Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 314586235 Change-Id: I44923bbd5c38b6cc4b0aa3eee02453916f62d5a2
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
50c3196789
commit
59092da68c
|
|
@ -12571,6 +12571,151 @@ func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (conten
|
|||
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`.
|
||||
//
|
||||
// Example:
|
||||
// ```python
|
||||
// import tensorflow as tf
|
||||
// from tensorflow.python.ops import bitwise_ops
|
||||
//
|
||||
// # flip 2 (00000010) to -3 (11111101)
|
||||
// tf.assert_equal(-3, bitwise_ops.invert(2))
|
||||
//
|
||||
// dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
|
||||
// dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
|
||||
//
|
||||
// inputs = [0, 5, 3, 14]
|
||||
// for dtype in dtype_list:
|
||||
// # Because of issues with negative numbers, let's test this indirectly.
|
||||
// # 1. invert(a) and a = 0
|
||||
// # 2. invert(a) or a = invert(0)
|
||||
// input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
|
||||
// not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
|
||||
// input_tensor, bitwise_ops.invert(input_tensor)),
|
||||
// bitwise_ops.bitwise_or(
|
||||
// input_tensor, bitwise_ops.invert(input_tensor)),
|
||||
// bitwise_ops.invert(
|
||||
// tf.constant(0, dtype=dtype))]
|
||||
//
|
||||
// expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
|
||||
// tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)
|
||||
//
|
||||
// expected = tf.cast([not_0] * 4, tf.float32)
|
||||
// tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)
|
||||
//
|
||||
// # For unsigned dtypes let's also check the result directly.
|
||||
// if dtype.is_unsigned:
|
||||
// inverted = bitwise_ops.invert(input_tensor)
|
||||
// expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
|
||||
// tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
|
||||
// ```
|
||||
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)
|
||||
}
|
||||
|
||||
// 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.io.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,
|
||||
}
|
||||
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
|
||||
// interpreted 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)
|
||||
}
|
||||
|
||||
// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape.
|
||||
type ExtractJpegShapeAttr func(optionalAttr)
|
||||
|
||||
|
|
@ -15370,123 +15515,6 @@ func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_s
|
|||
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`.
|
||||
//
|
||||
// Example:
|
||||
// ```python
|
||||
// import tensorflow as tf
|
||||
// from tensorflow.python.ops import bitwise_ops
|
||||
//
|
||||
// # flip 2 (00000010) to -3 (11111101)
|
||||
// tf.assert_equal(-3, bitwise_ops.invert(2))
|
||||
//
|
||||
// dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
|
||||
// dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
|
||||
//
|
||||
// inputs = [0, 5, 3, 14]
|
||||
// for dtype in dtype_list:
|
||||
// # Because of issues with negative numbers, let's test this indirectly.
|
||||
// # 1. invert(a) and a = 0
|
||||
// # 2. invert(a) or a = invert(0)
|
||||
// input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
|
||||
// not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
|
||||
// input_tensor, bitwise_ops.invert(input_tensor)),
|
||||
// bitwise_ops.bitwise_or(
|
||||
// input_tensor, bitwise_ops.invert(input_tensor)),
|
||||
// bitwise_ops.invert(
|
||||
// tf.constant(0, dtype=dtype))]
|
||||
//
|
||||
// expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
|
||||
// tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)
|
||||
//
|
||||
// expected = tf.cast([not_0] * 4, tf.float32)
|
||||
// tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)
|
||||
//
|
||||
// # For unsigned dtypes let's also check the result directly.
|
||||
// if dtype.is_unsigned:
|
||||
// inverted = bitwise_ops.invert(input_tensor)
|
||||
// expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
|
||||
// tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
|
||||
// ```
|
||||
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)
|
||||
}
|
||||
|
||||
// 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.io.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,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// Computes the gradient for the sqrt of `x` wrt its input.
|
||||
//
|
||||
// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
|
||||
|
|
@ -20380,34 +20408,6 @@ func TensorListPopBack(scope *Scope, input_handle tf.Output, element_shape tf.Ou
|
|||
return op.Output(0), op.Output(1)
|
||||
}
|
||||
|
||||
// Adjust the saturation of one or more images.
|
||||
//
|
||||
// `images` is a tensor of at least 3 dimensions. The last dimension is
|
||||
// interpreted 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)
|
||||
}
|
||||
|
||||
// QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2.
