func FusedBatchNormGradV3DataFormat(value string) FusedBatchNormGradV3Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// FusedBatchNormGradV3IsTraining sets the optional is_training attribute to value. -// -// value: A bool value to indicate the operation is for training (default) -// or inference. -// If not specified, defaults to true -func FusedBatchNormGradV3IsTraining(value bool) FusedBatchNormGradV3Attr { - return func(m optionalAttr) { - m["is_training"] = value - } -} - -// Gradient for batch normalization. -// -// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". -// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// The tensor `keys` must of the same type as the keys of the table. Keys not +// already in the table are silently ignored. // // Arguments: -// y_backprop: A 4D Tensor for the gradient with respect to y. -// x: A 4D Tensor for input data. -// scale: A 1D Tensor for scaling factor, to scale the normalized x. -// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch -// mean to be reused in gradient computation. When is_training is -// False, a 1D Tensor for the population mean to be reused in both -// 1st and 2nd order gradient computation. -// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch -// variance (inverted variance in the cuDNN case) to be reused in -// gradient computation. When is_training is False, a 1D Tensor -// for the population variance to be reused in both 1st and 2nd -// order gradient computation. -// reserve_space_3: When is_training is True, a 1D Tensor for some intermediate results to be reused -// in gradient computation. When is_training is False, a dummy empty Tensor will be -// created. +// table_handle: Handle to the table. +// keys: Any shape. Keys of the elements to remove. // -// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input -// in FusedBatchNorm. -func FusedBatchNormGradV3(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output, optional ...FusedBatchNormGradV3Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_4 tf.Output, reserve_space_5 tf.Output) { +// Returns the created operation. +func LookupTableRemoveV2(scope *Scope, table_handle tf.Output, keys tf.Output) (o *tf.Operation) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } opspec := tf.OpSpec{ - Type: "FusedBatchNormGradV3", + Type: "LookupTableRemoveV2", Input: []tf.Input{ - y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3, + table_handle, keys, }, - Attrs: attrs, } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) + return scope.AddOperation(opspec) } -// AnyAttr is an optional argument to Any. -type AnyAttr func(optionalAttr) - -// AnyKeepDims sets the optional keep_dims attribute to value. +// Checks a tensor for NaN and Inf values. // -// value: If true, retain reduced dimensions with length 1. -// If not specified, defaults to false -func AnyKeepDims(value bool) AnyAttr { - return func(m optionalAttr) { - m["keep_dims"] = value - } -} - -// Computes the "logical or" of elements across dimensions of a tensor. -// -// Reduces `input` along the dimensions given in `axis`. Unless -// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in -// `axis`. If `keep_dims` is true, the reduced dimensions are -// retained with length 1. +// When run, reports an `InvalidArgument` error if `tensor` has any values +// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is. // // Arguments: -// input: The tensor to reduce. -// axis: The dimensions to reduce. Must be in the range -// `[-rank(input), rank(input))`. // -// Returns The reduced tensor. -func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { +// message: Prefix of the error message. +func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"message": message} opspec := tf.OpSpec{ - Type: "Any", + Type: "CheckNumerics", Input: []tf.Input{ - input, axis, + tensor, }, Attrs: attrs, } @@ -35784,61 +37336,6 @@ func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (ou return op.Output(0) } -// Computes the determinant of one or more square matrices. -// -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. The output is a tensor containing the determinants -// for all input submatrices `[..., :, :]`. -// -// Arguments: -// input: Shape is `[..., M, M]`. -// -// Returns Shape is `[...]`. -func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixDeterminant", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the sign and the log of the absolute value of the determinant of -// -// one or more square matrices. -// -// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions -// form square matrices. The outputs are two tensors containing the signs and -// absolute values of the log determinants for all N input submatrices -// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant). -// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU -// is the LU decomposition of the input and P is the corresponding -// permutation matrix. -// -// Arguments: -// input: Shape is `[N, M, M]`. -// -// Returns The signs of the log determinants of the inputs. Shape is `[N]`.The logs of the absolute values of the determinants -// of the N input matrices. Shape is `[N]`. -func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LogMatrixDeterminant", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - // MatrixInverseAttr is an optional argument to MatrixInverse. type MatrixInverseAttr func(optionalAttr) @@ -35891,38 +37388,32 @@ func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) return op.Output(0) } -// Computes the matrix logarithm of one or more square matrices: -// -// -// \\(log(exp(A)) = A\\) -// -// This op is only defined for complex matrices. If A is positive-definite and -// real, then casting to a complex matrix, taking the logarithm and casting back -// to a real matrix will give the correct result. -// -// This function computes the matrix logarithm using the Schur-Parlett algorithm. -// Details of the algorithm can be found in Section 11.6.2 of: -// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008. -// ISBN 978-0-898716-46-7. +// Computes the Cholesky decomposition of one or more square matrices. // // The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. The output is a tensor of the same shape as the input -// containing the exponential for all input submatrices `[..., :, :]`. +// form square matrices. +// +// The input has to be symmetric and positive definite. Only the lower-triangular +// part of the input will be used for this operation. The upper-triangular part +// will not be read. +// +// The output is a tensor of the same shape as the input +// containing the Cholesky decompositions for all input submatrices `[..., :, :]`. +// +// **Note**: The gradient computation on GPU is faster for large matrices but +// not for large batch dimensions when the submatrices are small. In this +// case it might be faster to use the CPU. // // Arguments: // input: Shape is `[..., M, M]`. // // Returns Shape is `[..., M, M]`. -// -// @compatibility(scipy) -// Equivalent to scipy.linalg.logm -// @end_compatibility -func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) { +func Cholesky(scope *Scope, input tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "MatrixLogarithm", + Type: "Cholesky", Input: []tf.Input{ input, }, @@ -35931,249 +37422,34 @@ func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } -// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2. -type SelfAdjointEigV2Attr func(optionalAttr) - -// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value. +// Computes the reverse mode backpropagated gradient of the Cholesky algorithm. // -// value: If `True` then eigenvectors will be computed and returned in `v`. -// Otherwise, only the eigenvalues will be computed. -// If not specified, defaults to true -func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr { - return func(m optionalAttr) { - m["compute_v"] = value - } -} - -// Computes the eigen decomposition of one or more square self-adjoint matrices. -// -// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in -// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues -// are sorted in non-decreasing order. -// -// ```python -// # a is a tensor. -// # e is a tensor of eigenvalues. -// # v is a tensor of eigenvectors. -// e, v = self_adjoint_eig(a) -// e = self_adjoint_eig(a, compute_v=False) -// ``` +// For an explanation see "Differentiation of the Cholesky algorithm" by +// Iain Murray http://arxiv.org/abs/1602.07527. // // Arguments: -// input: `Tensor` input of shape `[N, N]`. +// l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`. +// Algorithm depends only on lower triangular part of the innermost matrices of +// this tensor. +// grad: df/dl where f is some scalar function. Shape is `[..., M, M]`. +// Algorithm depends only on lower triangular part of the innermost matrices of +// this tensor. // -// Returns Eigenvalues. Shape is `[N]`.Eigenvectors. Shape is `[N, N]`. -func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SelfAdjointEigV2", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Splits a tensor into `num_split` tensors along one dimension. -// -// Arguments: -// axis: 0-D. The dimension along which to split. Must be in the range -// `[-rank(value), rank(value))`. -// value: The tensor to split. -// num_split: The number of ways to split. Must evenly divide -// `value.shape[split_dim]`. -// -// Returns They are identically shaped tensors, whose shape matches that of `value` -// except along `axis`, where their sizes are -// `values.shape[split_dim] / num_split`. -func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_split": num_split} - opspec := tf.OpSpec{ - Type: "Split", - Input: []tf.Input{ - axis, value, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("Split", err) - return - } - return output -} - -// Computes the gradient for the sqrt of `x` wrt its input. -// -// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy` -// is the corresponding input gradient. -func SqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { +// Returns Symmetrized version of df/dA . Shape is `[..., M, M]` +func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "SqrtGrad", + Type: "CholeskyGrad", Input: []tf.Input{ - y, dy, + l, grad, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// LuAttr is an optional argument to Lu. -type LuAttr func(optionalAttr) - -// LuOutputIdxType sets the optional output_idx_type attribute to value. -// If not specified, defaults to DT_INT32 -func LuOutputIdxType(value tf.DataType) LuAttr { - return func(m optionalAttr) { - m["output_idx_type"] = value - } -} - -// Computes the LU decomposition of one or more square matrices. -// -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. -// -// The input has to be invertible. -// -// The output consists of two tensors LU and P containing the LU decomposition -// of all input submatrices `[..., :, :]`. LU encodes the lower triangular and -// upper triangular factors. -// -// For each input submatrix of shape `[M, M]`, L is a lower triangular matrix of -// shape `[M, M]` with unit diagonal whose entries correspond to the strictly lower -// triangular part of LU. U is a upper triangular matrix of shape `[M, M]` whose -// entries correspond to the upper triangular part, including the diagonal, of LU. -// -// P represents a permutation matrix encoded as a list of indices each between `0` -// and `M-1`, inclusive. If P_mat denotes the permutation matrix corresponding to -// P, then the L, U and P satisfies P_mat * input = L * U. -// -// Arguments: -// input: A tensor of shape `[..., M, M]` whose inner-most 2 dimensions form matrices of -// size `[M, M]`. -// -// Returns A tensor of shape `[..., M, M]` whose strictly lower triangular part denotes the -// lower triangular factor `L` with unit diagonal, and whose upper triangular part -// denotes the upper triangular factor `U`.Permutation of the rows encoded as a list of indices in `0..M-1`. Shape is -// `[..., M]`. -// @compatibility(scipy) -// Similar to `scipy.linalg.lu`, except the triangular factors `L` and `U` are -// packed into a single tensor, the permutation is applied to `input` instead of -// the right hand side and the permutation `P` is returned as a list of indices -// instead of a permutation matrix. -// @end_compatibility -func Lu(scope *Scope, input tf.Output, optional ...LuAttr) (lu tf.Output, p tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Lu", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// RandomPoissonV2Attr is an optional argument to RandomPoissonV2. -type RandomPoissonV2Attr func(optionalAttr) - -// RandomPoissonV2Seed sets the optional seed attribute to value. -// -// value: If either `seed` or `seed2` are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomPoissonV2Seed2 sets the optional seed2 attribute to value. -// -// value: A second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// RandomPoissonV2Dtype sets the optional dtype attribute to value. -// If not specified, defaults to DT_INT64 -func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr { - return func(m optionalAttr) { - m["dtype"] = value - } -} - -// Outputs random values from the Poisson distribution(s) described by rate. -// -// This op uses two algorithms, depending on rate. If rate >= 10, then -// the algorithm by Hormann is used to acquire samples via -// transformation-rejection. -// See http://www.sciencedirect.com/science/article/pii/0167668793909974. -// -// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform -// random variables. -// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer -// Programming, Volume 2. Addison Wesley -// -// Arguments: -// shape: 1-D integer tensor. Shape of independent samples to draw from each -// distribution described by the shape parameters given in rate. -// rate: A tensor in which each scalar is a "rate" parameter describing the -// associated poisson distribution. -// -// Returns A tensor with shape `shape + shape(rate)`. Each slice -// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for -// `rate[i0, i1, ...iN]`. -func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomPoissonV2", - Input: []tf.Input{ - shape, rate, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // MatrixSolveAttr is an optional argument to MatrixSolve. type MatrixSolveAttr func(optionalAttr) @@ -36221,177 +37497,60 @@ func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...Matr return op.Output(0) } -// Produces a summary of any statistics recorded by the given statistics manager. -func ExperimentalStatsAggregatorSummary(scope *Scope, iterator tf.Output) (summary tf.Output) { - if scope.Err() != nil { - return +// FusedBatchNormAttr is an optional argument to FusedBatchNorm. +type FusedBatchNormAttr func(optionalAttr) + +// FusedBatchNormEpsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr { + return func(m optionalAttr) { + m["epsilon"] = value } - opspec := tf.OpSpec{ - Type: "ExperimentalStatsAggregatorSummary", - Input: []tf.Input{ - iterator, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) } -// ShuffleDatasetAttr is an optional argument to ShuffleDataset. -type ShuffleDatasetAttr func(optionalAttr) - -// ShuffleDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value. +// FusedBatchNormDataFormat sets the optional data_format attribute to value. // -// value: If true, each iterator over this dataset will be given -// a different pseudorandomly generated seed, based on a sequence seeded by the -// `seed` and `seed2` inputs. If false, each iterator will be given the same -// seed, and repeated iteration over this dataset will yield the exact same -// sequence of results. +// value: The data format for x and y. Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormDataFormat(value string) FusedBatchNormAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormIsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. // If not specified, defaults to true -func ShuffleDatasetReshuffleEachIteration(value bool) ShuffleDatasetAttr { +func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr { return func(m optionalAttr) { - m["reshuffle_each_iteration"] = value + m["is_training"] = value } } -// Creates a dataset that shuffles elements from `input_dataset` pseudorandomly. +// Batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. // // Arguments: +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// offset: A 1D Tensor for offset, to shift to the normalized x. +// mean: A 1D Tensor for population mean. Used for inference only; +// must be empty for training. +// variance: A 1D Tensor for population variance. Used for inference only; +// must be empty for training. // -// buffer_size: The number of output elements to buffer in an iterator over -// this dataset. Compare with the `min_after_dequeue` attr when creating a -// `RandomShuffleQueue`. -// seed: A scalar seed for the random number generator. If either `seed` or -// `seed2` is set to be non-zero, the random number generator is seeded -// by the given seed. Otherwise, a random seed is used. -// seed2: A second scalar seed to avoid seed collision. -// -// -func ShuffleDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShuffleDatasetAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ShuffleDataset", - Input: []tf.Input{ - input_dataset, buffer_size, seed, seed2, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts a `RaggedTensor` into a `SparseTensor` with the same values. -// -// input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) -// output=SparseTensor(indices=sparse_indices, values=sparse_values, -// dense_shape=sparse_dense_shape) -// -// Arguments: -// rt_nested_splits: The `row_splits` for the `RaggedTensor`. -// rt_dense_values: The `flat_values` for the `RaggedTensor`. -// -// Returns The indices for the `SparseTensor`.The values of the `SparseTensor`.`sparse_dense_shape` is a tight bounding box of the input `RaggedTensor`. -func RaggedTensorToSparse(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output) (sparse_indices tf.Output, sparse_values tf.Output, sparse_dense_shape tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RaggedTensorToSparse", - Input: []tf.Input{ - tf.OutputList(rt_nested_splits), rt_dense_values, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve. -type MatrixTriangularSolveAttr func(optionalAttr) - -// MatrixTriangularSolveLower sets the optional lower attribute to value. -// -// value: Boolean indicating whether the innermost matrices in `matrix` are -// lower or upper triangular. -// If not specified, defaults to true -func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { - return func(m optionalAttr) { - m["lower"] = value - } -} - -// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value. -// -// value: Boolean indicating whether to solve with `matrix` or its (block-wise) -// adjoint. -// -// @compatibility(numpy) -// Equivalent to scipy.linalg.solve_triangular -// @end_compatibility -// If not specified, defaults to false -func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { - return func(m optionalAttr) { - m["adjoint"] = value - } -} - -// Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. -// -// -// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form -// square matrices. If `lower` is `True` then the strictly upper triangular part -// of each inner-most matrix is assumed to be zero and not accessed. -// If `lower` is False then the strictly lower triangular part of each inner-most -// matrix is assumed to be zero and not accessed. -// `rhs` is a tensor of shape `[..., M, K]`. -// -// The output is a tensor of shape `[..., M, K]`. If `adjoint` is -// `True` then the innermost matrices in `output` satisfy matrix equations -// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. -// If `adjoint` is `False` then the strictly then the innermost matrices in -// `output` satisfy matrix equations -// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. -// -// Example: -// ```python -// -// a = tf.constant([[3, 0, 0, 0], -// [2, 1, 0, 0], -// [1, 0, 1, 0], -// [1, 1, 1, 1]], dtype=tf.float32) -// -// b = tf.constant([[4], -// [2], -// [4], -// [2]], dtype=tf.float32) -// -// x = tf.linalg.triangular_solve(a, b, lower=True) -// x -// # -// -// # in python3 one can use `a@x` -// tf.matmul(a, x) -// # -// ``` -// -// Arguments: -// matrix: Shape is `[..., M, M]`. -// rhs: Shape is `[..., M, K]`. -// -// Returns Shape is `[..., M, K]`. -func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) { +// Returns A 4D Tensor for output data.A 1D Tensor for the computed batch mean, to be used by TensorFlow +// to compute the running mean.A 1D Tensor for the computed batch variance, to be used by +// TensorFlow to compute the running variance.A 1D Tensor for the computed batch mean, to be reused +// in the gradient computation.A 1D Tensor for the computed batch variance (inverted variance +// in the cuDNN case), to be reused in the gradient computation. +func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) { if scope.Err() != nil { return } @@ -36400,91 +37559,14 @@ func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, option a(attrs) } opspec := tf.OpSpec{ - Type: "MatrixTriangularSolve", + Type: "FusedBatchNorm", Input: []tf.Input{ - matrix, rhs, + x, scale, offset, mean, variance, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MatrixSolveLsAttr is an optional argument to MatrixSolveLs. -type MatrixSolveLsAttr func(optionalAttr) - -// MatrixSolveLsFast sets the optional fast attribute to value. -// If not specified, defaults to true -func MatrixSolveLsFast(value bool) MatrixSolveLsAttr { - return func(m optionalAttr) { - m["fast"] = value - } -} - -// Solves one or more linear least-squares problems. -// -// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions -// form real or complex matrices of size `[M, N]`. `Rhs` is a tensor of the same -// type as `matrix` and shape `[..., M, K]`. -// The output is a tensor shape `[..., N, K]` where each output matrix solves -// each of the equations -// `matrix[..., :, :]` * `output[..., :, :]` = `rhs[..., :, :]` -// in the least squares sense. -// -// We use the following notation for (complex) matrix and right-hand sides -// in the batch: -// -// `matrix`=\\(A \in \mathbb{C}^{m \times n}\\), -// `rhs`=\\(B \in \mathbb{C}^{m \times k}\\), -// `output`=\\(X \in \mathbb{C}^{n \times k}\\), -// `l2_regularizer`=\\(\lambda \in \mathbb{R}\\). -// -// If `fast` is `True`, then the solution is computed by solving the normal -// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then -// \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares -// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\). -// If \\(m \lt n\\) then `output` is computed as -// \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the -// minimum-norm solution to the under-determined linear system, i.e. -// \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\), -// subject to \\(A Z = B\\). Notice that the fast path is only numerically stable -// when \\(A\\) is numerically full rank and has a condition number -// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or \\(\lambda\\) is -// sufficiently large. -// -// If `fast` is `False` an algorithm based on the numerically robust complete -// orthogonal decomposition is used. This computes the minimum-norm -// least-squares solution, even when \\(A\\) is rank deficient. This path is -// typically 6-7 times slower than the fast path. If `fast` is `False` then -// `l2_regularizer` is ignored. -// -// Arguments: -// matrix: Shape is `[..., M, N]`. -// rhs: Shape is `[..., M, K]`. -// l2_regularizer: Scalar tensor. -// -// @compatibility(numpy) -// Equivalent to np.linalg.lstsq -// @end_compatibility -// -// Returns Shape is `[..., N, K]`. -func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MatrixSolveLs", - Input: []tf.Input{ - matrix, rhs, l2_regularizer, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) } // QrAttr is an optional argument to Qr. @@ -36541,6 +37623,68 @@ func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Ou