Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 341512329 Change-Id: I75cd65e79ffcc95f057d583d892843046c12d2b6
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A. Unique TensorFlower
TensorFlower Gardener
parent
99118cceb8
commit
d99d8b10dc
@ -14570,6 +14570,21 @@ func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_
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return op.Output(0)
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}
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// Generate a glob pattern matching all sharded file names.
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func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "ShardedFilespec",
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Input: []tf.Input{
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basename, num_shards,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Saves the input tensors to disk.
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//
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// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
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@ -22697,6 +22712,69 @@ func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) {
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return op.Output(0)
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}
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// RiscConvAttr is an optional argument to RiscConv.
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type RiscConvAttr func(optionalAttr)
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// RiscConvDataFormat sets the optional data_format attribute to value.
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//
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// value: Specify the data format of the input and output data. With the
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// default format "NHWC", the data is stored in the order of:
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// [batch, height, width, channels].
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// Alternatively, the format could be "NCHW", the data storage order of:
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// [batch, channels, height, width].
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// If not specified, defaults to "NHWC"
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func RiscConvDataFormat(value string) RiscConvAttr {
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return func(m optionalAttr) {
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m["data_format"] = value
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}
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}
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// RiscConvDilations sets the optional dilations attribute to value.
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//
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// value: 1-D tensor of length 4. The dilation factor for each dimension of
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// `input`. If set to k > 1, there will be k-1 skipped cells between each
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to <i:1 i:1 i:1 i:1 >
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func RiscConvDilations(value []int64) RiscConvAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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}
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}
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// Computes a 2-D convolution given 4-D `input` and `filter` tensors.
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//
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// Arguments:
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// input: A 4-D tensor. The dimension order is interpreted according to the value
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// of `data_format`, see below for details.
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// filter: A 4-D tensor of shape
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// `[filter_height, filter_width, in_channels, out_channels]`
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// strides: 1-D tensor of length 4. The stride of the sliding window for each
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// dimension of `input`. The dimension order is determined by the value of
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// `data_format`, see below for details.
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//
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// Returns A 4-D tensor. The dimension order is determined by the value of
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// `data_format`, see below for details.
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func RiscConv(scope *Scope, input tf.Output, filter tf.Output, strides []int64, optional ...RiscConvAttr) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"strides": strides}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "RiscConv",
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Input: []tf.Input{
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input, filter,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Computes hyperbolic tangent of `x` element-wise.
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//
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// Given an input tensor, this function computes hyperbolic tangent of every
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@ -29742,6 +29820,67 @@ func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf
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return op.Output(0)
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}
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// RandomShuffleAttr is an optional argument to RandomShuffle.
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type RandomShuffleAttr func(optionalAttr)
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// RandomShuffleSeed sets the optional seed attribute to value.
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//
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// value: If either `seed` or `seed2` are set to be non-zero, the random number
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// generator is seeded by the given seed. Otherwise, it is seeded by a
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// random seed.
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// If not specified, defaults to 0
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func RandomShuffleSeed(value int64) RandomShuffleAttr {
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return func(m optionalAttr) {
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m["seed"] = value
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}
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}
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// RandomShuffleSeed2 sets the optional seed2 attribute to value.
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//
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// value: A second seed to avoid seed collision.
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// If not specified, defaults to 0
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func RandomShuffleSeed2(value int64) RandomShuffleAttr {
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return func(m optionalAttr) {
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m["seed2"] = value
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}
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}
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// Randomly shuffles a tensor along its first dimension.
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//
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// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
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// to one and only one `output[i]`. For example, a mapping that might occur for a
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// 3x2 tensor is:
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//
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// ```
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// [[1, 2], [[5, 6],
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// [3, 4], ==> [1, 2],
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// [5, 6]] [3, 4]]
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// ```
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//
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// Arguments:
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// value: The tensor to be shuffled.
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//
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// Returns A tensor of same shape and type as `value`, shuffled along its first
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// dimension.
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func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "RandomShuffle",
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Input: []tf.Input{
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value,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
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//
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// There is no transformation in the `tf.data` Python API for creating this dataset.
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@ -36066,21 +36205,6 @@ func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes [
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// Generate a glob pattern matching all sharded file names.
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func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "ShardedFilespec",
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Input: []tf.Input{
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basename, num_shards,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Writes a scalar summary.
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//
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// Writes scalar `value` at `step` with `tag` using summary `writer`.
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@ -37137,67 +37261,6 @@ func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// RandomShuffleAttr is an optional argument to RandomShuffle.
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type RandomShuffleAttr func(optionalAttr)
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// RandomShuffleSeed sets the optional seed attribute to value.
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//
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// value: If either `seed` or `seed2` are set to be non-zero, the random number
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// generator is seeded by the given seed. Otherwise, it is seeded by a
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// random seed.
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// If not specified, defaults to 0
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func RandomShuffleSeed(value int64) RandomShuffleAttr {
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return func(m optionalAttr) {
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m["seed"] = value
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}
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}
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// RandomShuffleSeed2 sets the optional seed2 attribute to value.
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//
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// value: A second seed to avoid seed collision.
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// If not specified, defaults to 0
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func RandomShuffleSeed2(value int64) RandomShuffleAttr {
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return func(m optionalAttr) {
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m["seed2"] = value
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}
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}
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// Randomly shuffles a tensor along its first dimension.
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//
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// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
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// to one and only one `output[i]`. For example, a mapping that might occur for a
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// 3x2 tensor is:
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//
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// ```
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// [[1, 2], [[5, 6],
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// [3, 4], ==> [1, 2],
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// [5, 6]] [3, 4]]
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// ```
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//
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// Arguments:
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// value: The tensor to be shuffled.
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//
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// Returns A tensor of same shape and type as `value`, shuffled along its first
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// dimension.
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func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "RandomShuffle",
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Input: []tf.Input{
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value,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Selects elements from `x` or `y`, depending on `condition`.
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//
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// The `x`, and `y` tensors must all have the same shape, and the
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