Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 304254154 Change-Id: I3d298c01658d0a2ce365f8f20e9162f71ecccb2e
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
573e194ffa
commit
3b17f5ba31
@ -11894,252 +11894,6 @@ func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Ou
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return op.Output(0)
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}
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// EigAttr is an optional argument to Eig.
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type EigAttr func(optionalAttr)
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// EigComputeV sets the optional compute_v attribute to value.
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//
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// value: If `True` then eigenvectors will be computed and returned in `v`.
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// Otherwise, only the eigenvalues will be computed.
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// If not specified, defaults to true
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func EigComputeV(value bool) EigAttr {
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return func(m optionalAttr) {
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m["compute_v"] = value
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}
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}
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// Computes the eigen decomposition of one or more square matrices.
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//
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// Computes the eigenvalues and (optionally) right eigenvectors of each inner matrix in
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// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
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// are sorted in non-decreasing order.
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//
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// ```python
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// # a is a tensor.
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// # e is a tensor of eigenvalues.
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// # v is a tensor of eigenvectors.
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// e, v = eig(a)
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// e = eig(a, compute_v=False)
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// ```
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//
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// Arguments:
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// input: `Tensor` input of shape `[N, N]`.
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//
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//
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// Returns:
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// e: Eigenvalues. Shape is `[N]`.
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// v: Eigenvectors. Shape is `[N, N]`.
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func Eig(scope *Scope, input tf.Output, Tout tf.DataType, optional ...EigAttr) (e tf.Output, v 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{}{"Tout": Tout}
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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: "Eig",
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Input: []tf.Input{
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input,
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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), op.Output(1)
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}
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// ProdAttr is an optional argument to Prod.
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type ProdAttr func(optionalAttr)
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// ProdKeepDims sets the optional keep_dims attribute to value.
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//
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// value: If true, retain reduced dimensions with length 1.
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// If not specified, defaults to false
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func ProdKeepDims(value bool) ProdAttr {
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return func(m optionalAttr) {
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m["keep_dims"] = value
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}
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}
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// Computes the product of elements across dimensions of a tensor.
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//
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// Reduces `input` along the dimensions given in `axis`. Unless
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// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
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// `axis`. If `keep_dims` is true, the reduced dimensions are
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// retained with length 1.
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//
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// Arguments:
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// input: The tensor to reduce.
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// axis: The dimensions to reduce. Must be in the range
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// `[-rank(input), rank(input))`.
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//
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// Returns The reduced tensor.
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func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (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: "Prod",
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Input: []tf.Input{
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input, axis,
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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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// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2.
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type SampleDistortedBoundingBoxV2Attr func(optionalAttr)
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// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value.
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//
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// value: If either `seed` or `seed2` are set to non-zero, the random number
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// generator is seeded by the given `seed`. Otherwise, it is seeded by a random
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// seed.
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// If not specified, defaults to 0
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func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr {
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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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// SampleDistortedBoundingBoxV2Seed2 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 SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr {
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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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// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value.
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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}
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}
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// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value.
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["area_range"] = value
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}
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}
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// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value.
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//
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// value: Number of attempts at generating a cropped region of the image
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// of the specified constraints. After `max_attempts` failures, return the entire
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// image.
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// If not specified, defaults to 100
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func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["max_attempts"] = value
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}
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}
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// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
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//
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// value: Controls behavior if no bounding boxes supplied.
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// If true, assume an implicit bounding box covering the whole input. If false,
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// raise an error.
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// If not specified, defaults to false
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func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["use_image_if_no_bounding_boxes"] = value
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}
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}
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// Generate a single randomly distorted bounding box for an image.
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//
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// Bounding box annotations are often supplied in addition to ground-truth labels
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// in image recognition or object localization tasks. A common technique for
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// training such a system is to randomly distort an image while preserving
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// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
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// localization of an object, i.e. bounding box, given an `image_size`,
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// `bounding_boxes` and a series of constraints.
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//
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// The output of this Op is a single bounding box that may be used to crop the
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// original image. The output is returned as 3 tensors: `begin`, `size` and
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// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
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// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
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// what the bounding box looks like.
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//
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// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
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// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
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// height of the underlying image.
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//
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// For example,
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//
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// ```python
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// # Generate a single distorted bounding box.
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// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
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// tf.shape(image),
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// bounding_boxes=bounding_boxes)
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//
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// # Draw the bounding box in an image summary.
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// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
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// bbox_for_draw)
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// tf.summary.image('images_with_box', image_with_box)
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//
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// # Employ the bounding box to distort the image.
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// distorted_image = tf.slice(image, begin, size)
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// ```
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//
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// Note that if no bounding box information is available, setting
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// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
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// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
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// false and no bounding boxes are supplied, an error is raised.
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//
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// Arguments:
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// image_size: 1-D, containing `[height, width, channels]`.