return op.Output(0), op.Output(1) } +// Outputs random integers from a uniform distribution. +// +// The generated values are uniform integers in the range `[minval, maxval)`. +// The lower bound `minval` is included in the range, while the upper bound +// `maxval` is excluded. +// +// The random integers are slightly biased unless `maxval - minval` is an exact +// power of two. The bias is small for values of `maxval - minval` significantly +// smaller than the range of the output (either `2^32` or `2^64`). +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// minval: Minimum value (inclusive, scalar). +// maxval: Maximum value (exclusive, scalar). +// +// Returns Random values with specified shape. +func StatefulUniformInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatefulUniformInt", + Input: []tf.Input{ + resource, algorithm, shape, minval, maxval, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// The gradient of SparseFillEmptyRows. +// +// Takes vectors reverse_index_map, shaped `[N]`, and grad_values, +// shaped `[N_full]`, where `N_full >= N` and copies data into either +// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and +// `d_default_value` is a scalar. +// +// d_values[j] = grad_values[reverse_index_map[j]] +// d_default_value = sum_{k : 0 .. N_full - 1} ( +// grad_values[k] * 1{k not in reverse_index_map}) +// +// Arguments: +// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows. +// grad_values: 1-D. The gradients from backprop. +// +// Returns 1-D. The backprop into values.0-D. The backprop into default_value. +func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseFillEmptyRowsGrad", + Input: []tf.Input{ + reverse_index_map, grad_values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + // SvdAttr is an optional argument to Svd. type SvdAttr func(optionalAttr) @@ -36610,64 +37754,21 @@ func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf. return op.Output(0), op.Output(1), op.Output(2) } -// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. -type ResourceApplyMomentumAttr func(optionalAttr) - -// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var and accum tensors will be protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. -// -// value: If `True`, the tensor passed to compute grad will be -// var - lr * momentum * accum, so in the end, the var you get is actually -// var - lr * momentum * accum. -// If not specified, defaults to false -func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr { - return func(m optionalAttr) { - m["use_nesterov"] = value - } -} - -// Update '*var' according to the momentum scheme. Set use_nesterov = True if you -// -// want to use Nesterov momentum. -// -// accum = accum * momentum + grad -// var -= lr * accum -// -// Arguments: -// var_: Should be from a Variable(). -// accum: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// grad: The gradient. -// momentum: Momentum. Must be a scalar. -// -// Returns the created operation. -func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) { +// Creates a dataset that contains the elements of `input_dataset` ignoring errors. +func ExperimentalIgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ResourceApplyMomentum", + Type: "ExperimentalIgnoreErrorsDataset", Input: []tf.Input{ - var_, accum, lr, grad, momentum, + input_dataset, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } // Calculate product with tridiagonal matrix. @@ -36699,79 +37800,83 @@ func TridiagonalMatMul(scope *Scope, superdiag tf.Output, maindiag tf.Output, su return op.Output(0) } -// Reduces sparse updates into the variable referenced by `resource` using the `max` operation. +// Aggregates the summary of accumulated stats for the batch. // -// This operation computes -// -// # Scalar indices -// ref[indices, ...] = max(ref[indices, ...], updates[...]) -// -// # Vector indices (for each i) -// ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...]) -// -// # High rank indices (for each i, ..., j) -// ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) -// -// Duplicate entries are handled correctly: if multiple `indices` reference -// the same location, their contributions are combined. -// -// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. -// -//
-// -//
+// The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id. // // Arguments: -// resource: Should be from a `Variable` node. -// indices: A tensor of indices into the first dimension of `ref`. -// updates: A tensor of updated values to add to `ref`. +// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. +// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. +// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. +// feature_indices: int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]). +// Number of sparse entries across all instances from the batch. The first value is +// the index of the instance, the second is dimension of the feature. The second axis +// can only have 2 values, i.e., the input dense version of Tensor can only be matrix. +// feature_values: int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]). +// Number of sparse entries across all instances from the batch. The first value is +// the index of the instance, the second is dimension of the feature. +// feature_shape: int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]). +// The first axis can only have 2 values, [batch_size, feature_dimension]. +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature + 1. // -// Returns the created operation. -func ResourceScatterMax(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { +// Returns int32; Rank 2 indices of summary sparse Tensors (shape=[number of non zero statistics, 4]) +// The second axis can only be 4 including node id, feature dimension, bucket id, and statistics_dimension. +// statistics_dimension = logits_dimension + hessian_dimension.output Rank 1 Tensor (shape=[number of non zero statistics])output Rank 1 Tensor (shape=[4]) +// The tensor has following 4 values: [max_splits, feature_dimension, num_buckets, statistics_dimension], +// where statistics_dimension = gradient_dimension + hessian_dimension. gradient_dimension +// is the same as label_dimension, i.e., the output space. hessian_dimension can be the same +// as logits dimension when diagonal hessian is used, or label_dimension^2 when full +// hessian is used. +func BoostedTreesSparseAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature_indices tf.Output, feature_values tf.Output, feature_shape tf.Output, max_splits int64, num_buckets int64) (stats_summary_indices tf.Output, stats_summary_values tf.Output, stats_summary_shape tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} opspec := tf.OpSpec{ - Type: "ResourceScatterMax", + Type: "BoostedTreesSparseAggregateStats", Input: []tf.Input{ - resource, indices, updates, + node_ids, gradients, hessians, feature_indices, feature_values, feature_shape, }, + Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// ResourceApplyAddSignAttr is an optional argument to ResourceApplyAddSign. -type ResourceApplyAddSignAttr func(optionalAttr) +// TridiagonalSolveAttr is an optional argument to TridiagonalSolve. +type TridiagonalSolveAttr func(optionalAttr) -// ResourceApplyAddSignUseLocking sets the optional use_locking attribute to value. +// TridiagonalSolvePartialPivoting sets the optional partial_pivoting attribute to value. // -// value: If `True`, updating of the var and m tensors is -// protected by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceApplyAddSignUseLocking(value bool) ResourceApplyAddSignAttr { +// value: Whether to apply partial pivoting. Partial pivoting makes the procedure more +// stable, but slower. +// If not specified, defaults to true +func TridiagonalSolvePartialPivoting(value bool) TridiagonalSolveAttr { return func(m optionalAttr) { - m["use_locking"] = value + m["partial_pivoting"] = value } } -// Update '*var' according to the AddSign update. +// Solves tridiagonal systems of equations. // -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g -// update <- (alpha + sign_decay * sign(g) *sign(m)) * g -// variable <- variable - lr_t * update +// Solves tridiagonal systems of equations. +// Supports batch dimensions and multiple right-hand sides per each left-hand +// side. +// On CPU, solution is computed via Gaussian elimination with or without partial +// pivoting, depending on `partial_pivoting` attribute. On GPU, Nvidia's cuSPARSE +// library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv // // Arguments: -// var_: Should be from a Variable(). -// m: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// alpha: Must be a scalar. -// sign_decay: Must be a scalar. -// beta: Must be a scalar. -// grad: The gradient. +// diagonals: Tensor of shape `[..., 3, M]` whose innermost 2 dimensions represent the +// tridiagonal matrices with three rows being the superdiagonal, diagonals, and +// subdiagonals, in order. The last element of the superdiagonal and the first +// element of the subdiagonal is ignored. +// rhs: Tensor of shape `[..., M, K]`, representing K right-hand sides per each +// left-hand side. // -// Returns the created operation. -func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, alpha tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyAddSignAttr) (o *tf.Operation) { +// Returns Tensor of shape `[..., M, K]` containing the solutions +func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional ...TridiagonalSolveAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -36780,45 +37885,155 @@ func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Outpu a(attrs) } opspec := tf.OpSpec{ - Type: "ResourceApplyAddSign", + Type: "TridiagonalSolve", Input: []tf.Input{ - var_, m, lr, alpha, sign_decay, beta, grad, + diagonals, rhs, }, Attrs: attrs, } - return scope.AddOperation(opspec) + op := scope.AddOperation(opspec) + return op.Output(0) } -// RetrieveTPUEmbeddingProximalAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParameters. -type RetrieveTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) +// MaxPool3DAttr is an optional argument to MaxPool3D. +type MaxPool3DAttr func(optionalAttr) -// RetrieveTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. +// MaxPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DDataFormat(value string) MaxPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D max pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseReduceMaxAttr is an optional argument to SparseReduceMax. +type SparseReduceMaxAttr func(optionalAttr) + +// SparseReduceMaxKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the max of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_max()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceMax(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceMax", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingProximalAdagradParametersAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParameters. +type LoadTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. // If not specified, defaults to -1 // // REQUIRES: value >= -1 -func RetrieveTPUEmbeddingProximalAdagradParametersTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersAttr { +func LoadTPUEmbeddingProximalAdagradParametersTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersAttr { return func(m optionalAttr) { m["table_id"] = value } } -// RetrieveTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. +// LoadTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. // If not specified, defaults to "" -func RetrieveTPUEmbeddingProximalAdagradParametersTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersAttr { +func LoadTPUEmbeddingProximalAdagradParametersTableName(value string) LoadTPUEmbeddingProximalAdagradParametersAttr { return func(m optionalAttr) { m["table_name"] = value } } -// Retrieve proximal Adagrad embedding parameters. +// Load proximal Adagrad embedding parameters. // -// An op that retrieves optimization parameters from embedding to host -// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up -// the correct embedding table configuration. For example, this op is -// used to retrieve updated parameters before saving a checkpoint. +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. // -// Returns Parameter parameters updated by the proximal Adagrad optimization algorithm.Parameter accumulators updated by the proximal Adagrad optimization algorithm. -func RetrieveTPUEmbeddingProximalAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { +// Arguments: +// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingProximalAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -36827,271 +38042,61 @@ func RetrieveTPUEmbeddingProximalAdagradParameters(scope *Scope, num_shards int6 a(attrs) } opspec := tf.OpSpec{ - Type: "RetrieveTPUEmbeddingProximalAdagradParameters", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// RestoreAttr is an optional argument to Restore. -type RestoreAttr func(optionalAttr) - -// RestorePreferredShard sets the optional preferred_shard attribute to value. -// -// value: Index of file to open first if multiple files match -// `file_pattern`. -// If not specified, defaults to -1 -func RestorePreferredShard(value int64) RestoreAttr { - return func(m optionalAttr) { - m["preferred_shard"] = value - } -} - -// Restores a tensor from checkpoint files. -// -// Reads a tensor stored in one or several files. If there are several files (for -// instance because a tensor was saved as slices), `file_pattern` may contain -// wildcard symbols (`*` and `?`) in the filename portion only, not in the -// directory portion. -// -// If a `file_pattern` matches several files, `preferred_shard` can be used to hint -// in which file the requested tensor is likely to be found. This op will first -// open the file at index `preferred_shard` in the list of matching files and try -// to restore tensors from that file. Only if some tensors or tensor slices are -// not found in that first file, then the Op opens all the files. Setting -// `preferred_shard` to match the value passed as the `shard` input -// of a matching `Save` Op may speed up Restore. This attribute only affects -// performance, not correctness. The default value -1 means files are processed in -// order. -// -// See also `RestoreSlice`. -// -// Arguments: -// file_pattern: Must have a single element. The pattern of the files from -// which we read the tensor. -// tensor_name: Must have a single element. The name of the tensor to be -// restored. -// dt: The type of the tensor to be restored. -// -// Returns The restored tensor. -func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dt": dt} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Restore", + Type: "LoadTPUEmbeddingProximalAdagradParameters", Input: []tf.Input{ - file_pattern, tensor_name, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Retrieves the tree ensemble resource stamp token, number of trees and growing statistics. -// -// Arguments: -// tree_ensemble_handle: Handle to the tree ensemble. -// -// Returns Stamp token of the tree ensemble resource.The number of trees in the tree ensemble resource.The number of trees that were finished successfully.The number of layers we attempted to build (but not necessarily succeeded).Rank size 2 tensor that contains start and end ids of the nodes in the latest -// layer. -func BoostedTreesGetEnsembleStates(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, num_trees tf.Output, num_finalized_trees tf.Output, num_attempted_layers tf.Output, last_layer_nodes_range tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BoostedTreesGetEnsembleStates", - Input: []tf.Input{ - tree_ensemble_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) -} - -// Returns the name of the device on which `resource` has been placed. -func ExperimentalIteratorGetDevice(scope *Scope, resource tf.Output) (device tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ExperimentalIteratorGetDevice", - Input: []tf.Input{ - resource, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Conv3DBackpropInputV2Attr is an optional argument to Conv3DBackpropInputV2. -type Conv3DBackpropInputV2Attr func(optionalAttr) - -// Conv3DBackpropInputV2DataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Conv3DBackpropInputV2Dilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 5. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each -// filter element on that dimension. The dimension order is determined by the -// value of `data_format`, see above for details. Dilations in the batch and -// depth dimensions must be 1. -// If not specified, defaults to -func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of 3-D convolution with respect to the input. -// -// Arguments: -// input_sizes: An integer vector representing the tensor shape of `input`, -// where `input` is a 5-D -// `[batch, depth, rows, cols, in_channels]` tensor. -// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. -// `in_channels` must match between `input` and `filter`. -// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, -// out_channels]`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputV2Attr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Conv3DBackpropInputV2", - Input: []tf.Input{ - input_sizes, filter, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. -type ResourceApplyProximalGradientDescentAttr func(optionalAttr) - -// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. -// -// value: If True, the subtraction will be protected by a lock; -// otherwise the behavior is undefined, but may exhibit less contention. -// If not specified, defaults to false -func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' as FOBOS algorithm with fixed learning rate. -// -// prox_v = var - alpha * delta -// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} -// -// Arguments: -// var_: Should be from a Variable(). -// alpha: Scaling factor. Must be a scalar. -// l1: L1 regularization. Must be a scalar. -// l2: L2 regularization. Must be a scalar. -// delta: The change. -// -// Returns the created operation. -func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceApplyProximalGradientDescent", - Input: []tf.Input{ - var_, alpha, l1, l2, delta, + parameters, accumulators, }, Attrs: attrs, } return scope.AddOperation(opspec) } -// Generate a sharded filename. The filename is printf formatted as -// -// %s-%05d-of-%05d, basename, shard, num_shards. -func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ShardedFilename", - Input: []tf.Input{ - basename, shard, num_shards, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. +type ResourceSparseApplyMomentumAttr func(optionalAttr) -// AudioSummaryV2Attr is an optional argument to AudioSummaryV2. -type AudioSummaryV2Attr func(optionalAttr) - -// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value. +// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. // -// value: Max number of batch elements to generate audio for. -// If not specified, defaults to 3 -// -// REQUIRES: value >= 1 -func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr { +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { return func(m optionalAttr) { - m["max_outputs"] = value + m["use_locking"] = value } } -// Outputs a `Summary` protocol buffer with audio. +// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. // -// The summary has up to `max_outputs` summary values containing audio. The -// audio is built from `tensor` which must be 3-D with shape `[batch_size, -// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are -// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. // -// The `tag` argument is a scalar `Tensor` of type `string`. It is used to -// build the `tag` of the summary values: +// Set use_nesterov = True if you want to use Nesterov momentum. // -// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. -// * If `max_outputs` is greater than 1, the summary value tags are -// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. +// That is for rows we have grad for, we update var and accum as follows: +// +// accum = accum * momentum + grad +// var -= lr * accum // // Arguments: -// tag: Scalar. Used to build the `tag` attribute of the summary values. -// tensor: 2-D of shape `[batch_size, frames]`. -// sample_rate: The sample rate of the signal in hertz. +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. // -// Returns Scalar. Serialized `Summary` protocol buffer. -func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) { +// Returns the created operation. +func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { if scope.Err() != nil { return } @@ -37100,9 +38105,81 @@ func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate t a(attrs) } opspec := tf.OpSpec{ - Type: "AudioSummaryV2", + Type: "ResourceSparseApplyMomentum", Input: []tf.Input{ - tag, tensor, sample_rate, + var_, accum, lr, grad, indices, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// BatchMatMulV2Attr is an optional argument to BatchMatMulV2. +type BatchMatMulV2Attr func(optionalAttr) + +// BatchMatMulV2AdjX sets the optional adj_x attribute to value. +// +// value: If `True`, adjoint the slices of `x`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulV2AdjX(value bool) BatchMatMulV2Attr { + return func(m optionalAttr) { + m["adj_x"] = value + } +} + +// BatchMatMulV2AdjY sets the optional adj_y attribute to value. +// +// value: If `True`, adjoint the slices of `y`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulV2AdjY(value bool) BatchMatMulV2Attr { + return func(m optionalAttr) { + m["adj_y"] = value + } +} + +// Multiplies slices of two tensors in batches. +// +// Multiplies all slices of `Tensor` `x` and `y` (each slice can be +// viewed as an element of a batch), and arranges the individual results +// in a single output tensor of the same batch size. Each of the +// individual slices can optionally be adjointed (to adjoint a matrix +// means to transpose and conjugate it) before multiplication by setting +// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. +// +// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` +// and `[..., r_y, c_y]`. +// +// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: +// +// r_o = c_x if adj_x else r_x +// c_o = r_y if adj_y else c_y +// +// It is computed as: +// +// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) +// +// *NOTE*: `BatchMatMulV2` supports broadcasting in the batch dimensions. More +// about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). +// +// +// Arguments: +// x: 2-D or higher with shape `[..., r_x, c_x]`. +// y: 2-D or higher with shape `[..., r_y, c_y]`. +// +// Returns 3-D or higher with shape `[..., r_o, c_o]` +func BatchMatMulV2(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchMatMulV2", + Input: []tf.Input{ + x, y, }, Attrs: attrs, } @@ -37110,6 +38187,57 @@ func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate t return op.Output(0) } +// Restores tensors from a V2 checkpoint. +// +// For backward compatibility with the V1 format, this Op currently allows +// restoring from a V1 checkpoint as well: +// - This Op first attempts to find the V2 index file pointed to by "prefix", and +// if found proceed to read it as a V2 checkpoint; +// - Otherwise the V1 read path is invoked. +// Relying on this behavior is not recommended, as the ability to fall back to read +// V1 might be deprecated and eventually removed. +// +// By default, restores the named tensors in full. If the caller wishes to restore +// specific slices of stored tensors, "shape_and_slices" should be non-empty +// strings and correspondingly well-formed. +// +// Callers must ensure all the named tensors are indeed stored in the checkpoint. +// +// Arguments: +// prefix: Must have a single element. The prefix of a V2 checkpoint. +// tensor_names: shape {N}. The names of the tensors to be restored. +// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. +// Empty strings indicate that they are non-partitioned tensors. +// dtypes: shape {N}. The list of expected dtype for the tensors. Must match +// those stored in the checkpoint. +// +// Returns shape {N}. The restored tensors, whose shapes are read from the +// checkpoint directly. +func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + opspec := tf.OpSpec{ + Type: "RestoreV2", + Input: []tf.Input{ + prefix, tensor_names, shape_and_slices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { + scope.UpdateErr("RestoreV2", err) + return + } + return tensors +} + // Generate a glob pattern matching all sharded file names. func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) { if scope.Err() != nil { @@ -37125,55 +38253,111 @@ func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (fi return op.Output(0) } -// NonMaxSuppressionV4Attr is an optional argument to NonMaxSuppressionV4. -type NonMaxSuppressionV4Attr func(optionalAttr) - -// NonMaxSuppressionV4PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. +// Deserialize and concatenate `SparseTensors` from a serialized minibatch. // -// value: If true, the output `selected_indices` is padded to be of length -// `max_output_size`. Defaults to false. -// If not specified, defaults to false -func NonMaxSuppressionV4PadToMaxOutputSize(value bool) NonMaxSuppressionV4Attr { +// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where +// `N` is the minibatch size and the rows correspond to packed outputs of +// `SerializeSparse`. The ranks of the original `SparseTensor` objects +// must all match. When the final `SparseTensor` is created, it has rank one +// higher than the ranks of the incoming `SparseTensor` objects +// (they have been concatenated along a new row dimension). +// +// The output `SparseTensor` object's shape values for all dimensions but the +// first are the max across the input `SparseTensor` objects' shape values +// for the corresponding dimensions. Its first shape value is `N`, the minibatch +// size. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. +// Must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "DeserializeManySparse", + Input: []tf.Input{ + serialized_sparse, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// PackAttr is an optional argument to Pack. +type PackAttr func(optionalAttr) + +// PackAxis sets the optional axis attribute to value. +// +// value: Dimension along which to pack. Negative values wrap around, so the +// valid range is `[-(R+1), R+1)`. +// If not specified, defaults to 0 +func PackAxis(value int64) PackAttr { return func(m optionalAttr) { - m["pad_to_max_output_size"] = value + m["axis"] = value } } -// Greedily selects a subset of bounding boxes in descending order of score, +// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. // -// pruning away boxes that have high intersection-over-union (IOU) overlap -// with previously selected boxes. Bounding boxes with score less than -// `score_threshold` are removed. Bounding boxes are supplied as -// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any -// diagonal pair of box corners and the coordinates can be provided as normalized -// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm -// is agnostic to where the origin is in the coordinate system and more -// generally is invariant to orthogonal transformations and translations -// of the coordinate system; thus translating or reflections of the coordinate -// system result in the same boxes being selected by the algorithm. -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// selected_indices = tf.image.non_max_suppression_v2( -// boxes, scores, max_output_size, iou_threshold, score_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) +// Packs the `N` tensors in `values` into a tensor with rank one higher than each +// tensor in `values`, by packing them along the `axis` dimension. +// Given a list of tensors of shape `(A, B, C)`; +// +// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. +// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. +// Etc. +// +// For example: +// +// ``` +// # 'x' is [1, 4] +// # 'y' is [2, 5] +// # 'z' is [3, 6] +// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. +// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] +// ``` +// +// This is the opposite of `unpack`. // // Arguments: -// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// iou_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too much with respect to IOU. -// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove -// boxes based on score. +// values: Must be of same shape and type. // -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`.A 0-D integer tensor representing the number of valid elements in -// `selected_indices`, with the valid elements appearing first. -func NonMaxSuppressionV4(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...NonMaxSuppressionV4Attr) (selected_indices tf.Output, valid_outputs tf.Output) { +// Returns The packed tensor. +func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -37182,84 +38366,14 @@ func NonMaxSuppressionV4(scope *Scope, boxes tf.Output, scores tf.Output, max_ou a(attrs) } opspec := tf.OpSpec{ - Type: "NonMaxSuppressionV4", + Type: "Pack", Input: []tf.Input{ - boxes, scores, max_output_size, iou_threshold, score_threshold, + tf.OutputList(values), }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1) -} - -// Transforms a tf.Example proto (as a string) into typed tensors. -// -// Arguments: -// serialized: A vector containing a batch of binary serialized Example protos. -// dense_defaults: A list of Tensors (some may be empty), whose length matches -// the length of `dense_keys`. dense_defaults[j] provides default values -// when the example's feature_map lacks dense_key[j]. If an empty Tensor is -// provided for dense_defaults[j], then the Feature dense_keys[j] is required. -// The input type is inferred from dense_defaults[j], even when it's empty. -// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, -// then the shape of dense_defaults[j] must match that of dense_shapes[j]. -// If dense_shapes[j] has an undefined major dimension (variable strides dense -// feature), dense_defaults[j] must contain a single element: -// the padding element. -// num_sparse: The number of sparse features to be parsed from the example. This -// must match the lengths of `sparse_keys` and `sparse_types`. -// sparse_keys: A list of `num_sparse` strings. -// The keys expected in the Examples' features associated with sparse values. -// dense_keys: The keys expected in the Examples' features associated with dense -// values. -// sparse_types: A list of `num_sparse` types; the data types of data in each -// Feature given in sparse_keys. -// Currently the ParseSingleExample op supports DT_FLOAT (FloatList), -// DT_INT64 (Int64List), and DT_STRING (BytesList). -// dense_shapes: The shapes of data in each Feature given in dense_keys. -// The length of this list must match the length of `dense_keys`. The -// number of elements in the Feature corresponding to dense_key[j] must -// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == -// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] -// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1, -// ..., DN), the shape of the output Tensor dense_values[j] will be (M, -// D1, .., DN), where M is the number of blocks of elements of length -// D1 * .... * DN, in the input. -func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes} - opspec := tf.OpSpec{ - Type: "ParseSingleExample", - Input: []tf.Input{ - serialized, tf.OutputList(dense_defaults), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { - scope.UpdateErr("ParseSingleExample", err) - return - } - if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { - scope.UpdateErr("ParseSingleExample", err) - return - } - if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { - scope.UpdateErr("ParseSingleExample", err) - return - } - if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { - scope.UpdateErr("ParseSingleExample", err) - return - } - return sparse_indices, sparse_values, sparse_shapes, dense_values + return op.Output(0) } // WholeFileReaderV2Attr is an optional argument to WholeFileReaderV2. @@ -37310,6 +38424,58 @@ func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_ return op.Output(0) } +// ResizeBilinearAttr is an optional argument to ResizeBilinear. +type ResizeBilinearAttr func(optionalAttr) + +// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBilinearHalfPixelCenters(value bool) ResizeBilinearAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using bilinear interpolation. +// +// Input images can be of different types but output images are always float. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBilinear", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. type FixedLengthRecordReaderV2Attr func(optionalAttr) @@ -37400,69 +38566,99 @@ func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...Fix return op.Output(0) } -// Draw bounding boxes on a batch of images. +// Creates a dataset that shuffles and repeats elements from `input_dataset` // -// Outputs a copy of `images` but draws on top of the pixels zero or more bounding -// boxes specified by the locations in `boxes`. The coordinates of the each -// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The -// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and -// height of the underlying image. -// -// For example, if an image is 100 x 200 pixels (height x width) and the bounding -// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of -// the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). -// -// Parts of the bounding box may fall outside the image. +// pseudorandomly. // // Arguments: -// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. -// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding -// boxes. -// colors: 2-D. A list of RGBA colors to cycle through for the boxes. // -// Returns 4-D with the same shape as `images`. The batch of input images with -// bounding boxes drawn on the images. -func DrawBoundingBoxesV2(scope *Scope, images tf.Output, boxes tf.Output, colors tf.Output) (output tf.Output) { +// buffer_size: The number of output elements to buffer in an iterator over +// this dataset. Compare with the `min_after_dequeue` attr when creating a +// `RandomShuffleQueue`. +// seed: A scalar seed for the random number generator. If either `seed` or +// `seed2` is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// count: A scalar representing the number of times the underlying dataset +// should be repeated. The default is `-1`, which results in infinite repetition. +// +// +func ShuffleAndRepeatDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "DrawBoundingBoxesV2", + Type: "ShuffleAndRepeatDataset", Input: []tf.Input{ - images, boxes, colors, + input_dataset, buffer_size, seed, seed2, count, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// StringLengthAttr is an optional argument to StringLength. -type StringLengthAttr func(optionalAttr) +// LRNAttr is an optional argument to LRN. +type LRNAttr func(optionalAttr) -// StringLengthUnit sets the optional unit attribute to value. +// LRNDepthRadius sets the optional depth_radius attribute to value. // -// value: The unit that is counted to compute string length. One of: `"BYTE"` (for -// the number of bytes in each string) or `"UTF8_CHAR"` (for the number of UTF-8 -// encoded Unicode code points in each string). Results are undefined -// if `unit=UTF8_CHAR` and the `input` strings do not contain structurally -// valid UTF-8. -// If not specified, defaults to "BYTE" -func StringLengthUnit(value string) StringLengthAttr { +// value: 0-D. Half-width of the 1-D normalization window. +// If not specified, defaults to 5 +func LRNDepthRadius(value int64) LRNAttr { return func(m optionalAttr) { - m["unit"] = value + m["depth_radius"] = value } } -// String lengths of `input`. +// LRNBias sets the optional bias attribute to value. // -// Computes the length of each string given in the input tensor. +// value: An offset (usually positive to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNBias(value float32) LRNAttr { + return func(m optionalAttr) { + m["bias"] = value + } +} + +// LRNAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNAlpha(value float32) LRNAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNBeta sets the optional beta attribute to value. +// +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNBeta(value float32) LRNAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Local Response Normalization. +// +// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last +// dimension), and each vector is normalized independently. Within a given vector, +// each component is divided by the weighted, squared sum of inputs within +// `depth_radius`. In detail, +// +// sqr_sum[a, b, c, d] = +// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) +// output = input / (bias + alpha * sqr_sum) ** beta +// +// For details, see [Krizhevsky et al., ImageNet classification with deep +// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). // // Arguments: -// input: The string for which to compute the length. -// -// Returns Integer tensor that has the same shape as `input`. The output contains the -// element-wise string lengths of `input`. -func StringLength(scope *Scope, input tf.Output, optional ...StringLengthAttr) (output tf.Output) { +// input: 4-D. +func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { if scope.Err() != nil { return } @@ -37471,7 +38667,7 @@ func StringLength(scope *Scope, input tf.Output, optional ...StringLengthAttr) ( a(attrs) } opspec := tf.OpSpec{ - Type: "StringLength", + Type: "LRN", Input: []tf.Input{ input, }, @@ -37481,1434 +38677,68 @@ func StringLength(scope *Scope, input tf.Output, optional ...StringLengthAttr) ( return op.Output(0) } -// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2. -type TFRecordReaderV2Attr func(optionalAttr) - -// TFRecordReaderV2Container sets the optional container attribute to value. -// -// value: If non-empty, this reader is placed in the given container. -// Otherwise, a default container is used. -// If not specified, defaults to "" -func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// TFRecordReaderV2SharedName sets the optional shared_name attribute to value. -// -// value: If non-empty, this reader is named in the given bucket -// with this shared_name. Otherwise, the node name is used instead. -// If not specified, defaults to "" -func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value. -// If not specified, defaults to "" -func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr { - return func(m optionalAttr) { - m["compression_type"] = value - } -} - -// A Reader that outputs the records from a TensorFlow Records file. -// -// Returns The handle to reference the Reader. -func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TFRecordReaderV2", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Exits the current frame to its parent frame. -// -// Exit makes its input `data` available to the parent frame. -// -// Arguments: -// data: The tensor to be made available to the parent frame. -// -// Returns The same tensor as `data`. -func Exit(scope *Scope, data tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Exit", - Input: []tf.Input{ - data, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ComplexAttr is an optional argument to Complex. -type ComplexAttr func(optionalAttr) - -// ComplexTout sets the optional Tout attribute to value. -// If not specified, defaults to DT_COMPLEX64 -func ComplexTout(value tf.DataType) ComplexAttr { - return func(m optionalAttr) { - m["Tout"] = value - } -} - -// Converts two real numbers to a complex number. -// -// Given a tensor `real` representing the real part of a complex number, and a -// tensor `imag` representing the imaginary part of a complex number, this -// operation returns complex numbers elementwise of the form \\(a + bj\\), where -// *a* represents the `real` part and *b* represents the `imag` part. -// -// The input tensors `real` and `imag` must have the same shape. -// -// For example: -// -// ``` -// # tensor 'real' is [2.25, 3.25] -// # tensor `imag` is [4.75, 5.75] -// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] -// ``` -func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Complex", - Input: []tf.Input{ - real, imag, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// StringSplitAttr is an optional argument to StringSplit. -type StringSplitAttr func(optionalAttr) - -// StringSplitSkipEmpty sets the optional skip_empty attribute to value. -// -// value: A `bool`. If `True`, skip the empty strings from the result. -// If not specified, defaults to true -func StringSplitSkipEmpty(value bool) StringSplitAttr { - return func(m optionalAttr) { - m["skip_empty"] = value - } -} - -// Split elements of `input` based on `delimiter` into a `SparseTensor`. -// -// Let N be the size of source (typically N will be the batch size). Split each -// element of `input` based on `delimiter` and return a `SparseTensor` -// containing the splitted tokens. Empty tokens are ignored. -// -// `delimiter` can be empty, or a string of split characters. If `delimiter` is an -// empty string, each element of `input` is split into individual single-byte -// character strings, including splitting of UTF-8 multibyte sequences. Otherwise -// every character of `delimiter` is a potential split point. -// -// For example: -// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output -// will be -// -// indices = [0, 0; -// 0, 1; -// 1, 0; -// 1, 1; -// 1, 2] -// shape = [2, 3] -// values = ['hello', 'world', 'a', 'b', 'c'] -// -// Arguments: -// input: 1-D. Strings to split. -// delimiter: 0-D. Delimiter characters (bytes), or empty string. -// -// Returns A dense matrix of int64 representing the indices of the sparse tensor.A vector of strings corresponding to the splited values.a length-2 vector of int64 representing the shape of the sparse -// tensor, where the first value is N and the second value is the maximum number -// of tokens in a single input entry. -func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "StringSplit", - Input: []tf.Input{ - input, delimiter, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// QueueEnqueueV2Attr is an optional argument to QueueEnqueueV2. -type QueueEnqueueV2Attr func(optionalAttr) - -// QueueEnqueueV2TimeoutMs sets the optional timeout_ms attribute to value. -// -// value: If the queue is full, this operation will block for up to -// timeout_ms milliseconds. -// Note: This option is not supported yet. -// If not specified, defaults to -1 -func QueueEnqueueV2TimeoutMs(value int64) QueueEnqueueV2Attr { - return func(m optionalAttr) { - m["timeout_ms"] = value - } -} - -// Enqueues a tuple of one or more tensors in the given queue. -// -// The components input has k elements, which correspond to the components of -// tuples stored in the given queue. -// -// N.B. If the queue is full, this operation will block until the given -// element has been enqueued (or 'timeout_ms' elapses, if specified). -// -// Arguments: -// handle: The handle to a queue. -// components: One or more tensors from which the enqueued tensors should be taken. -// -// Returns the created operation. -func QueueEnqueueV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueV2Attr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QueueEnqueueV2", - Input: []tf.Input{ - handle, tf.OutputList(components), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// MaxPoolAttr is an optional argument to MaxPool. -type MaxPoolAttr func(optionalAttr) - -// MaxPoolDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. -// If not specified, defaults to "NHWC" -func MaxPoolDataFormat(value string) MaxPoolAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs max pooling on the input. -// -// Arguments: -// input: 4-D input to pool over. -// ksize: The size of the window for each dimension of the input tensor. -// strides: The stride of the sliding window for each dimension of the -// input tensor. -// padding: The type of padding algorithm to use. -// -// Returns The max pooled output tensor. -func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MaxPool", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Returns up to `num_records` (key, value) pairs produced by a Reader. +// Returns the next record (key, value pair) produced by a Reader. // // Will dequeue from the input queue if necessary (e.g. when the // Reader needs to start reading from a new file since it has finished // with the previous file). -// It may return less than `num_records` even before the last batch. // // Arguments: -// reader_handle: Handle to a `Reader`. -// queue_handle: Handle to a `Queue`, with string work items. -// num_records: number of records to read from `Reader`. +// reader_handle: Handle to a Reader. +// queue_handle: Handle to a Queue, with string work items. // -// Returns A 1-D tensor.A 1-D tensor. -func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) { +// Returns A scalar.A scalar. +func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ReaderReadUpToV2", + Type: "ReaderReadV2", Input: []tf.Input{ - reader_handle, queue_handle, num_records, + reader_handle, queue_handle, }, } op := scope.AddOperation(opspec) return op.Output(0), op.Output(1) } -// ResizeBicubicAttr is an optional argument to ResizeBicubic. -type ResizeBicubicAttr func(optionalAttr) - -// ResizeBicubicAlignCorners sets the optional align_corners attribute to value. +// Adds two `SparseTensor` objects to produce another `SparseTensor`. // -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// ResizeBicubicHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func ResizeBicubicHalfPixelCenters(value bool) ResizeBicubicAttr { - return func(m optionalAttr) { - m["half_pixel_centers"] = value - } -} - -// Resize `images` to `size` using bicubic interpolation. +// The input `SparseTensor` objects' indices are assumed ordered in standard +// lexicographic order. If this is not the case, before this step run +// `SparseReorder` to restore index ordering. // -// Input images can be of different types but output images are always float. +// By default, if two values sum to zero at some index, the output `SparseTensor` +// would still include that particular location in its index, storing a zero in the +// corresponding value slot. To override this, callers can specify `thresh`, +// indicating that if the sum has a magnitude strictly smaller than `thresh`, its +// corresponding value and index would then not be included. In particular, +// `thresh == 0` (default) means everything is kept and actual thresholding happens +// only for a positive value. +// +// In the following shapes, `nnz` is the count after taking `thresh` into account. // // Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeBicubic", - Input: []tf.Input{ - images, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DecodeProtoV2Attr is an optional argument to DecodeProtoV2. -type DecodeProtoV2Attr func(optionalAttr) - -// DecodeProtoV2DescriptorSource sets the optional descriptor_source attribute to value. -// -// value: Either the special value `local://` or a path to a file containing -// a serialized `FileDescriptorSet`. -// If not specified, defaults to "local://" -func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr { - return func(m optionalAttr) { - m["descriptor_source"] = value - } -} - -// DecodeProtoV2MessageFormat sets the optional message_format attribute to value. -// -// value: Either `binary` or `text`. -// If not specified, defaults to "binary" -func DecodeProtoV2MessageFormat(value string) DecodeProtoV2Attr { - return func(m optionalAttr) { - m["message_format"] = value - } -} - -// DecodeProtoV2Sanitize sets the optional sanitize attribute to value. -// -// value: Whether to sanitize the result or not. -// If not specified, defaults to false -func DecodeProtoV2Sanitize(value bool) DecodeProtoV2Attr { - return func(m optionalAttr) { - m["sanitize"] = value - } -} - -// The op extracts fields from a serialized protocol buffers message into tensors. -// -// The `decode_proto` op extracts fields from a serialized protocol buffers -// message into tensors. The fields in `field_names` are decoded and converted -// to the corresponding `output_types` if possible. -// -// A `message_type` name must be provided to give context for the field -// names. The actual message descriptor can be looked up either in the -// linked-in descriptor pool or a filename provided by the caller using -// the `descriptor_source` attribute. -// -// Each output tensor is a dense tensor. This means that it is padded to -// hold the largest number of repeated elements seen in the input -// minibatch. (The shape is also padded by one to prevent zero-sized -// dimensions). The actual repeat counts for each example in the -// minibatch can be found in the `sizes` output. In many cases the output -// of `decode_proto` is fed immediately into tf.squeeze if missing values -// are not a concern. When using tf.squeeze, always pass the squeeze -// dimension explicitly to avoid surprises. -// -// For the most part, the mapping between Proto field types and -// TensorFlow dtypes is straightforward. However, there are a few -// special cases: -// -// - A proto field that contains a submessage or group can only be converted -// to `DT_STRING` (the serialized submessage). This is to reduce the -// complexity of the API. The resulting string can be used as input -// to another instance of the decode_proto op. -// -// - TensorFlow lacks support for unsigned integers. The ops represent uint64 -// types as a `DT_INT64` with the same twos-complement bit pattern -// (the obvious way). Unsigned int32 values can be represented exactly by -// specifying type `DT_INT64`, or using twos-complement if the caller -// specifies `DT_INT32` in the `output_types` attribute. -// -// The `descriptor_source` attribute selects a source of protocol -// descriptors to consult when looking up `message_type`. This may be a -// filename containing a serialized `FileDescriptorSet` message, -// or the special value `local://`, in which case only descriptors linked -// into the code will be searched; the filename can be on any filesystem -// accessible to TensorFlow. -// -// You can build a `descriptor_source` file using the `--descriptor_set_out` -// and `--include_imports` options to the protocol compiler `protoc`. -// -// The `local://` database only covers descriptors linked into the -// code via C++ libraries, not Python imports. You can link in a proto descriptor -// by creating a cc_library target with alwayslink=1. -// -// Both binary and text proto serializations are supported, and can be -// chosen using the `format` attribute. -// -// Arguments: -// bytes: Tensor of serialized protos with shape `batch_shape`. -// message_type: Name of the proto message type to decode. -// field_names: List of strings containing proto field names. An extension field can be decoded -// by using its full name, e.g. EXT_PACKAGE.EXT_FIELD_NAME. -// output_types: List of TF types to use for the respective field in field_names. -// -// Returns Tensor of int32 with shape `[batch_shape, len(field_names)]`. -// Each entry is the number of values found for the corresponding field. -// Optional fields may have 0 or 1 values.List of tensors containing values for the corresponding field. -// `values[i]` has datatype `output_types[i]` -// and shape `[batch_shape, max(sizes[...,i])]`. -func DecodeProtoV2(scope *Scope, bytes tf.Output, message_type string, field_names []string, output_types []tf.DataType, optional ...DecodeProtoV2Attr) (sizes tf.Output, values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"message_type": message_type, "field_names": field_names, "output_types": output_types} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DecodeProtoV2", - Input: []tf.Input{ - bytes, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - sizes = op.Output(idx) - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("DecodeProtoV2", err) - return - } - return sizes, values -} - -// Produce a string tensor that encodes the state of a Reader. -// -// Not all Readers support being serialized, so this can produce an -// Unimplemented error. -// -// Arguments: -// reader_handle: Handle to a Reader. -func ReaderSerializeStateV2(scope *Scope, reader_handle tf.Output) (state tf.Output) { +// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. +// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. +// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. +// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. +// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. +// thresh: 0-D. The magnitude threshold that determines if an output value/index +// pair takes space. +func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "ReaderSerializeStateV2", + Type: "SparseAdd", Input: []tf.Input{ - reader_handle, + a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, }, } op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Restore a reader to a previously saved state. -// -// Not all Readers support being restored, so this can produce an -// Unimplemented error. -// -// Arguments: -// reader_handle: Handle to a Reader. -// state: Result of a ReaderSerializeState of a Reader with type -// matching reader_handle. -// -// Returns the created operation. -func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderRestoreStateV2", - Input: []tf.Input{ - reader_handle, state, - }, - } - return scope.AddOperation(opspec) -} - -// Bitcasts a tensor from one type to another without copying data. -// -// Given a tensor `input`, this operation returns a tensor that has the same buffer -// data as `input` with datatype `type`. -// -// If the input datatype `T` is larger than the output datatype `type` then the -// shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)]. -// -// If `T` is smaller than `type`, the operator requires that the rightmost -// dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from -// [..., sizeof(`type`)/sizeof(`T`)] to [...]. -// -// tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype -// (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() -// gives module error. -// For example, -// -// Example 1: -// ```python -// >>> a = [1., 2., 3.] -// >>> equality_bitcast = tf.bitcast(a,tf.complex128) -// tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot bitcast from float to complex128: shape [3] [Op:Bitcast] -// >>> equality_cast = tf.cast(a,tf.complex128) -// >>> print(equality_cast) -// tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128) -// ``` -// Example 2: -// ```python -// >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8) -// -// ``` -// Example 3: -// ```python -// >>> x = [1., 2., 3.] -// >>> y = [0., 2., 3.] -// >>> equality= tf.equal(x,y) -// >>> equality_cast = tf.cast(equality,tf.float32) -// >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8) -// >>> print(equality) -// tf.Tensor([False True True], shape=(3,), dtype=bool) -// >>> print(equality_cast) -// tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32) -// >>> print(equality_bitcast) -// tf.Tensor( -// [[ 0 0 0 0] -// [ 0 0 128 63] -// [ 0 0 128 63]], shape=(3, 4), dtype=uint8) -// ``` -// -// *NOTE*: Bitcast is implemented as a low-level cast, so machines with different -// endian orderings will give different results. -func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"type": type_} - opspec := tf.OpSpec{ - Type: "Bitcast", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// MatMulAttr is an optional argument to MatMul. -type MatMulAttr func(optionalAttr) - -// MatMulTransposeA sets the optional transpose_a attribute to value. -// -// value: If true, "a" is transposed before multiplication. -// If not specified, defaults to false -func MatMulTransposeA(value bool) MatMulAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// MatMulTransposeB sets the optional transpose_b attribute to value. -// -// value: If true, "b" is transposed before multiplication. -// If not specified, defaults to false -func MatMulTransposeB(value bool) MatMulAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// Multiply the matrix "a" by the matrix "b". -// -// The inputs must be two-dimensional matrices and the inner dimension of -// "a" (after being transposed if transpose_a is true) must match the -// outer dimension of "b" (after being transposed if transposed_b is -// true). -// -// *Note*: The default kernel implementation for MatMul on GPUs uses -// cublas. -func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MatMul", - Input: []tf.Input{ - a, b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. -type ResourceSparseApplyRMSPropAttr func(optionalAttr) - -// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. -// -// value: If `True`, updating of the var, ms, and mom tensors is protected -// by a lock; otherwise the behavior is undefined, but may exhibit less -// contention. -// If not specified, defaults to false -func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { - return func(m optionalAttr) { - m["use_locking"] = value - } -} - -// Update '*var' according to the RMSProp algorithm. -// -// Note that in dense implementation of this algorithm, ms and mom will -// update even if the grad is zero, but in this sparse implementation, ms -// and mom will not update in iterations during which the grad is zero. -// -// mean_square = decay * mean_square + (1-decay) * gradient ** 2 -// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) -// -// ms <- rho * ms_{t-1} + (1-rho) * grad * grad -// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) -// var <- var - mom -// -// Arguments: -// var_: Should be from a Variable(). -// ms: Should be from a Variable(). -// mom: Should be from a Variable(). -// lr: Scaling factor. Must be a scalar. -// rho: Decay rate. Must be a scalar. -// -// epsilon: Ridge term. Must be a scalar. -// grad: The gradient. -// indices: A vector of indices into the first dimension of var, ms and mom. -// -// Returns the created operation. -func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResourceSparseApplyRMSProp", - Input: []tf.Input{ - var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Restore a Reader to its initial clean state. -// -// Arguments: -// reader_handle: Handle to a Reader. -// -// Returns the created operation. -func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "ReaderResetV2", - Input: []tf.Input{ - reader_handle, - }, - } - return scope.AddOperation(opspec) -} - -// Writes contents to the file at input filename. Creates file and recursively -// -// creates directory if not existing. -// -// Arguments: -// filename: scalar. The name of the file to which we write the contents. -// contents: scalar. The content to be written to the output file. -// -// Returns the created operation. -func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "WriteFile", - Input: []tf.Input{ - filename, contents, - }, - } - return scope.AddOperation(opspec) -} - -// Returns the set of files matching one or more glob patterns. -// -// Note that this routine only supports wildcard characters in the -// basename portion of the pattern, not in the directory portion. -// Note also that the order of filenames returned can be non-deterministic. -// -// Arguments: -// pattern: Shell wildcard pattern(s). Scalar or vector of type string. -// -// Returns A vector of matching filenames. -func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatchingFiles", - Input: []tf.Input{ - pattern, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts one or more images from RGB to HSV. -// -// Outputs a tensor of the same shape as the `images` tensor, containing the HSV -// value of the pixels. The output is only well defined if the value in `images` -// are in `[0,1]`. -// -// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and -// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0 -// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. -// -// Arguments: -// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3. -// -// Returns `images` converted to HSV. -func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RGBToHSV", - Input: []tf.Input{ - images, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeAreaAttr is an optional argument to ResizeArea. -type ResizeAreaAttr func(optionalAttr) - -// ResizeAreaAlignCorners sets the optional align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// Resize `images` to `size` using area interpolation. -// -// Input images can be of different types but output images are always float. -// -// The range of pixel values for the output image might be slightly different -// from the range for the input image because of limited numerical precision. -// To guarantee an output range, for example `[0.0, 1.0]`, apply -// `tf.clip_by_value` to the output. -// -// Each output pixel is computed by first transforming the pixel's footprint into -// the input tensor and then averaging the pixels that intersect the footprint. An -// input pixel's contribution to the average is weighted by the fraction of its -// area that intersects the footprint. This is the same as OpenCV's INTER_AREA. -// -// Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeArea", - Input: []tf.Input{ - images, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering. -// -// Arguments: -// tree_ensemble_handle: Handle to the tree ensemble. -// mean_gradients: A tensor with shape=[logits_dimension] with mean of gradients for a first node. -// mean_hessians: A tensor with shape=[logits_dimension] mean of hessians for a first node. -// l1: l1 regularization factor on leaf weights, per instance based. -// l2: l2 regularization factor on leaf weights, per instance based. -// -// Returns Bool, whether to continue bias centering. -func BoostedTreesCenterBias(scope *Scope, tree_ensemble_handle tf.Output, mean_gradients tf.Output, mean_hessians tf.Output, l1 tf.Output, l2 tf.Output) (continue_centering tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BoostedTreesCenterBias", - Input: []tf.Input{ - tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeBicubicGradAttr is an optional argument to ResizeBicubicGrad. -type ResizeBicubicGradAttr func(optionalAttr) - -// ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and grad tensors are -// aligned. Defaults to false. -// If not specified, defaults to false -func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// ResizeBicubicGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func ResizeBicubicGradHalfPixelCenters(value bool) ResizeBicubicGradAttr { - return func(m optionalAttr) { - m["half_pixel_centers"] = value - } -} - -// Computes the gradient of bicubic interpolation. -// -// Arguments: -// grads: 4-D with shape `[batch, height, width, channels]`. -// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`, -// The image tensor that was resized. -// -// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. -// Gradients with respect to the input image. Input image must have been -// float or double. -func ResizeBicubicGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBicubicGradAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeBicubicGrad", - Input: []tf.Input{ - grads, original_image, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeBilinearAttr is an optional argument to ResizeBilinear. -type ResizeBilinearAttr func(optionalAttr) - -// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// ResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func ResizeBilinearHalfPixelCenters(value bool) ResizeBilinearAttr { - return func(m optionalAttr) { - m["half_pixel_centers"] = value - } -} - -// Resize `images` to `size` using bilinear interpolation. -// -// Input images can be of different types but output images are always float. -// -// Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeBilinear", - Input: []tf.Input{ - images, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor. -type ResizeNearestNeighborAttr func(optionalAttr) - -// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value. -// -// value: If true, the centers of the 4 corner pixels of the input and output tensors are -// aligned, preserving the values at the corner pixels. Defaults to false. -// If not specified, defaults to false -func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr { - return func(m optionalAttr) { - m["align_corners"] = value - } -} - -// ResizeNearestNeighborHalfPixelCenters sets the optional half_pixel_centers attribute to value. -// If not specified, defaults to false -func ResizeNearestNeighborHalfPixelCenters(value bool) ResizeNearestNeighborAttr { - return func(m optionalAttr) { - m["half_pixel_centers"] = value - } -} - -// Resize `images` to `size` using nearest neighbor interpolation. -// -// Arguments: -// images: 4-D with shape `[batch, height, width, channels]`. -// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The -// new size for the images. -// -// Returns 4-D with shape -// `[batch, new_height, new_width, channels]`. -func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ResizeNearestNeighbor", - Input: []tf.Input{ - images, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the sum along segments of a tensor. -// -// Read -// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) -// for an explanation of segments. -// -// Computes a tensor such that -// \\(output[i] = \sum_{j...} data[j...]\\) where the sum is over tuples `j...` such -// that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids` -// need not be sorted and need not cover all values in the full -// range of valid values. -// -// If the sum is empty for a given segment ID `i`, `output[i] = 0`. -// If the given segment ID `i` is negative, the value is dropped and will not be -// added to the sum of the segment. -// -// `num_segments` should equal the number of distinct segment IDs. -// -//
-// -//
-// -// ``` python -// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) -// tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2) -// # ==> [[ 5, 5, 5, 5], -// # [5, 6, 7, 8]] -// ``` -// -// -// Arguments: -// -// segment_ids: A tensor whose shape is a prefix of `data.shape`. -// -// -// Returns Has same shape as data, except for the first `segment_ids.rank` -// dimensions, which are replaced with a single dimension which has size -// `num_segments`. -func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "UnsortedSegmentSum", - Input: []tf.Input{ - data, segment_ids, num_segments, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RandomCropAttr is an optional argument to RandomCrop. -type RandomCropAttr func(optionalAttr) - -// RandomCropSeed sets the optional seed attribute to value. -// -// value: If either seed or seed2 are set to be non-zero, the random number -// generator is seeded by the given seed. Otherwise, it is seeded by a -// random seed. -// If not specified, defaults to 0 -func RandomCropSeed(value int64) RandomCropAttr { - return func(m optionalAttr) { - m["seed"] = value - } -} - -// RandomCropSeed2 sets the optional seed2 attribute to value. -// -// value: An second seed to avoid seed collision. -// If not specified, defaults to 0 -func RandomCropSeed2(value int64) RandomCropAttr { - return func(m optionalAttr) { - m["seed2"] = value - } -} - -// Randomly crop `image`. -// -// DEPRECATED at GraphDef version 8: Random crop is now pure Python -// -// `size` is a 1-D int64 tensor with 2 elements representing the crop height and -// width. The values must be non negative. -// -// This Op picks a random location in `image` and crops a `height` by `width` -// rectangle from that location. The random location is picked so the cropped -// area will fit inside the original image. -// -// Arguments: -// image: 3-D of shape `[height, width, channels]`. -// size: 1-D of length 2 containing: `crop_height`, `crop_width`.. -// -// Returns 3-D of shape `[crop_height, crop_width, channels].` -func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RandomCrop", - Input: []tf.Input{ - image, size, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DecodeJpegAttr is an optional argument to DecodeJpeg. -type DecodeJpegAttr func(optionalAttr) - -// DecodeJpegChannels sets the optional channels attribute to value. -// -// value: Number of color channels for the decoded image. -// If not specified, defaults to 0 -func DecodeJpegChannels(value int64) DecodeJpegAttr { - return func(m optionalAttr) { - m["channels"] = value - } -} - -// DecodeJpegRatio sets the optional ratio attribute to value. -// -// value: Downscaling ratio. -// If not specified, defaults to 1 -func DecodeJpegRatio(value int64) DecodeJpegAttr { - return func(m optionalAttr) { - m["ratio"] = value - } -} - -// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. -// -// value: If true use a slower but nicer upscaling of the -// chroma planes (yuv420/422 only). -// If not specified, defaults to true -func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr { - return func(m optionalAttr) { - m["fancy_upscaling"] = value - } -} - -// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. -// -// value: If true try to recover an image from truncated input. -// If not specified, defaults to false -func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr { - return func(m optionalAttr) { - m["try_recover_truncated"] = value - } -} - -// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. -// -// value: The minimum required fraction of lines before a truncated -// input is accepted. -// If not specified, defaults to 1 -func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr { - return func(m optionalAttr) { - m["acceptable_fraction"] = value - } -} - -// DecodeJpegDctMethod sets the optional dct_method attribute to value. -// -// value: string specifying a hint about the algorithm used for -// decompression. Defaults to "" which maps to a system-specific -// default. Currently valid values are ["INTEGER_FAST", -// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal -// jpeg library changes to a version that does not have that specific -// option.) -// If not specified, defaults to "" -func DecodeJpegDctMethod(value string) DecodeJpegAttr { - return func(m optionalAttr) { - m["dct_method"] = value - } -} - -// Decode a JPEG-encoded image to a uint8 tensor. -// -// The attr `channels` indicates the desired number of color channels for the -// decoded image. -// -// Accepted values are: -// -// * 0: Use the number of channels in the JPEG-encoded image. -// * 1: output a grayscale image. -// * 3: output an RGB image. -// -// If needed, the JPEG-encoded image is transformed to match the requested number -// of color channels. -// -// The attr `ratio` allows downscaling the image by an integer factor during -// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than -// downscaling the image later. -// -// -// This op also supports decoding PNGs and non-animated GIFs since the interface is -// the same, though it is cleaner to use `tf.image.decode_image`. -// -// Arguments: -// contents: 0-D. The JPEG-encoded image. -// -// Returns 3-D with shape `[height, width, channels]`.. -func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DecodeJpeg", - Input: []tf.Input{ - contents, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. -type DecodeAndCropJpegAttr func(optionalAttr) - -// DecodeAndCropJpegChannels sets the optional channels attribute to value. -// -// value: Number of color channels for the decoded image. -// If not specified, defaults to 0 -func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["channels"] = value - } -} - -// DecodeAndCropJpegRatio sets the optional ratio attribute to value. -// -// value: Downscaling ratio. -// If not specified, defaults to 1 -func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["ratio"] = value - } -} - -// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. -// -// value: If true use a