|
||||
type QueueDequeueManyV2Attr func(optionalAttr)
|
||||
|
||||
|
|
@ -26421,72 +26421,45 @@ func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output,
|
|||
return op.Output(0)
|
||||
}
|
||||
|
||||
// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU.
|
||||
type ConfigureDistributedTPUAttr func(optionalAttr)
|
||||
|
||||
// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value.
|
||||
// Returns the truth value of x OR y element-wise.
|
||||
//
|
||||
// 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
|
||||
}
|
||||
}
|
||||
|
||||
// ConfigureDistributedTPUEnableWholeMeshCompilations sets the optional enable_whole_mesh_compilations attribute to value.
|
||||
// If not specified, defaults to false
|
||||
func ConfigureDistributedTPUEnableWholeMeshCompilations(value bool) ConfigureDistributedTPUAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["enable_whole_mesh_compilations"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// ConfigureDistributedTPUCompilationFailureClosesChips sets the optional compilation_failure_closes_chips attribute to value.
|
||||
// If not specified, defaults to true
|
||||
func ConfigureDistributedTPUCompilationFailureClosesChips(value bool) ConfigureDistributedTPUAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["compilation_failure_closes_chips"] = 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) {
|
||||
// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
|
||||
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
|
||||
func LogicalOr(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: "LogicalOr",
|
||||
Input: []tf.Input{
|
||||
x, y,
|
||||
},
|
||||
}
|
||||
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: "ConfigureDistributedTPU",
|
||||
|
||||
Attrs: attrs,
|
||||
Type: "BiasAddV1",
|
||||
Input: []tf.Input{
|
||||
value, bias,
|
||||
},
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
|
|
@ -29339,21 +29312,6 @@ func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...
|
|||
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)
|
||||
}
|
||||
|
||||
// Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
|
||||
//
|
||||
// There is no transformation in the `tf.data` Python API for creating this dataset.
|
||||
|
|
@ -34779,50 +34737,6 @@ func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf
|
|||
return op.Output(0)
|
||||
}
|
||||
|
||||
// Returns the truth value of x OR y element-wise.
|
||||
//
|
||||
// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting
|
||||
// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
|
||||
func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "LogicalOr",
|
||||
Input: []tf.Input{
|
||||
x, y,
|
||||
},
|
||||
}
|
||||
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)
|
||||
}
|
||||
|
||||
// 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
|
||||
|
|
@ -38331,6 +38245,139 @@ func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string)
|
|||
return outputs
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// Computes the sparse Cholesky decomposition of `input`.
|
||||
//
|
||||
// Computes the Sparse Cholesky decomposition of a sparse matrix, with the given
|
||||
|
|
@ -48648,6 +48695,77 @@ func RetrieveTPUEmbeddingMomentumParameters(scope *Scope, num_shards int64, shar
|
|||
return op.Output(0), op.Output(1)
|
||||
}
|
||||
|
||||
// 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
|
||||
}
|
||||
}
|
||||
|
||||
// ConfigureDistributedTPUEnableWholeMeshCompilations sets the optional enable_whole_mesh_compilations attribute to value.
|
||||
// If not specified, defaults to false
|
||||
func ConfigureDistributedTPUEnableWholeMeshCompilations(value bool) ConfigureDistributedTPUAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["enable_whole_mesh_compilations"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// ConfigureDistributedTPUCompilationFailureClosesChips sets the optional compilation_failure_closes_chips attribute to value.
|
||||
// If not specified, defaults to true
|
||||
func ConfigureDistributedTPUCompilationFailureClosesChips(value bool) ConfigureDistributedTPUAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["compilation_failure_closes_chips"] = 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)
|
||||
}
|
||||
|
||||
// Combines (nests of) input elements into a dataset of (nests of) windows.
|
||||
//
|
||||
// A "window" is a finite dataset of flat elements of size `size` (or possibly
|
||||
|
|
@ -48908,124 +49026,6 @@ func RetrieveTPUEmbeddingFTRLParameters(scope *Scope, num_shards int64, shard_id
|
|||
return op.Output(0), op.Output(1), op.Output(2)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// Returns the result of a TPU compilation.
|
||||
//
|
||||
// This operation returns the result of a TPU compilation as a serialized
|
||||
|
|
|
|||
Loading…
Reference in New Issue