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// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
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// associated with the image.
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// min_object_covered: The cropped area of the image must contain at least this
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// fraction of any bounding box supplied. The value of this parameter should be
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// non-negative. In the case of 0, the cropped area does not need to overlap
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// any of the bounding boxes supplied.
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//
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// Returns:
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// begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
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// `tf.slice`.
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// size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to
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// `tf.slice`.
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// bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
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// Provide as input to `tf.image.draw_bounding_boxes`.
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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) {
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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: "SampleDistortedBoundingBoxV2",
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Input: []tf.Input{
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image_size, bounding_boxes, min_object_covered,
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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), op.Output(1), op.Output(2)
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}
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// Converts one or more images from RGB to HSV.
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//
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// Outputs a tensor of the same shape as the `images` tensor, containing the HSV
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@ -18840,6 +18594,252 @@ func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (ou
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return op.Output(0)
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}
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// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2.
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type SampleDistortedBoundingBoxV2Attr func(optionalAttr)
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// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value.
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//
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// value: If either `seed` or `seed2` are set to non-zero, the random number
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// generator is seeded by the given `seed`. Otherwise, it is seeded by a random
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// seed.
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// If not specified, defaults to 0
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func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr {
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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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// SampleDistortedBoundingBoxV2Seed2 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 SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr {
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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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// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value.
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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}
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}
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// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value.
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["area_range"] = value
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}
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}
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// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value.
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//
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// value: Number of attempts at generating a cropped region of the image
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// of the specified constraints. After `max_attempts` failures, return the entire
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// image.
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// If not specified, defaults to 100
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func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["max_attempts"] = value
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}
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}
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// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value.
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//
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// value: Controls behavior if no bounding boxes supplied.
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// If true, assume an implicit bounding box covering the whole input. If false,
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// raise an error.
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// If not specified, defaults to false
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func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["use_image_if_no_bounding_boxes"] = value
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}
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}
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// Generate a single randomly distorted bounding box for an image.
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//
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// Bounding box annotations are often supplied in addition to ground-truth labels
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// in image recognition or object localization tasks. A common technique for
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// training such a system is to randomly distort an image while preserving
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// its content, i.e. *data augmentation*. This Op outputs a randomly distorted
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// localization of an object, i.e. bounding box, given an `image_size`,
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// `bounding_boxes` and a series of constraints.
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//
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// The output of this Op is a single bounding box that may be used to crop the
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// original image. The output is returned as 3 tensors: `begin`, `size` and
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// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
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// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
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// what the bounding box looks like.
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//
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// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
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// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
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// height of the underlying image.
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//
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// For example,
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//
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// ```python
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// # Generate a single distorted bounding box.
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// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
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// tf.shape(image),
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// bounding_boxes=bounding_boxes)
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//
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// # Draw the bounding box in an image summary.
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// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
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// bbox_for_draw)
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// tf.summary.image('images_with_box', image_with_box)
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//
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// # Employ the bounding box to distort the image.
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// distorted_image = tf.slice(image, begin, size)
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// ```
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//
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// Note that if no bounding box information is available, setting
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// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit
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// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
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// false and no bounding boxes are supplied, an error is raised.
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//
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// Arguments:
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// image_size: 1-D, containing `[height, width, channels]`.
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// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
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// associated with the image.
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// min_object_covered: The cropped area of the image must contain at least this
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// fraction of any bounding box supplied. The value of this parameter should be
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// non-negative. In the case of 0, the cropped area does not need to overlap
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// any of the bounding boxes supplied.
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//
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// Returns:
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// begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
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// `tf.slice`.
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// size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to
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// `tf.slice`.
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// bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
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// Provide as input to `tf.image.draw_bounding_boxes`.
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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) {
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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: "SampleDistortedBoundingBoxV2",
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Input: []tf.Input{
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image_size, bounding_boxes, min_object_covered,
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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), op.Output(1), op.Output(2)
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}
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// EigAttr is an optional argument to Eig.
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type EigAttr func(optionalAttr)
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// EigComputeV sets the optional compute_v attribute to value.
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//
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// value: If `True` then eigenvectors will be computed and returned in `v`.
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// Otherwise, only the eigenvalues will be computed.
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// If not specified, defaults to true
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func EigComputeV(value bool) EigAttr {
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return func(m optionalAttr) {
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m["compute_v"] = value
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}
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}
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// Computes the eigen decomposition of one or more square matrices.
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//
|
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// Computes the eigenvalues and (optionally) right eigenvectors of each inner matrix in
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// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues
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// are sorted in non-decreasing order.
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//
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// ```python
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// # a is a tensor.
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// # e is a tensor of eigenvalues.
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||||
// # v is a tensor of eigenvectors.
|
||||
// e, v = eig(a)
|
||||
// e = eig(a, compute_v=False)
|
||||
// ```
|
||||
//
|
||||
// Arguments:
|
||||
// input: `Tensor` input of shape `[N, N]`.
|
||||
//
|
||||
//
|
||||
// Returns:
|
||||
// e: Eigenvalues. Shape is `[N]`.
|
||||
// v: Eigenvectors. Shape is `[N, N]`.
|
||||
func Eig(scope *Scope, input tf.Output, Tout tf.DataType, optional ...EigAttr) (e tf.Output, v tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"Tout": Tout}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "Eig",
|
||||
Input: []tf.Input{
|
||||
input,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0), op.Output(1)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// SumAttr is an optional argument to Sum.
|
||||
type SumAttr func(optionalAttr)
|
||||
|
||||
@ -23843,29 +23843,6 @@ func FIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...FIFOQu
|
||||
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)
|
||||
}
|
||||
|
||||
// Quantized Batch normalization.
|
||||
//
|
||||
// This op is deprecated and will be removed in the future. Prefer
|
||||
@ -30591,6 +30568,29 @@ func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAtt
|
||||
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)
|
||||
}
|
||||
|
||||
// UpperBoundAttr is an optional argument to UpperBound.
|
||||
type UpperBoundAttr func(optionalAttr)
|
||||
|
||||
@ -40528,6 +40528,94 @@ func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// EnqueueTPUEmbeddingRaggedTensorBatchAttr is an optional argument to EnqueueTPUEmbeddingRaggedTensorBatch.
|
||||
type EnqueueTPUEmbeddingRaggedTensorBatchAttr func(optionalAttr)
|
||||
|
||||
// EnqueueTPUEmbeddingRaggedTensorBatchDeviceOrdinal 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 EnqueueTPUEmbeddingRaggedTensorBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingRaggedTensorBatchAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["device_ordinal"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// EnqueueTPUEmbeddingRaggedTensorBatchCombiners 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 EnqueueTPUEmbeddingRaggedTensorBatchCombiners(value []string) EnqueueTPUEmbeddingRaggedTensorBatchAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["combiners"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// EnqueueTPUEmbeddingRaggedTensorBatchMaxSequenceLengths sets the optional max_sequence_lengths attribute to value.
|
||||
// If not specified, defaults to {}
|
||||
func EnqueueTPUEmbeddingRaggedTensorBatchMaxSequenceLengths(value []int64) EnqueueTPUEmbeddingRaggedTensorBatchAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["max_sequence_lengths"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Eases the porting of code that uses tf.nn.embedding_lookup().
|
||||
//
|
||||
// sample_splits[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 two of the input lists,
|
||||
// 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_splits: A list of rank 1 Tensors specifying the break points for splitting
|
||||
// embedding_indices and aggregation_weights into rows.
|
||||
// It corresponds to ids.row_splits in embedding_lookup(), when ids is a
|
||||
// RaggedTensor.
|
||||
// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables.
|
||||
// It corresponds to ids.values in embedding_lookup(), when ids is a RaggedTensor.
|
||||
// aggregation_weights: A list of rank 1 Tensors containing per training example
|
||||
// aggregation weights. It corresponds to the values field of a RaggedTensor
|
||||
// with the same row_splits as ids in embedding_lookup(), when ids is a
|
||||
// RaggedTensor.
|
||||
// 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 EnqueueTPUEmbeddingRaggedTensorBatch(scope *Scope, sample_splits []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, table_ids []int64, optional ...EnqueueTPUEmbeddingRaggedTensorBatchAttr) (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: "EnqueueTPUEmbeddingRaggedTensorBatch",
|
||||
Input: []tf.Input{
|
||||
tf.OutputList(sample_splits), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
return scope.AddOperation(opspec)
|
||||
}
|
||||
|
||||
// ResourceApplyKerasMomentumAttr is an optional argument to ResourceApplyKerasMomentum.
|
||||
type ResourceApplyKerasMomentumAttr func(optionalAttr)
|
||||
|
||||
@ -43462,131 +43550,6 @@ func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Outpu
|
||||
return scope.AddOperation(opspec)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// 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
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig sets the optional config attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["config"] = 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)
|
||||
}
|
||||
|
||||
// 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 {unknown_rank:true}
|
||||
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)
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to LoadTPUEmbeddingMDLAdagradLightParameters.
|
||||
type LoadTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr)
|
||||
|
||||
@ -46145,6 +46108,131 @@ func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Out
|
||||
return scope.AddOperation(opspec)
|
||||
}
|
||||
|
||||
// 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 {unknown_rank:true}
|
||||
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)
|
||||
}
|
||||
|
||||
// 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)
|
||||
}
|
||||
|
||||
// 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
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig sets the optional config attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["config"] = 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)
|
||||
}
|
||||
|
||||
// EqualAttr is an optional argument to Equal.
|
||||
type EqualAttr func(optionalAttr)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user