slower but nicer upscaling of the -// chroma planes (yuv420/422 only). -// If not specified, defaults to true -func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["fancy_upscaling"] = value - } -} - -// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. -// -// value: If true try to recover an image from truncated input. -// If not specified, defaults to false -func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["try_recover_truncated"] = value - } -} - -// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. -// -// value: The minimum required fraction of lines before a truncated -// input is accepted. -// If not specified, defaults to 1 -func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["acceptable_fraction"] = value - } -} - -// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. -// -// value: string specifying a hint about the algorithm used for -// decompression. Defaults to "" which maps to a system-specific -// default. Currently valid values are ["INTEGER_FAST", -// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal -// jpeg library changes to a version that does not have that specific -// option.) -// If not specified, defaults to "" -func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { - return func(m optionalAttr) { - m["dct_method"] = value - } -} - -// Decode and Crop a JPEG-encoded image to a uint8 tensor. -// -// The attr `channels` indicates the desired number of color channels for the -// decoded image. -// -// Accepted values are: -// -// * 0: Use the number of channels in the JPEG-encoded image. -// * 1: output a grayscale image. -// * 3: output an RGB image. -// -// If needed, the JPEG-encoded image is transformed to match the requested number -// of color channels. -// -// The attr `ratio` allows downscaling the image by an integer factor during -// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than -// downscaling the image later. -// -// -// It is equivalent to a combination of decode and crop, but much faster by only -// decoding partial jpeg image. -// -// Arguments: -// contents: 0-D. The JPEG-encoded image. -// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. -// -// Returns 3-D with shape `[height, width, channels]`.. -func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DecodeAndCropJpeg", - Input: []tf.Input{ - contents, crop_window, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ShapeNAttr is an optional argument to ShapeN. -type ShapeNAttr func(optionalAttr) - -// ShapeNOutType sets the optional out_type attribute to value. -// If not specified, defaults to DT_INT32 -func ShapeNOutType(value tf.DataType) ShapeNAttr { - return func(m optionalAttr) { - m["out_type"] = value - } -} - -// Returns shape of tensors. -// -// This operation returns N 1-D integer tensors representing shape of `input[i]s`. -func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "ShapeN", - Input: []tf.Input{ - tf.OutputList(input), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if output, idx, err = makeOutputList(op, idx, "output"); err != nil { - scope.UpdateErr("ShapeN", err) - return - } - return output -} - -// Returns 0 if the denominator is zero. -// -// -// *NOTE*: `DivNoNan` supports broadcasting. More about broadcasting -// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func DivNoNan(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "DivNoNan", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } // EncodeJpegAttr is an optional argument to EncodeJpeg. @@ -39047,78 +38877,19 @@ func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (cont return op.Output(0) } -// Creates a dataset that skips `count` elements from the `input_dataset`. -// -// Arguments: -// -// count: A scalar representing the number of elements from the `input_dataset` -// that should be skipped. If count is -1, skips everything. -// -// -func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "SkipDataset", - Input: []tf.Input{ - input_dataset, count, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} +// LeakyReluAttr is an optional argument to LeakyRelu. +type LeakyReluAttr func(optionalAttr) -// JPEG encode input image with provided compression quality. -// -// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. -// `quality` is an int32 jpeg compression quality value between 0 and 100. -// -// -// Arguments: -// images: Images to adjust. At least 3-D. -// quality: An int quality to encode to. -// -// Returns 0-D. JPEG-encoded image. -func EncodeJpegVariableQuality(scope *Scope, images tf.Output, quality tf.Output) (contents tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "EncodeJpegVariableQuality", - Input: []tf.Input{ - images, quality, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape. -type ExtractJpegShapeAttr func(optionalAttr) - -// ExtractJpegShapeOutputType sets the optional output_type attribute to value. -// -// value: (Optional) The output type of the operation (int32 or int64). -// Defaults to int32. -// If not specified, defaults to DT_INT32 -func ExtractJpegShapeOutputType(value tf.DataType) ExtractJpegShapeAttr { +// LeakyReluAlpha sets the optional alpha attribute to value. +// If not specified, defaults to 0.2 +func LeakyReluAlpha(value float32) LeakyReluAttr { return func(m optionalAttr) { - m["output_type"] = value + m["alpha"] = value } } -// Extract the shape information of a JPEG-encoded image. -// -// This op only parses the image header, so it is much faster than DecodeJpeg. -// -// Arguments: -// contents: 0-D. The JPEG-encoded image. -// -// Returns 1-D. The image shape with format [height, width, channels]. -func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegShapeAttr) (image_shape tf.Output) { +// Computes rectified linear: `max(features, features * alpha)`. +func LeakyRelu(scope *Scope, features tf.Output, optional ...LeakyReluAttr) (activations tf.Output) { if scope.Err() != nil { return } @@ -39127,9 +38898,9 @@ func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegS a(attrs) } opspec := tf.OpSpec{ - Type: "ExtractJpegShape", + Type: "LeakyRelu", Input: []tf.Input{ - contents, + features, }, Attrs: attrs, } @@ -39137,38 +38908,183 @@ func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegS return op.Output(0) } -// EncodePngAttr is an optional argument to EncodePng. -type EncodePngAttr func(optionalAttr) - -// EncodePngCompression sets the optional compression attribute to value. +// Returns the number of work units this Reader has finished processing. // -// value: Compression level. -// If not specified, defaults to -1 -func EncodePngCompression(value int64) EncodePngAttr { +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderNumWorkUnitsCompletedV2", + Input: []tf.Input{ + reader_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Connects outputs of an N-way replicated computation to N outputs. +func TPUReplicatedOutput(scope *Scope, input tf.Output, num_replicas int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_replicas": num_replicas} + opspec := tf.OpSpec{ + Type: "TPUReplicatedOutput", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("TPUReplicatedOutput", err) + return + } + return outputs +} + +// Produce a string tensor that encodes the state of a Reader. +// +// Not all Readers support being serialized, so this can produce an +// Unimplemented error. +// +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderSerializeStateV2(scope *Scope, reader_handle tf.Output) (state tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderSerializeStateV2", + Input: []tf.Input{ + reader_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Restore a reader to a previously saved state. +// +// Not all Readers support being restored, so this can produce an +// Unimplemented error. +// +// Arguments: +// reader_handle: Handle to a Reader. +// state: Result of a ReaderSerializeState of a Reader with type +// matching reader_handle. +// +// Returns the created operation. +func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderRestoreStateV2", + Input: []tf.Input{ + reader_handle, state, + }, + } + return scope.AddOperation(opspec) +} + +// Bitcasts a tensor from one type to another without copying data. +// +// Given a tensor `input`, this operation returns a tensor that has the same buffer +// data as `input` with datatype `type`. +// +// If the input datatype `T` is larger than the output datatype `type` then the +// shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)]. +// +// If `T` is smaller than `type`, the operator requires that the rightmost +// dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from +// [..., sizeof(`type`)/sizeof(`T`)] to [...]. +// +// tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype +// (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() +// gives module error. +// For example, +// +// Example 1: +// ```python +// >>> a = [1., 2., 3.] +// >>> equality_bitcast = tf.bitcast(a,tf.complex128) +// tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot bitcast from float to complex128: shape [3] [Op:Bitcast] +// >>> equality_cast = tf.cast(a,tf.complex128) +// >>> print(equality_cast) +// tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128) +// ``` +// Example 2: +// ```python +// >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8) +// +// ``` +// Example 3: +// ```python +// >>> x = [1., 2., 3.] +// >>> y = [0., 2., 3.] +// >>> equality= tf.equal(x,y) +// >>> equality_cast = tf.cast(equality,tf.float32) +// >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8) +// >>> print(equality) +// tf.Tensor([False True True], shape=(3,), dtype=bool) +// >>> print(equality_cast) +// tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32) +// >>> print(equality_bitcast) +// tf.Tensor( +// [[ 0 0 0 0] +// [ 0 0 128 63] +// [ 0 0 128 63]], shape=(3, 4), dtype=uint8) +// ``` +// +// *NOTE*: Bitcast is implemented as a low-level cast, so machines with different +// endian orderings will give different results. +func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "Bitcast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorForestTreeResourceHandleOpAttr is an optional argument to TensorForestTreeResourceHandleOp. +type TensorForestTreeResourceHandleOpAttr func(optionalAttr) + +// TensorForestTreeResourceHandleOpContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func TensorForestTreeResourceHandleOpContainer(value string) TensorForestTreeResourceHandleOpAttr { return func(m optionalAttr) { - m["compression"] = value + m["container"] = value } } -// PNG-encode an image. -// -// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]` -// where `channels` is: -// -// * 1: for grayscale. -// * 2: for grayscale + alpha. -// * 3: for RGB. -// * 4: for RGBA. -// -// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder -// default or a value from 0 to 9. 9 is the highest compression level, generating -// the smallest output, but is slower. -// -// Arguments: -// image: 3-D with shape `[height, width, channels]`. -// -// Returns 0-D. PNG-encoded image. -func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) { +// TensorForestTreeResourceHandleOpSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func TensorForestTreeResourceHandleOpSharedName(value string) TensorForestTreeResourceHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a TensorForestTreeResource +func TensorForestTreeResourceHandleOp(scope *Scope, optional ...TensorForestTreeResourceHandleOpAttr) (resource tf.Output) { if scope.Err() != nil { return } @@ -39177,9 +39093,104 @@ func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (conten a(attrs) } opspec := tf.OpSpec{ - Type: "EncodePng", + Type: "TensorForestTreeResourceHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Restore a Reader to its initial clean state. +// +// Arguments: +// reader_handle: Handle to a Reader. +// +// Returns the created operation. +func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderResetV2", Input: []tf.Input{ - image, + reader_handle, + }, + } + return scope.AddOperation(opspec) +} + +// Returns the set of files matching one or more glob patterns. +// +// Note that this routine only supports wildcard characters in the +// basename portion of the pattern, not in the directory portion. +// Note also that the order of filenames returned can be non-deterministic. +// +// Arguments: +// pattern: Shell wildcard pattern(s). Scalar or vector of type string. +// +// Returns A vector of matching filenames. +func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatchingFiles", + Input: []tf.Input{ + pattern, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeAreaAttr is an optional argument to ResizeArea. +type ResizeAreaAttr func(optionalAttr) + +// ResizeAreaAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// Resize `images` to `size` using area interpolation. +// +// Input images can be of different types but output images are always float. +// +// The range of pixel values for the output image might be slightly different +// from the range for the input image because of limited numerical precision. +// To guarantee an output range, for example `[0.0, 1.0]`, apply +// `tf.clip_by_value` to the output. +// +// Each output pixel is computed by first transforming the pixel's footprint into +// the input tensor and then averaging the pixels that intersect the footprint. An +// input pixel's contribution to the average is weighted by the fraction of its +// area that intersects the footprint. This is the same as OpenCV's INTER_AREA. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeArea", + Input: []tf.Input{ + images, size, }, Attrs: attrs, } @@ -39187,78 +39198,229 @@ func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (conten return op.Output(0) } -// Greedily selects a subset of bounding boxes in descending order of score, +// ResizeBicubicGradAttr is an optional argument to ResizeBicubicGrad. +type ResizeBicubicGradAttr func(optionalAttr) + +// ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value. // -// pruning away boxes that have high overlaps -// with previously selected boxes. Bounding boxes with score less than -// `score_threshold` are removed. N-by-n overlap values are supplied as square matrix, -// which allows for defining a custom overlap criterium (eg. intersection over union, -// intersection over area, etc.). -// -// The output of this operation is a set of integers indexing into the input -// collection of bounding boxes representing the selected boxes. The bounding -// box coordinates corresponding to the selected indices can then be obtained -// using the `tf.gather operation`. For example: -// -// selected_indices = tf.image.non_max_suppression_with_overlaps( -// overlaps, scores, max_output_size, overlap_threshold, score_threshold) -// selected_boxes = tf.gather(boxes, selected_indices) +// value: If true, the centers of the 4 corner pixels of the input and grad tensors are +// aligned. Defaults to false. +// If not specified, defaults to false +func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBicubicGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBicubicGradHalfPixelCenters(value bool) ResizeBicubicGradAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Computes the gradient of bicubic interpolation. // // Arguments: -// overlaps: A 2-D float tensor of shape `[num_boxes, num_boxes]` representing -// the n-by-n box overlap values. -// scores: A 1-D float tensor of shape `[num_boxes]` representing a single -// score corresponding to each box (each row of boxes). -// max_output_size: A scalar integer tensor representing the maximum number of -// boxes to be selected by non max suppression. -// overlap_threshold: A 0-D float tensor representing the threshold for deciding whether -// boxes overlap too. -// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove -// boxes based on score. +// grads: 4-D with shape `[batch, height, width, channels]`. +// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`, +// The image tensor that was resized. // -// Returns A 1-D integer tensor of shape `[M]` representing the selected -// indices from the boxes tensor, where `M <= max_output_size`. -func NonMaxSuppressionWithOverlaps(scope *Scope, overlaps tf.Output, scores tf.Output, max_output_size tf.Output, overlap_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { +// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. +// Gradients with respect to the input image. Input image must have been +// float or double. +func ResizeBicubicGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBicubicGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBicubicGrad", + Input: []tf.Input{ + grads, original_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the number of records this Reader has produced. +// +// This is the same as the number of ReaderRead executions that have +// succeeded. +// +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "NonMaxSuppressionWithOverlaps", + Type: "ReaderNumRecordsProducedV2", Input: []tf.Input{ - overlaps, scores, max_output_size, overlap_threshold, score_threshold, + reader_handle, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// DecodeBmpAttr is an optional argument to DecodeBmp. -type DecodeBmpAttr func(optionalAttr) +// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear. +type QuantizedResizeBilinearAttr func(optionalAttr) -// DecodeBmpChannels sets the optional channels attribute to value. +// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// QuantizedResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func QuantizedResizeBilinearHalfPixelCenters(value bool) QuantizedResizeBilinearAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize quantized `images` to `size` using quantized bilinear interpolation. +// +// Input images and output images must be quantized types. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedResizeBilinear", + Input: []tf.Input{ + images, size, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// DecodeJpegAttr is an optional argument to DecodeJpeg. +type DecodeJpegAttr func(optionalAttr) + +// DecodeJpegChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. // If not specified, defaults to 0 -func DecodeBmpChannels(value int64) DecodeBmpAttr { +func DecodeJpegChannels(value int64) DecodeJpegAttr { return func(m optionalAttr) { m["channels"] = value } } -// Decode the first frame of a BMP-encoded image to a uint8 tensor. +// DecodeJpegRatio sets the optional ratio attribute to value. +// +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeJpegRatio(value int64) DecodeJpegAttr { + return func(m optionalAttr) { + m["ratio"] = value + } +} + +// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value + } +} + +// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value + } +} + +// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// DecodeJpegDctMethod sets the optional dct_method attribute to value. +// +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeJpegDctMethod(value string) DecodeJpegAttr { + return func(m optionalAttr) { + m["dct_method"] = value + } +} + +// Decode a JPEG-encoded image to a uint8 tensor. // // The attr `channels` indicates the desired number of color channels for the // decoded image. // // Accepted values are: // -// * 0: Use the number of channels in the BMP-encoded image. +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. // * 3: output an RGB image. -// * 4: output an RGBA image. +// +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. +// +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. +// +// +// This op also supports decoding PNGs and non-animated GIFs since the interface is +// the same, though it is cleaner to use `tf.image.decode_image`. // // Arguments: -// contents: 0-D. The BMP-encoded image. +// contents: 0-D. The JPEG-encoded image. // -// Returns 3-D with shape `[height, width, channels]`. RGB order -func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) { +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) { if scope.Err() != nil { return } @@ -39267,7 +39429,7 @@ func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (ima a(attrs) } opspec := tf.OpSpec{ - Type: "DecodeBmp", + Type: "DecodeJpeg", Input: []tf.Input{ contents, }, @@ -39277,36 +39439,119 @@ func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (ima return op.Output(0) } -// PrelinearizeAttr is an optional argument to Prelinearize. -type PrelinearizeAttr func(optionalAttr) +// Constructs an Optional variant from a tuple of tensors. +func OptionalFromValue(scope *Scope, components []tf.Output) (optional tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalFromValue", + Input: []tf.Input{ + tf.OutputList(components), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// PrelinearizeShape sets the optional shape attribute to value. +// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. +type DecodeAndCropJpegAttr func(optionalAttr) + +// DecodeAndCropJpegChannels sets the optional channels attribute to value. // -// value: The shape of the tensor. -// If not specified, defaults to <> -func PrelinearizeShape(value tf.Shape) PrelinearizeAttr { +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["shape"] = value + m["channels"] = value } } -// PrelinearizeLayout sets the optional layout attribute to value. +// DecodeAndCropJpegRatio sets the optional ratio attribute to value. // -// value: A vector holding the requested layout in minor-to-major sequence. If a layout -// attribute is passed but its values are all -1 the layout will be computed by -// the infeed operation. -// If not specified, defaults to <> -func PrelinearizeLayout(value []int64) PrelinearizeAttr { +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { return func(m optionalAttr) { - m["layout"] = value + m["ratio"] = value } } -// An op which linearizes one Tensor value to an opaque variant tensor. +// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value + } +} + +// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value + } +} + +// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. +// +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["dct_method"] = value + } +} + +// Decode and Crop a JPEG-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. +// +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. +// +// +// It is equivalent to a combination of decode and crop, but much faster by only +// decoding partial jpeg image. // // Arguments: -// input: A tensor that will be linearized. -func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) (output tf.Output) { +// contents: 0-D. The JPEG-encoded image. +// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. +// +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { if scope.Err() != nil { return } @@ -39315,9 +39560,124 @@ func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) ( a(attrs) } opspec := tf.OpSpec{ - Type: "Prelinearize", + Type: "DecodeAndCropJpeg", Input: []tf.Input{ - input, + contents, crop_window, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Writes the given dataset to the given file using the TFRecord format. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to write. +// filename: A scalar string tensor representing the filename to use. +// compression_type: A scalar string tensor containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// +// Returns the created operation. +func ExperimentalDatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalDatasetToTFRecord", + Input: []tf.Input{ + input_dataset, filename, compression_type, + }, + } + return scope.AddOperation(opspec) +} + +// Adjust the contrast of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last 3 dimensions are +// interpreted as `[height, width, channels]`. The other dimensions only +// represent a collection of images, such as `[batch, height, width, channels].` +// +// Contrast is adjusted independently for each channel of each image. +// +// For each channel, the Op first computes the mean of the image pixels in the +// channel and then adjusts each component of each pixel to +// `(x - mean) * contrast_factor + mean`. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// contrast_factor: A float multiplier for adjusting contrast. +// +// Returns The contrast-adjusted image or images. +func AdjustContrastv2(scope *Scope, images tf.Output, contrast_factor tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustContrastv2", + Input: []tf.Input{ + images, contrast_factor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodePngAttr is an optional argument to DecodePng. +type DecodePngAttr func(optionalAttr) + +// DecodePngChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodePngChannels(value int64) DecodePngAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// DecodePngDtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_UINT8 +func DecodePngDtype(value tf.DataType) DecodePngAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Decode a PNG-encoded image to a uint8 or uint16 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the PNG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// * 4: output an RGBA image. +// +// If needed, the PNG-encoded image is transformed to match the requested number +// of color channels. +// +// This op also supports decoding JPEGs and non-animated GIFs since the interface +// is the same, though it is cleaner to use `tf.image.decode_image`. +// +// Arguments: +// contents: 0-D. The PNG-encoded image. +// +// Returns 3-D with shape `[height, width, channels]`. +func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodePng", + Input: []tf.Input{ + contents, }, Attrs: attrs, } @@ -39354,85 +39714,162 @@ func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) { return op.Output(0) } -// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. -type SampleDistortedBoundingBoxAttr func(optionalAttr) +// Convert JSON-encoded Example records to binary protocol buffer strings. +// +// This op translates a tensor containing Example records, encoded using +// the [standard JSON +// mapping](https://developers.google.com/protocol-buffers/docs/proto3#json), +// into a tensor containing the same records encoded as binary protocol +// buffers. The resulting tensor can then be fed to any of the other +// Example-parsing ops. +// +// Arguments: +// json_examples: Each string is a JSON object serialized according to the JSON +// mapping of the Example proto. +// +// Returns Each string is a binary Example protocol buffer corresponding +// to the respective element of `json_examples`. +func DecodeJSONExample(scope *Scope, json_examples tf.Output) (binary_examples tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DecodeJSONExample", + Input: []tf.Input{ + json_examples, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} -// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// Convert one or more images from HSV to RGB. +// +// Outputs a tensor of the same shape as the `images` tensor, containing the RGB +// value of the pixels. The output is only well defined if the value in `images` +// are in `[0,1]`. +// +// See `rgb_to_hsv` for a description of the HSV encoding. +// +// Arguments: +// images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3. +// +// Returns `images` converted to RGB. +func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "HSVToRGB", + Input: []tf.Input{ + images, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Draw bounding boxes on a batch of images. +// +// Outputs a copy of `images` but draws on top of the pixels zero or more bounding +// boxes specified by the locations in `boxes`. The coordinates of the each +// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, if an image is 100 x 200 pixels (height x width) and the bounding +// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of +// the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates). +// +// Parts of the bounding box may fall outside the image. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. +// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding +// boxes. +// +// Returns 4-D with the same shape as `images`. The batch of input images with +// bounding boxes drawn on the images. +func DrawBoundingBoxes(scope *Scope, images tf.Output, boxes tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DrawBoundingBoxes", + Input: []tf.Input{ + images, boxes, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2. +type SampleDistortedBoundingBoxV2Attr func(optionalAttr) + +// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value. // // value: If either `seed` or `seed2` are set to non-zero, the random number // generator is seeded by the given `seed`. Otherwise, it is seeded by a random // seed. // If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["seed"] = value } } -// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// SampleDistortedBoundingBoxV2Seed2 sets the optional seed2 attribute to value. // // value: A second seed to avoid seed collision. // If not specified, defaults to 0 -func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["seed2"] = value } } -// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. -// -// value: The cropped area of the image must contain at least this -// fraction of any bounding box supplied. The value of this parameter should be -// non-negative. In the case of 0, the cropped area does not need to overlap -// any of the bounding boxes supplied. -// If not specified, defaults to 0.1 -func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { - return func(m optionalAttr) { - m["min_object_covered"] = value - } -} - -// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. +// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value. // // value: The cropped area of the image must have an aspect ratio = // width / height within this range. // If not specified, defaults to -func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["aspect_ratio_range"] = value } } -// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the // supplied image within this range. // If not specified, defaults to -func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["area_range"] = value } } -// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value. // // value: Number of attempts at generating a cropped region of the image // of the specified constraints. After `max_attempts` failures, return the entire // image. // If not specified, defaults to 100 -func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["max_attempts"] = value } } -// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. // // value: Controls behavior if no bounding boxes supplied. // If true, assume an implicit bounding box covering the whole input. If false, // raise an error. // If not specified, defaults to false -func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { +func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["use_image_if_no_bounding_boxes"] = value } @@ -39483,12 +39920,16 @@ func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDisto // image_size: 1-D, containing `[height, width, channels]`. // bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes // associated with the image. +// min_object_covered: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. // // Returns 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to // `tf.slice`.1-D, containing `[target_height, target_width, -1]`. Provide as input to // `tf.slice`.3-D with shape `[1, 1, 4]` containing the distorted bounding box. // Provide as input to `tf.image.draw_bounding_boxes`. -func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { +func SampleDistortedBoundingBoxV2(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, min_object_covered tf.Output, optional ...SampleDistortedBoundingBoxV2Attr) (begin tf.Output, size tf.Output, bboxes tf.Output) { if scope.Err() != nil { return } @@ -39497,9 +39938,9 @@ func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_box a(attrs) } opspec := tf.OpSpec{ - Type: "SampleDistortedBoundingBox", + Type: "SampleDistortedBoundingBoxV2", Input: []tf.Input{ - image_size, bounding_boxes, + image_size, bounding_boxes, min_object_covered, }, Attrs: attrs, } @@ -39507,356 +39948,138 @@ func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_box return op.Output(0), op.Output(1), op.Output(2) } -// LoadTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingCenteredRMSPropParameters. -type LoadTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) +// StagePeekAttr is an optional argument to StagePeek. +type StagePeekAttr func(optionalAttr) -// LoadTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. -// If not specified, defaults to -1 -// -// REQUIRES: value >= -1 -func LoadTPUEmbeddingCenteredRMSPropParametersTableId(value int64) LoadTPUEmbeddingCenteredRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_id"] = value - } -} - -// LoadTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. -// If not specified, defaults to "" -func LoadTPUEmbeddingCenteredRMSPropParametersTableName(value string) LoadTPUEmbeddingCenteredRMSPropParametersAttr { - return func(m optionalAttr) { - m["table_name"] = value - } -} - -// Load centered RMSProp embedding parameters. -// -// An op that loads optimization parameters into HBM for embedding. Must be -// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct -// embedding table configuration. For example, this op is used to install -// parameters that are loaded from a checkpoint before a training loop is -// executed. -// -// Arguments: -// parameters: Value of parameters used in the centered RMSProp optimization algorithm. -// ms: Value of ms used in the centered RMSProp optimization algorithm. -// mom: Value of mom used in the centered RMSProp optimization algorithm. -// mg: Value of mg used in the centered RMSProp optimization algorithm. -// -// -// -// Returns the created operation. -func LoadTPUEmbeddingCenteredRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingCenteredRMSPropParametersAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "LoadTPUEmbeddingCenteredRMSPropParameters", - Input: []tf.Input{ - parameters, ms, mom, mg, - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Outputs a `Summary` protocol buffer with a tensor and per-plugin data. -// -// Arguments: -// tag: A string attached to this summary. Used for organization in TensorBoard. -// tensor: A tensor to serialize. -// serialized_summary_metadata: A serialized SummaryMetadata proto. Contains plugin -// data. -func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_summary_metadata tf.Output) (summary tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorSummaryV2", - Input: []tf.Input{ - tag, tensor, serialized_summary_metadata, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// CropAndResizeGradBoxesAttr is an optional argument to CropAndResizeGradBoxes. -type CropAndResizeGradBoxesAttr func(optionalAttr) - -// CropAndResizeGradBoxesMethod sets the optional method attribute to value. -// -// value: A string specifying the interpolation method. Only 'bilinear' is -// supported for now. -// If not specified, defaults to "bilinear" -func CropAndResizeGradBoxesMethod(value string) CropAndResizeGradBoxesAttr { - return func(m optionalAttr) { - m["method"] = value - } -} - -// Computes the gradient of the crop_and_resize op wrt the input boxes tensor. -// -// Arguments: -// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. -// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. -// Both `image_height` and `image_width` need to be positive. -// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor -// specifies the coordinates of a box in the `box_ind[i]` image and is specified -// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of -// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the -// `[0, 1]` interval of normalized image height is mapped to -// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in -// which case the sampled crop is an up-down flipped version of the original -// image. The width dimension is treated similarly. Normalized coordinates -// outside the `[0, 1]` range are allowed, in which case we use -// `extrapolation_value` to extrapolate the input image values. -// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. -// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. -// -// Returns A 2-D tensor of shape `[num_boxes, 4]`. -func CropAndResizeGradBoxes(scope *Scope, grads tf.Output, image tf.Output, boxes tf.Output, box_ind tf.Output, optional ...CropAndResizeGradBoxesAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CropAndResizeGradBoxes", - Input: []tf.Input{ - grads, image, boxes, box_ind, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// RegexReplaceAttr is an optional argument to RegexReplace. -type RegexReplaceAttr func(optionalAttr) - -// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. -// -// value: If True, the replacement is global (that is, all matches of the `pattern` regular -// expression in each input string are rewritten), otherwise the `rewrite` -// substitution is only made for the first `pattern` match. -// If not specified, defaults to true -func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { - return func(m optionalAttr) { - m["replace_global"] = value - } -} - -// Replaces matches of the `pattern` regular expression in `input` with the -// replacement string provided in `rewrite`. -// -// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) -// -// Arguments: -// input: The text to be processed. -// pattern: The regular expression to be matched in the `input` strings. -// rewrite: The rewrite string to be substituted for the `pattern` expression where it is -// matched in the `input` strings. -// -// Returns The text after applying pattern match and rewrite substitution. -func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "RegexReplace", - Input: []tf.Input{ - input, pattern, rewrite, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter. -type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr) - -// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value. -// -// value: Specify the data format of the input and output data. With the -// default format "NHWC", the data is stored in the order of: -// [batch, height, width, channels]. -// Alternatively, the format could be "NCHW", the data storage order of: -// [batch, channels, height, width]. -// If not specified, defaults to "NHWC" -func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// DepthwiseConv2dNativeBackpropFilterDilations sets the optional dilations attribute to value. -// -// value: 1-D tensor of length 4. The dilation factor for each dimension of -// `input`. If set to k > 1, there will be k-1 skipped cells between each filter -// element on that dimension. The dimension order is determined by the value of -// `data_format`, see above for details. Dilations in the batch and depth -// dimensions must be 1. -// If not specified, defaults to -func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr { - return func(m optionalAttr) { - m["dilations"] = value - } -} - -// Computes the gradients of depthwise convolution with respect to the filter. -// -// Arguments: -// input: 4-D with shape based on `data_format`. For example, if -// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height, -// in_width, in_channels]` tensor. -// filter_sizes: An integer vector representing the tensor shape of `filter`, -// where `filter` is a 4-D -// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor. -// out_backprop: 4-D with shape based on `data_format`. -// For example, if `data_format` is 'NHWC' then -// out_backprop shape is `[batch, out_height, out_width, out_channels]`. -// Gradients w.r.t. the output of the convolution. -// strides: The stride of the sliding window for each dimension of the input -// of the convolution. -// padding: The type of padding algorithm to use. -// -// Returns 4-D with shape -// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. -// the `filter` input of the convolution. -func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "DepthwiseConv2dNativeBackpropFilter", - Input: []tf.Input{ - input, filter_sizes, out_backprop, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TryRpcAttr is an optional argument to TryRpc. -type TryRpcAttr func(optionalAttr) - -// TryRpcProtocol sets the optional protocol attribute to value. -// -// value: RPC protocol to use. Empty string means use the default protocol. -// Options include 'grpc'. -// If not specified, defaults to "" -func TryRpcProtocol(value string) TryRpcAttr { - return func(m optionalAttr) { - m["protocol"] = value - } -} - -// TryRpcFailFast sets the optional fail_fast attribute to value. -// -// value: `boolean`. If `true` (default), then failures to connect -// (i.e., the server does not immediately respond) cause an RPC failure. -// If not specified, defaults to true -func TryRpcFailFast(value bool) TryRpcAttr { - return func(m optionalAttr) { - m["fail_fast"] = value - } -} - -// TryRpcTimeoutInMs sets the optional timeout_in_ms attribute to value. -// -// value: `int`. If `0` (default), then the kernel will run the RPC -// request and only time out if the RPC deadline passes or the session times out. -// If this value is greater than `0`, then the op will raise an exception if -// the RPC takes longer than `timeout_in_ms`. +// StagePeekCapacity sets the optional capacity attribute to value. // If not specified, defaults to 0 -func TryRpcTimeoutInMs(value int64) TryRpcAttr { +// +// REQUIRES: value >= 0 +func StagePeekCapacity(value int64) StagePeekAttr { return func(m optionalAttr) { - m["timeout_in_ms"] = value + m["capacity"] = value } } -// Perform batches of RPC requests. +// StagePeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 // -// This op asynchronously performs either a single RPC request, or a batch -// of requests. RPC requests are defined by three main parameters: +// REQUIRES: value >= 0 +func StagePeekMemoryLimit(value int64) StagePeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StagePeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StagePeekContainer(value string) StagePeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StagePeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StagePeekSharedName(value string) StagePeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified index. If the // -// - `address` (the host+port or BNS address of the request) -// - `method` (the method name for the request) -// - `request` (the serialized proto string, or vector of strings, -// of the RPC request argument). +// underlying container does not contain sufficient elements +// this op will block until it does. This Op is optimized for +// performance. +func StagePeek(scope *Scope, index tf.Output, dtypes []tf.DataType, optional ...StagePeekAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StagePeek", + Input: []tf.Input{ + index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("StagePeek", err) + return + } + return values +} + +// NonMaxSuppressionV5Attr is an optional argument to NonMaxSuppressionV5. +type NonMaxSuppressionV5Attr func(optionalAttr) + +// NonMaxSuppressionV5PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. // -// For example, if you have an RPC service running on port localhost:2345, -// and its interface is configured with the following proto declaration: +// value: If true, the output `selected_indices` is padded to be of length +// `max_output_size`. Defaults to false. +// If not specified, defaults to false +func NonMaxSuppressionV5PadToMaxOutputSize(value bool) NonMaxSuppressionV5Attr { + return func(m optionalAttr) { + m["pad_to_max_output_size"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, // -// ``` -// service MyService { -// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { -// } -// }; -// ``` -// -// then call this op with arguments: -// -// ``` -// address = "localhost:2345" -// method = "MyService/MyMethod" -// ``` -// -// The `request` tensor is a string tensor representing serialized `MyRequestProto` -// strings; and the output string tensor `response` will have the same shape -// and contain (upon successful completion) corresponding serialized -// `MyResponseProto` strings. -// -// For example, to send a single, empty, `MyRequestProto`, call -// this op with `request = ""`. To send 5 **parallel** empty requests, -// call this op with `request = ["", "", "", "", ""]`. -// -// More generally, one can create a batch of `MyRequestProto` serialized protos -// from regular batched tensors using the `encode_proto` op, and convert -// the response `MyResponseProto` serialized protos to batched tensors -// using the `decode_proto` op. -// -// **NOTE** Working with serialized proto strings is faster than instantiating -// actual proto objects in memory, so no performance degradation is expected -// compared to writing custom kernels for this workflow. -// -// Unlike the standard `Rpc` op, if the connection fails or the remote worker -// returns an error status, this op does **not** reraise the exception. -// Instead, the `status_code` and `status_message` entry for the corresponding RPC -// call is set with the error returned from the RPC call. The `response` tensor -// will contain valid response values for those minibatch entries whose RPCs did -// not fail; the rest of the entries will have empty strings. +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. +// Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score +// of other overlapping boxes instead of directly causing them to be pruned. +// To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be +// larger than 0. // // Arguments: -// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `method` and `request`. -// method: `0-D` or `1-D`. The method address on the RPC server. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `address` and `request`. -// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. -// If this tensor has more than 1 element, then multiple parallel rpc requests -// are sent. This argument broadcasts with `address` and `method`. +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// soft_nms_sigma: A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et +// al (c.f. https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which +// is default), we fall back to standard (hard) NMS. // -// Returns Same shape as `request`. Serialized proto strings: the rpc responses.Same shape as `request`. Values correspond to tensorflow Status enum codes.Same shape as `request`. Values correspond to Status messages -// returned from the RPC calls. -func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...TryRpcAttr) (response tf.Output, status_code tf.Output, status_message tf.Output) { +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`.A 1-D float tensor of shape `[M]` representing the corresponding +// scores for each selected box, where `M <= max_output_size`. Scores only differ +// from corresponding input scores when using Soft NMS (i.e. when +// `soft_nms_sigma>0`)A 0-D integer tensor representing the number of valid elements in +// `selected_indices`, with the valid elements appearing first. +func NonMaxSuppressionV5(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, soft_nms_sigma tf.Output, optional ...NonMaxSuppressionV5Attr) (selected_indices tf.Output, selected_scores tf.Output, valid_outputs tf.Output) { if scope.Err() != nil { return } @@ -39865,9 +40088,9 @@ func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output a(attrs) } opspec := tf.OpSpec{ - Type: "TryRpc", + Type: "NonMaxSuppressionV5", Input: []tf.Input{ - address, method, request, + boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, }, Attrs: attrs, } @@ -39875,6 +40098,174 @@ func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output return op.Output(0), op.Output(1), op.Output(2) } +// CombinedNonMaxSuppressionAttr is an optional argument to CombinedNonMaxSuppression. +type CombinedNonMaxSuppressionAttr func(optionalAttr) + +// CombinedNonMaxSuppressionPadPerClass sets the optional pad_per_class attribute to value. +// +// value: If false, the output nmsed boxes, scores and classes +// are padded/clipped to `max_total_size`. If true, the +// output nmsed boxes, scores and classes are padded to be of length +// `max_size_per_class`*`num_classes`, unless it exceeds `max_total_size` in +// which case it is clipped to `max_total_size`. Defaults to false. +// If not specified, defaults to false +func CombinedNonMaxSuppressionPadPerClass(value bool) CombinedNonMaxSuppressionAttr { + return func(m optionalAttr) { + m["pad_per_class"] = value + } +} + +// CombinedNonMaxSuppressionClipBoxes sets the optional clip_boxes attribute to value. +// +// value: If true, assume the box coordinates are between [0, 1] and clip the output boxes +// if they fall beyond [0, 1]. If false, do not do clipping and output the box +// coordinates as it is. +// If not specified, defaults to true +func CombinedNonMaxSuppressionClipBoxes(value bool) CombinedNonMaxSuppressionAttr { + return func(m optionalAttr) { + m["clip_boxes"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// This operation performs non_max_suppression on the inputs per batch, across +// all classes. +// Prunes away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Also note that +// this algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is the final boxes, scores and classes tensor +// returned after performing non_max_suppression. +// +// Arguments: +// boxes: A 4-D float tensor of shape `[batch_size, num_boxes, q, 4]`. If `q` is 1 then +// same boxes are used for all classes otherwise, if `q` is equal to number of +// classes, class-specific boxes are used. +// scores: A 3-D float tensor of shape `[batch_size, num_boxes, num_classes]` +// representing a single score corresponding to each box (each row of boxes). +// max_output_size_per_class: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression per class +// max_total_size: A scalar representing maximum number of boxes retained over all classes. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A [batch_size, max_detections, 4] float32 tensor +// containing the non-max suppressed boxes.A [batch_size, max_detections] float32 tensor +// containing the scores for the boxes.A [batch_size, max_detections] float32 tensor +// containing the classes for the boxes.A [batch_size] int32 tensor indicating the number of +// valid detections per batch item. Only the top num_detections[i] entries in +// nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the +// entries are zero paddings. +func CombinedNonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size_per_class tf.Output, max_total_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...CombinedNonMaxSuppressionAttr) (nmsed_boxes tf.Output, nmsed_scores tf.Output, nmsed_classes tf.Output, valid_detections tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CombinedNonMaxSuppression", + Input: []tf.Input{ + boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// ExperimentalThreadPoolHandleAttr is an optional argument to ExperimentalThreadPoolHandle. +type ExperimentalThreadPoolHandleAttr func(optionalAttr) + +// ExperimentalThreadPoolHandleMaxIntraOpParallelism sets the optional max_intra_op_parallelism attribute to value. +// +// value: The maximum degree of parallelism to use within operations that execute on this +// threadpool. +// If not specified, defaults to 1 +func ExperimentalThreadPoolHandleMaxIntraOpParallelism(value int64) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["max_intra_op_parallelism"] = value + } +} + +// ExperimentalThreadPoolHandleContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func ExperimentalThreadPoolHandleContainer(value string) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// ExperimentalThreadPoolHandleSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func ExperimentalThreadPoolHandleSharedName(value string) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// num_threads: The number of threads in the thread pool. +// display_name: A human-readable name for the threads that may be visible in some +// visualizations. +// threadpool. +// +// Returns A resource that can be consumed by one or more ExperimentalThreadPoolDataset +// ops. +func ExperimentalThreadPoolHandle(scope *Scope, num_threads int64, display_name string, optional ...ExperimentalThreadPoolHandleAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_threads": num_threads, "display_name": display_name} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalThreadPoolHandle", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a MultiDeviceIterator resource. +// +// Arguments: +// devices: A list of devices the iterator works across. +// shared_name: If non-empty, this resource will be shared under the given name +// across multiple sessions. +// container: If non-empty, this resource is placed in the given container. +// Otherwise, a default container is used. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +// +// Returns Handle to the resource created. +func MultiDeviceIterator(scope *Scope, devices []string, shared_name string, container string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"devices": devices, "shared_name": shared_name, "container": container, "output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "MultiDeviceIterator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // SubstrAttr is an optional argument to Substr. type SubstrAttr func(optionalAttr) @@ -39995,6 +40386,97 @@ func Substr(scope *Scope, input tf.Output, pos tf.Output, len tf.Output, optiona return op.Output(0) } +// FusedBatchNormGradV2Attr is an optional argument to FusedBatchNormGradV2. +type FusedBatchNormGradV2Attr func(optionalAttr) + +// FusedBatchNormGradV2Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormGradV2Epsilon(value float32) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormGradV2DataFormat sets the optional data_format attribute to value. +// +// value: The data format for y_backprop, x, x_backprop. +// Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormGradV2DataFormat(value string) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormGradV2IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormGradV2IsTraining(value bool) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Gradient for batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. +// +// Returns A 4D Tensor for the gradient with respect to x.A 1D Tensor for the gradient with respect to scale.A 1D Tensor for the gradient with respect to offset.Unused placeholder to match the mean input in FusedBatchNorm.Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGradV2(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradV2Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormGradV2", + Input: []tf.Input{ + y_backprop, x, scale, reserve_space_1, reserve_space_2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Set a summary_writer_interface to record statistics using given stats_aggregator. +// +// Returns the created operation. +func StatsAggregatorSetSummaryWriter(scope *Scope, stats_aggregator tf.Output, summary tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatsAggregatorSetSummaryWriter", + Input: []tf.Input{ + stats_aggregator, summary, + }, + } + return scope.AddOperation(opspec) +} + // Creates a dataset that shards the input dataset. // // Creates a dataset that shards the input dataset by num_workers, returning a @@ -40067,433 +40549,130 @@ func ExperimentalDatasetCardinality(scope *Scope, input_dataset tf.Output) (card return op.Output(0) } -// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder. -type CTCBeamSearchDecoderAttr func(optionalAttr) +// MeanAttr is an optional argument to Mean. +type MeanAttr func(optionalAttr) -// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value. +// MeanKeepDims sets the optional keep_dims attribute to value. // -// value: If true, merge repeated classes in output. -// If not specified, defaults to true -func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr { +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MeanKeepDims(value bool) MeanAttr { return func(m optionalAttr) { - m["merge_repeated"] = value + m["keep_dims"] = value } } -// Performs beam search decoding on the logits given in input. +// Computes the mean of elements across dimensions of a tensor. // -// A note about the attribute merge_repeated: For the beam search decoder, -// this means that if consecutive entries in a beam are the same, only -// the first of these is emitted. That is, when the top path is "A B B B B", -// "A B" is returned if merge_repeated = True but "A B B B B" is -// returned if merge_repeated = False. +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. // // Arguments: -// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. -// sequence_length: A vector containing sequence lengths, size `(batch)`. -// beam_width: A scalar >= 0 (beam search beam width). -// top_paths: A scalar >= 0, <= beam_width (controls output size). +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. // -// Returns A list (length: top_paths) of indices matrices. Matrix j, -// size `(total_decoded_outputs[j] x 2)`, has indices of a -// `SparseTensor`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j, -// size `(length total_decoded_outputs[j])`, has the values of a -// `SparseTensor`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j, -// size `(2)`, stores the shape of the decoded `SparseTensor[j]`. -// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The -// sequence log-probabilities. -func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) { +// Returns The reduced tensor. +func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "CTCBeamSearchDecoder", + Type: "Mean", Input: []tf.Input{ - inputs, sequence_length, + input, axis, }, Attrs: attrs, } op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A substitute for `InterleaveDataset` on a fixed list of `N` datasets. +// +// Arguments: +// selector_input_dataset: A dataset of scalar `DT_INT64` elements that determines which of the +// `N` data inputs should produce the next output element. +// data_input_datasets: `N` datasets with the same type that will be interleaved according to +// the values of `selector_input_dataset`. +// +// +func ExperimentalDirectedInterleaveDataset(scope *Scope, selector_input_dataset tf.Output, data_input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } - var idx int - var err error - if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalDirectedInterleaveDataset", + Input: []tf.Input{ + selector_input_dataset, tf.OutputList(data_input_datasets), + }, + Attrs: attrs, } - if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return - } - if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return - } - log_probability = op.Output(idx) - return decoded_indices, decoded_values, decoded_shape, log_probability + op := scope.AddOperation(opspec) + return op.Output(0) } -// MapIncompleteSizeAttr is an optional argument to MapIncompleteSize. -type MapIncompleteSizeAttr func(optionalAttr) +// PrintAttr is an optional argument to Print. +type PrintAttr func(optionalAttr) -// MapIncompleteSizeCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 +// PrintMessage sets the optional message attribute to value. // -// REQUIRES: value >= 0 -func MapIncompleteSizeCapacity(value int64) MapIncompleteSizeAttr { +// value: A string, prefix of the error message. +// If not specified, defaults to "" +func PrintMessage(value string) PrintAttr { return func(m optionalAttr) { - m["capacity"] = value + m["message"] = value } } -// MapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 +// PrintFirstN sets the optional first_n attribute to value. // -// REQUIRES: value >= 0 -func MapIncompleteSizeMemoryLimit(value int64) MapIncompleteSizeAttr { +// value: Only log `first_n` number of times. -1 disables logging. +// If not specified, defaults to -1 +func PrintFirstN(value int64) PrintAttr { return func(m optionalAttr) { - m["memory_limit"] = value + m["first_n"] = value } } -// MapIncompleteSizeContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapIncompleteSizeContainer(value string) MapIncompleteSizeAttr { +// PrintSummarize sets the optional summarize attribute to value. +// +// value: Only print this many entries of each tensor. +// If not specified, defaults to 3 +func PrintSummarize(value int64) PrintAttr { return func(m optionalAttr) { - m["container"] = value + m["summarize"] = value } } -// MapIncompleteSizeSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapIncompleteSizeSharedName(value string) MapIncompleteSizeAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op returns the number of incomplete elements in the underlying container. -func MapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...MapIncompleteSizeAttr) (size tf.Output) { +// Prints a list of tensors. +// +// Passes `input` through to `output` and prints `data` when evaluating. +// +// Arguments: +// input: The tensor passed to `output` +// data: A list of tensors to print out when op is evaluated. +// +// Returns = The unmodified `input` tensor +func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"dtypes": dtypes} + attrs := map[string]interface{}{} for _, a := range optional { a(attrs) } opspec := tf.OpSpec{ - Type: "MapIncompleteSize", - - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that batches input elements into a SparseTensor. -// -// Arguments: -// input_dataset: A handle to an input dataset. Must have a single component. -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. -// row_shape: A vector representing the dense shape of each row in the produced -// SparseTensor. The shape may be partially specified, using `-1` to indicate -// that a particular dimension should use the maximum size of all batch elements. -// -// -func ExperimentalDenseToSparseBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, row_shape tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalDenseToSparseBatchDataset", + Type: "Print", Input: []tf.Input{ - input_dataset, batch_size, row_shape, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Sets the index-th position of the list to contain the given tensor. -// -// input_handle: the list -// index: the position in the list to which the tensor will be assigned -// item: the element to be assigned to that position -// output_handle: the new list, with the element in the proper position -// -func TensorListSetItem(scope *Scope, input_handle tf.Output, index tf.Output, item tf.Output) (output_handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "TensorListSetItem", - Input: []tf.Input{ - input_handle, index, item, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Converts the given `resource_handle` representing an iterator to a variant tensor. -// -// Arguments: -// resource_handle: A handle to an iterator resource. -// -// Returns A variant tensor storing the state of the iterator contained in the -// resource. -func SerializeIterator(scope *Scope, resource_handle tf.Output) (serialized tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SerializeIterator", - Input: []tf.Input{ - resource_handle, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// SnapshotDatasetAttr is an optional argument to SnapshotDataset. -type SnapshotDatasetAttr func(optionalAttr) - -// SnapshotDatasetCompression sets the optional compression attribute to value. -// If not specified, defaults to "" -func SnapshotDatasetCompression(value string) SnapshotDatasetAttr { - return func(m optionalAttr) { - m["compression"] = value - } -} - -// SnapshotDatasetReaderPathPrefix sets the optional reader_path_prefix attribute to value. -// If not specified, defaults to "" -func SnapshotDatasetReaderPathPrefix(value string) SnapshotDatasetAttr { - return func(m optionalAttr) { - m["reader_path_prefix"] = value - } -} - -// SnapshotDatasetWriterPathPrefix sets the optional writer_path_prefix attribute to value. -// If not specified, defaults to "" -func SnapshotDatasetWriterPathPrefix(value string) SnapshotDatasetAttr { - return func(m optionalAttr) { - m["writer_path_prefix"] = value - } -} - -// Creates a dataset that will write to / read from a snapshot. -// -// This dataset attempts to determine whether a valid snapshot exists at the -// `snapshot_path`, and reads from the snapshot in lieu of using `input_dataset`. -// If not, it will run the preprocessing pipeline as usual, and write out a -// snapshot of the data processed for future use. -// -// Arguments: -// input_dataset: A variant tensor representing the input dataset. -// path: The path we should write snapshots to / read snapshots from. -// -// -func SnapshotDataset(scope *Scope, input_dataset tf.Output, path tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...SnapshotDatasetAttr) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "SnapshotDataset", - Input: []tf.Input{ - input_dataset, path, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Makes the summary of accumulated stats for the batch. -// -// The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example. -// -// Arguments: -// node_ids: int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer. -// gradients: float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients. -// hessians: float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians. -// bucketized_features_list: int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column). -// max_splits: int; the maximum number of splits possible in the whole tree. -// num_buckets: int; equals to the maximum possible value of bucketized feature. -// -// Returns output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket. The first index of 4th dimension refers to gradients, and the second to hessians. -func BoostedTreesMakeStatsSummary(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, bucketized_features_list []tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} - opspec := tf.OpSpec{ - Type: "BoostedTreesMakeStatsSummary", - Input: []tf.Input{ - node_ids, gradients, hessians, tf.OutputList(bucketized_features_list), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// A dataset that splits the elements of its input into multiple elements. -func ExperimentalUnbatchDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalUnbatchDataset", - Input: []tf.Input{ - input_dataset, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Component-wise divides a SparseTensor by a dense Tensor. -// -// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not -// the other direction. -// -// Arguments: -// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a -// SparseTensor, possibly not in canonical ordering. -// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. -// sp_shape: 1-D. Shape of the input SparseTensor. -// dense: `R`-D. The dense Tensor operand. -// -// Returns 1-D. The `N` values that are operated on. -func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseDenseCwiseDiv", - Input: []tf.Input{ - sp_indices, sp_values, sp_shape, dense, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Creates a dataset that overrides the maximum intra-op parallelism. -// -// Arguments: -// -// max_intra_op_parallelism: Identifies the maximum intra-op parallelism to use. -// -// -func ExperimentalMaxIntraOpParallelismDataset(scope *Scope, input_dataset tf.Output, max_intra_op_parallelism tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "ExperimentalMaxIntraOpParallelismDataset", - Input: []tf.Input{ - input_dataset, max_intra_op_parallelism, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// OrderedMapClearAttr is an optional argument to OrderedMapClear. -type OrderedMapClearAttr func(optionalAttr) - -// OrderedMapClearCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapClearCapacity(value int64) OrderedMapClearAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// OrderedMapClearMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func OrderedMapClearMemoryLimit(value int64) OrderedMapClearAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// OrderedMapClearContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func OrderedMapClearContainer(value string) OrderedMapClearAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// OrderedMapClearSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func OrderedMapClearSharedName(value string) OrderedMapClearAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op removes all elements in the underlying container. -// -// Returns the created operation. -func OrderedMapClear(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapClearAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "OrderedMapClear", - - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - -// Creates a dataset that batches `batch_size` elements from `input_dataset`. -// -// Arguments: -// -// batch_size: A scalar representing the number of elements to accumulate in a -// batch. -// -// -func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} - opspec := tf.OpSpec{ - Type: "BatchDataset", - Input: []tf.Input{ - input_dataset, batch_size, + input, tf.OutputList(data), }, Attrs: attrs, } @@ -40505,18 +40684,18 @@ func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, o // // Arguments: // -// thread_pool: A resource produced by the ThreadPoolHandle op. +// num_threads: Identifies the number of threads to use for the private threadpool. // // -func ExperimentalThreadPoolDataset(scope *Scope, input_dataset tf.Output, thread_pool tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +func ExperimentalPrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "ExperimentalThreadPoolDataset", + Type: "ExperimentalPrivateThreadPoolDataset", Input: []tf.Input{ - input_dataset, thread_pool, + input_dataset, num_threads, }, Attrs: attrs, } @@ -40524,91 +40703,113 @@ func ExperimentalThreadPoolDataset(scope *Scope, input_dataset tf.Output, thread return op.Output(0) } -// Computes the power of one value to another. +// Outputs a tensor containing the reduction across all input tensors. // -// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for -// corresponding elements in `x` and `y`. For example: +// Outputs a tensor containing the reduction across all input tensors passed to ops +// within the same `shared_name. // -// ``` -// # tensor 'x' is [[2, 2]], [3, 3]] -// # tensor 'y' is [[8, 16], [2, 3]] -// tf.pow(x, y) ==> [[256, 65536], [9, 27]] -// ``` -func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { +// The graph should be constructed so if one op runs with shared_name value `c`, +// then `num_devices` ops will run with shared_name value `c`. Failure to do so +// will cause the graph execution to fail to complete. +// +// input: the input to the reduction +// data: the value of the reduction across all `num_devices` devices. +// reduction: the reduction operation to perform. +// num_devices: The number of devices participating in this reduction. +// shared_name: Identifier that shared between ops of the same reduction. +func NcclAllReduce(scope *Scope, input tf.Output, reduction string, num_devices int64, shared_name string) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"reduction": reduction, "num_devices": num_devices, "shared_name": shared_name} + opspec := tf.OpSpec{ + Type: "NcclAllReduce", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the name of the device on which `resource` has been placed. +func ExperimentalIteratorGetDevice(scope *Scope, resource tf.Output) (device tf.Output) { if scope.Err() != nil { return } opspec := tf.OpSpec{ - Type: "Pow", + Type: "ExperimentalIteratorGetDevice", Input: []tf.Input{ - x, y, + resource, }, } op := scope.AddOperation(opspec) return op.Output(0) } -// StageAttr is an optional argument to Stage. -type StageAttr func(optionalAttr) - -// StageCapacity sets the optional capacity attribute to value. +// Computes the reciprocal of x element-wise. // -// value: Maximum number of elements in the Staging Area. If > 0, inserts -// on the container will block when the capacity is reached. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func StageCapacity(value int64) StageAttr { - return func(m optionalAttr) { - m["capacity"] = value +// I.e., \\(y = 1 / x\\). +func Inv(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return } + opspec := tf.OpSpec{ + Type: "Inv", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// StageMemoryLimit sets the optional memory_limit attribute to value. +// Creates a dataset that executes a SQL query and emits rows of the result set. // -// value: The maximum number of bytes allowed for Tensors in the Staging Area. -// If > 0, inserts will block until sufficient space is available. -// If not specified, defaults to 0 +// Arguments: +// driver_name: The database type. Currently, the only supported type is 'sqlite'. +// data_source_name: A connection string to connect to the database. +// query: A SQL query to execute. // -// REQUIRES: value >= 0 -func StageMemoryLimit(value int64) StageAttr { - return func(m optionalAttr) { - m["memory_limit"] = value +// +func ExperimentalSqlDataset(scope *Scope, driver_name tf.Output, data_source_name tf.Output, query tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalSqlDataset", + Input: []tf.Input{ + driver_name, data_source_name, query, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) } -// StageContainer sets the optional container attribute to value. -// -// value: If non-empty, this queue is placed in the given container. Otherwise, -// a default container is used. +// ExperimentalStatsAggregatorHandleAttr is an optional argument to ExperimentalStatsAggregatorHandle. +type ExperimentalStatsAggregatorHandleAttr func(optionalAttr) + +// ExperimentalStatsAggregatorHandleContainer sets the optional container attribute to value. // If not specified, defaults to "" -func StageContainer(value string) StageAttr { +func ExperimentalStatsAggregatorHandleContainer(value string) ExperimentalStatsAggregatorHandleAttr { return func(m optionalAttr) { m["container"] = value } } -// StageSharedName sets the optional shared_name attribute to value. -// -// value: It is necessary to match this name to the matching Unstage Op. +// ExperimentalStatsAggregatorHandleSharedName sets the optional shared_name attribute to value. // If not specified, defaults to "" -func StageSharedName(value string) StageAttr { +func ExperimentalStatsAggregatorHandleSharedName(value string) ExperimentalStatsAggregatorHandleAttr { return func(m optionalAttr) { m["shared_name"] = value } } -// Stage values similar to a lightweight Enqueue. -// -// The basic functionality of this Op is similar to a queue with many -// fewer capabilities and options. This Op is optimized for performance. -// -// Arguments: -// values: a list of tensors -// dtypes A list of data types that inserted values should adhere to. -// -// Returns the created operation. -func Stage(scope *Scope, values []tf.Output, optional ...StageAttr) (o *tf.Operation) { +// Creates a statistics manager resource. +func ExperimentalStatsAggregatorHandle(scope *Scope, optional ...ExperimentalStatsAggregatorHandleAttr) (handle tf.Output) { if scope.Err() != nil { return } @@ -40617,55 +40818,24 @@ func Stage(scope *Scope, values []tf.Output, optional ...StageAttr) (o *tf.Opera a(attrs) } opspec := tf.OpSpec{ - Type: "Stage", - Input: []tf.Input{ - tf.OutputList(values), - }, - Attrs: attrs, - } - return scope.AddOperation(opspec) -} + Type: "ExperimentalStatsAggregatorHandle", -// Computes arctangent of `y/x` element-wise, respecting signs of the arguments. -// -// This is the angle \( \theta \in [-\pi, \pi] \) such that -// \[ x = r \cos(\theta) \] -// and -// \[ y = r \sin(\theta) \] -// where \(r = \sqrt(x^2 + y^2) \). -func Atan2(scope *Scope, y tf.Output, x tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Atan2", - Input: []tf.Input{ - y, x, - }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Creates a dataset that contains `rate` elements from the `input_dataset`. -// -// Arguments: -// -// rate: A scalar representing the sample rate of elements from the `input_dataset` -// that should be taken. -// seed: A scalar representing seed of random number generator. -// seed2: A scalar representing seed2 of random number generator. -// -// -func SamplingDataset(scope *Scope, input_dataset tf.Output, rate tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +// Creates a dataset that contains the unique elements of `input_dataset`. +func ExperimentalUniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "SamplingDataset", + Type: "ExperimentalUniqueDataset", Input: []tf.Input{ - input_dataset, rate, seed, seed2, + input_dataset, }, Attrs: attrs, } @@ -40673,45 +40843,207 @@ func SamplingDataset(scope *Scope, input_dataset tf.Output, rate tf.Output, seed return op.Output(0) } -// Adjust the contrast of one or more images. +// SerializeSparseAttr is an optional argument to SerializeSparse. +type SerializeSparseAttr func(optionalAttr) + +// SerializeSparseOutType sets the optional out_type attribute to value. // -// `images` is a tensor of at least 3 dimensions. The last 3 dimensions are -// interpreted as `[height, width, channels]`. The other dimensions only -// represent a collection of images, such as `[batch, height, width, channels].` -// -// Contrast is adjusted independently for each channel of each image. -// -// For each channel, the Op first computes the mean of the image pixels in the -// channel and then adjusts each component of each pixel to -// `(x - mean) * contrast_factor + mean`. +// value: The `dtype` to use for serialization; the supported types are `string` +// (default) and `variant`. +// If not specified, defaults to DT_STRING +func SerializeSparseOutType(value tf.DataType) SerializeSparseAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Serialize a `SparseTensor` into a `[3]` `Tensor` object. // // Arguments: -// images: Images to adjust. At least 3-D. -// contrast_factor: A float multiplier for adjusting contrast. -// -// Returns The contrast-adjusted image or images. -func AdjustContrastv2(scope *Scope, images tf.Output, contrast_factor tf.Output) (output tf.Output) { +// sparse_indices: 2-D. The `indices` of the `SparseTensor`. +// sparse_values: 1-D. The `values` of the `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the `SparseTensor`. +func SerializeSparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeSparseAttr) (serialized_sparse tf.Output) { if scope.Err() != nil { return } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ - Type: "AdjustContrastv2", + Type: "SerializeSparse", Input: []tf.Input{ - images, contrast_factor, + sparse_indices, sparse_values, sparse_shape, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) } -// Creates a dataset that emits `components` as a tuple of tensors once. -func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { +// MfccAttr is an optional argument to Mfcc. +type MfccAttr func(optionalAttr) + +// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. +// +// value: The highest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 4000 +func MfccUpperFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["upper_frequency_limit"] = value + } +} + +// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. +// +// value: The lowest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 20 +func MfccLowerFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["lower_frequency_limit"] = value + } +} + +// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. +// +// value: Resolution of the Mel bank used internally. +// If not specified, defaults to 40 +func MfccFilterbankChannelCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["filterbank_channel_count"] = value + } +} + +// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. +// +// value: How many output channels to produce per time slice. +// If not specified, defaults to 13 +func MfccDctCoefficientCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["dct_coefficient_count"] = value + } +} + +// Transforms a spectrogram into a form that's useful for speech recognition. +// +// Mel Frequency Cepstral Coefficients are a way of representing audio data that's +// been effective as an input feature for machine learning. They are created by +// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the +// higher frequencies that are less significant to the human ear. They have a long +// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum +// is a good resource to learn more. +// +// Arguments: +// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared +// set to true. +// sample_rate: How many samples per second the source audio used. +func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Mfcc", + Input: []tf.Input{ + spectrogram, sample_rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EncodeProtoAttr is an optional argument to EncodeProto. +type EncodeProtoAttr func(optionalAttr) + +// EncodeProtoDescriptorSource sets the optional descriptor_source attribute to value. +// If not specified, defaults to "local://" +func EncodeProtoDescriptorSource(value string) EncodeProtoAttr { + return func(m optionalAttr) { + m["descriptor_source"] = value + } +} + +// The op serializes protobuf messages provided in the input tensors. +// +// The types of the tensors in `values` must match the schema for the +// fields specified in `field_names`. All the tensors in `values` must +// have a common shape prefix, *batch_shape*. +// +// The `sizes` tensor specifies repeat counts for each field. The repeat +// count (last dimension) of a each tensor in `values` must be greater +// than or equal to corresponding repeat count in `sizes`. +// +// A `message_type` name must be provided to give context for the field +// names. The actual message descriptor can be looked up either in the +// linked-in descriptor pool or a filename provided by the caller using +// the `descriptor_source` attribute. +// +// The `descriptor_source` attribute selects a source of protocol +// descriptors to consult when looking up `message_type`. This may be a +// filename containing a serialized `FileDescriptorSet` message, +// or the special value `local://`, in which case only descriptors linked +// into the code will be searched; the filename can be on any filesystem +// accessible to TensorFlow. +// +// You can build a `descriptor_source` file using the `--descriptor_set_out` +// and `--include_imports` options to the protocol compiler `protoc`. +// +// The `local://` database only covers descriptors linked into the +// code via C++ libraries, not Python imports. You can link in a proto descriptor +// by creating a cc_library target with alwayslink=1. +// +// There are a few special cases in the value mapping: +// +// Submessage and group fields must be pre-serialized as TensorFlow strings. +// +// TensorFlow lacks support for unsigned int64s, so they must be +// represented as `tf.int64` with the same twos-complement bit pattern +// (the obvious way). +// +// Unsigned int32 values can be represented exactly with `tf.int64`, or +// with sign wrapping if the input is of type `tf.int32`. +// +// Arguments: +// sizes: Tensor of int32 with shape `[batch_shape, len(field_names)]`. +// values: List of tensors containing values for the corresponding field. +// field_names: List of strings containing proto field names. +// message_type: Name of the proto message type to decode. +// +// Returns Tensor of serialized protos with shape `batch_shape`. +func EncodeProto(scope *Scope, sizes tf.Output, values []tf.Output, field_names []string, message_type string, optional ...EncodeProtoAttr) (bytes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"field_names": field_names, "message_type": message_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodeProto", + Input: []tf.Input{ + sizes, tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits each dim-0 slice of `components` once. +func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"output_shapes": output_shapes} opspec := tf.OpSpec{ - Type: "TensorDataset", + Type: "TensorSliceDataset", Input: []tf.Input{ tf.OutputList(components), }, @@ -40721,280 +41053,6 @@ func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shap return op.Output(0) } -// MapPeekAttr is an optional argument to MapPeek. -type MapPeekAttr func(optionalAttr) - -// MapPeekCapacity sets the optional capacity attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekCapacity(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["capacity"] = value - } -} - -// MapPeekMemoryLimit sets the optional memory_limit attribute to value. -// If not specified, defaults to 0 -// -// REQUIRES: value >= 0 -func MapPeekMemoryLimit(value int64) MapPeekAttr { - return func(m optionalAttr) { - m["memory_limit"] = value - } -} - -// MapPeekContainer sets the optional container attribute to value. -// If not specified, defaults to "" -func MapPeekContainer(value string) MapPeekAttr { - return func(m optionalAttr) { - m["container"] = value - } -} - -// MapPeekSharedName sets the optional shared_name attribute to value. -// If not specified, defaults to "" -func MapPeekSharedName(value string) MapPeekAttr { - return func(m optionalAttr) { - m["shared_name"] = value - } -} - -// Op peeks at the values at the specified key. If the -// -// underlying container does not contain this key -// this op will block until it does. -func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"dtypes": dtypes} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "MapPeek", - Input: []tf.Input{ - key, indices, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if values, idx, err = makeOutputList(op, idx, "values"); err != nil { - scope.UpdateErr("MapPeek", err) - return - } - return values -} - -// Creates a dataset that splits a SparseTensor into elements row-wise. -func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseTensorSliceDataset", - Input: []tf.Input{ - indices, values, dense_shape, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Pads a tensor with mirrored values. -// -// This operation pads a `input` with mirrored values according to the `paddings` -// you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is -// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates -// how many values to add before the contents of `input` in that dimension, and -// `paddings[D, 1]` indicates how many values to add after the contents of `input` -// in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater -// than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true -// (if false, respectively). -// -// The padded size of each dimension D of the output is: -// -// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` -// -// For example: -// -// ``` -// # 't' is [[1, 2, 3], [4, 5, 6]]. -// # 'paddings' is [[1, 1]], [2, 2]]. -// # 'mode' is SYMMETRIC. -// # rank of 't' is 2. -// pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2] -// [2, 1, 1, 2, 3, 3, 2] -// [5, 4, 4, 5, 6, 6, 5] -// [5, 4, 4, 5, 6, 6, 5]] -// ``` -// -// Arguments: -// input: The input tensor to be padded. -// paddings: A two-column matrix specifying the padding sizes. The number of -// rows must be the same as the rank of `input`. -// mode: Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions -// do not include the borders, while in symmetric mode the padded regions -// do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings` -// is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and -// it is `[1, 2, 3, 3, 2]` in symmetric mode. -// -// Returns The padded tensor. -func MirrorPad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"mode": mode} - opspec := tf.OpSpec{ - Type: "MirrorPad", - Input: []tf.Input{ - input, paddings, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Computes the Cholesky decomposition of one or more square matrices. -// -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. -// -// The input has to be symmetric and positive definite. Only the lower-triangular -// part of the input will be used for this operation. The upper-triangular part -// will not be read. -// -// The output is a tensor of the same shape as the input -// containing the Cholesky decompositions for all input submatrices `[..., :, :]`. -// -// **Note**: The gradient computation on GPU is faster for large matrices but -// not for large batch dimensions when the submatrices are small. In this -// case it might be faster to use the CPU. -// -// Arguments: -// input: Shape is `[..., M, M]`. -// -// Returns Shape is `[..., M, M]`. -func Cholesky(scope *Scope, input tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "Cholesky", - Input: []tf.Input{ - input, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TPUReplicateMetadataAttr is an optional argument to TPUReplicateMetadata. -type TPUReplicateMetadataAttr func(optionalAttr) - -// TPUReplicateMetadataNumCoresPerReplica sets the optional num_cores_per_replica attribute to value. -// -// value: Number of cores per replica. Used for model parallelism. -// If not specified, defaults to 1 -func TPUReplicateMetadataNumCoresPerReplica(value int64) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["num_cores_per_replica"] = value - } -} - -// TPUReplicateMetadataTopology sets the optional topology attribute to value. -// -// value: TopologyProto indicating the topology of the TPU pod slice. -// If not specified, defaults to "" -func TPUReplicateMetadataTopology(value string) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["topology"] = value - } -} - -// TPUReplicateMetadataUseTpu sets the optional use_tpu attribute to value. -// -// value: Whether to place the computation on the TPU. -// If not specified, defaults to true -func TPUReplicateMetadataUseTpu(value bool) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["use_tpu"] = value - } -} - -// TPUReplicateMetadataDeviceAssignment sets the optional device_assignment attribute to value. -// -// value: The assignment of devices for the computation. -// If not specified, defaults to <> -func TPUReplicateMetadataDeviceAssignment(value []int64) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["device_assignment"] = value - } -} - -// TPUReplicateMetadataComputationShape sets the optional computation_shape attribute to value. -// -// value: DEPRECATED. Use num_cores_per_replica instead. -// If not specified, defaults to <> -func TPUReplicateMetadataComputationShape(value []int64) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["computation_shape"] = value - } -} - -// TPUReplicateMetadataHostComputeCore sets the optional host_compute_core attribute to value. -// If not specified, defaults to <> -func TPUReplicateMetadataHostComputeCore(value []string) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["host_compute_core"] = value - } -} - -// TPUReplicateMetadataPaddingMap sets the optional padding_map attribute to value. -// If not specified, defaults to <> -func TPUReplicateMetadataPaddingMap(value []string) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["padding_map"] = value - } -} - -// TPUReplicateMetadataStepMarkerLocation sets the optional step_marker_location attribute to value. -// If not specified, defaults to "STEP_MARK_AT_ENTRY" -func TPUReplicateMetadataStepMarkerLocation(value string) TPUReplicateMetadataAttr { - return func(m optionalAttr) { - m["step_marker_location"] = value - } -} - -// Metadata indicaitng how the TPU computation should be replicated. -// -// Arguments: -// num_replicas: Number of replicas of the computation -// -// Returns the created operation. -func TPUReplicateMetadata(scope *Scope, num_replicas int64, optional ...TPUReplicateMetadataAttr) (o *tf.Operation) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"num_replicas": num_replicas} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TPUReplicateMetadata", - - Attrs: attrs, - } - return scope.AddOperation(opspec) -} - // Creates a dataset that zips together `input_datasets`. func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { @@ -41012,87 +41070,6 @@ func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.Data return op.Output(0) } -// QuantizedMatMulWithBiasAndReluAttr is an optional argument to QuantizedMatMulWithBiasAndRelu. -type QuantizedMatMulWithBiasAndReluAttr func(optionalAttr) - -// QuantizedMatMulWithBiasAndReluToutput sets the optional Toutput attribute to value. -// If not specified, defaults to DT_QINT32 -func QuantizedMatMulWithBiasAndReluToutput(value tf.DataType) QuantizedMatMulWithBiasAndReluAttr { - return func(m optionalAttr) { - m["Toutput"] = value - } -} - -// QuantizedMatMulWithBiasAndReluTransposeA sets the optional transpose_a attribute to value. -// -// value: If true, `a` is transposed before multiplication. -// If not specified, defaults to false -func QuantizedMatMulWithBiasAndReluTransposeA(value bool) QuantizedMatMulWithBiasAndReluAttr { - return func(m optionalAttr) { - m["transpose_a"] = value - } -} - -// QuantizedMatMulWithBiasAndReluTransposeB sets the optional transpose_b attribute to value. -// -// value: If true, `b` is transposed before multiplication. -// If not specified, defaults to false -func QuantizedMatMulWithBiasAndReluTransposeB(value bool) QuantizedMatMulWithBiasAndReluAttr { - return func(m optionalAttr) { - m["transpose_b"] = value - } -} - -// QuantizedMatMulWithBiasAndReluInputQuantMode sets the optional input_quant_mode attribute to value. -// -// value: Input data quantization mode. Either MIN_FIRST(default) or SCALED. -// If not specified, defaults to "MIN_FIRST" -func QuantizedMatMulWithBiasAndReluInputQuantMode(value string) QuantizedMatMulWithBiasAndReluAttr { - return func(m optionalAttr) { - m["input_quant_mode"] = value - } -} - -// Perform a quantized matrix multiplication of `a` by the matrix `b` with bias -// add and relu fusion. -// -// The inputs must be two-dimensional matrices and 1D bias vector. And the inner -// dimension of `a` (after being transposed if `transpose_a` is non-zero) must -// match the outer dimension of `b` (after being transposed if `transposed_b` is -// non-zero). Then do broadcast add operation with bias values on the matrix -// mulplication result. The bias size must match inner dimension of `b`. Then do -// relu activation to get non-negative result. -// -// Arguments: -// a: A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. -// b: A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. -// bias: A 1D bias tensor with size matching with inner dimension of `b` (after being -// transposed if `transposed_b` is non-zero). -// min_a: The float value that the lowest quantized `a` value represents. -// max_a: The float value that the highest quantized `a` value represents. -// min_b: The float value that the lowest quantized `b` value represents. -// max_b: The float value that the highest quantized `b` value represents. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -func QuantizedMatMulWithBiasAndRelu(scope *Scope, a tf.Output, b tf.Output, bias tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulWithBiasAndReluAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedMatMulWithBiasAndRelu", - Input: []tf.Input{ - a, b, bias, min_a, max_a, min_b, max_b, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - // Creates a dataset that emits the outputs of `input_dataset` `count` times. // // Arguments: @@ -41117,6 +41094,29 @@ func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, outpu return op.Output(0) } +// Returns the item in the list with the given index. +// +// input_handle: the list +// index: the position in the list from which an element will be retrieved +// item: the element at that position +// +// +func TensorListGetItem(scope *Scope, input_handle tf.Output, index tf.Output, element_shape tf.Output, element_dtype tf.DataType) (item tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListGetItem", + Input: []tf.Input{ + input_handle, index, element_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Creates a dataset that contains `count` elements from the `input_dataset`. // // Arguments: