Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 168494944
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
69301f3520
commit
eec4f1b3ac
@ -2466,78 +2466,6 @@ func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
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return op.Output(0)
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}
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// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
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type AllCandidateSamplerAttr func(optionalAttr)
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// AllCandidateSamplerSeed 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 AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr {
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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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// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value.
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//
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// value: An second seed to avoid seed collision.
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// If not specified, defaults to 0
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func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr {
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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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// Generates labels for candidate sampling with a learned unigram distribution.
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//
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// See explanations of candidate sampling and the data formats at
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// go/candidate-sampling.
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//
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// For each batch, this op picks a single set of sampled candidate labels.
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//
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// The advantages of sampling candidates per-batch are simplicity and the
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// possibility of efficient dense matrix multiplication. The disadvantage is that
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// the sampled candidates must be chosen independently of the context and of the
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// true labels.
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//
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// Arguments:
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// true_classes: A batch_size * num_true matrix, in which each row contains the
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// IDs of the num_true target_classes in the corresponding original label.
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// num_true: Number of true labels per context.
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// num_sampled: Number of candidates to produce.
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// unique: If unique is true, we sample with rejection, so that all sampled
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// candidates in a batch are unique. This requires some approximation to
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// estimate the post-rejection sampling probabilities.
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//
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// Returns A vector of length num_sampled, in which each element is
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// the ID of a sampled candidate.A batch_size * num_true matrix, representing
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// the number of times each candidate is expected to occur in a batch
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// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
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// candidate representing the number of times the candidate is expected
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// to occur in a batch of sampled candidates. If unique=true, then this is a
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// probability.
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func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
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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{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique}
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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: "AllCandidateSampler",
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Input: []tf.Input{
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true_classes,
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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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// FixedUnigramCandidateSamplerAttr is an optional argument to FixedUnigramCandidateSampler.
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type FixedUnigramCandidateSamplerAttr func(optionalAttr)
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@ -7004,6 +6932,194 @@ func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegS
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return op.Output(0)
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}
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// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler.
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type AllCandidateSamplerAttr func(optionalAttr)
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// AllCandidateSamplerSeed 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 AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr {
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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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// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value.
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//
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// value: An second seed to avoid seed collision.
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// If not specified, defaults to 0
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func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr {
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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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// Generates labels for candidate sampling with a learned unigram distribution.
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//
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// See explanations of candidate sampling and the data formats at
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// go/candidate-sampling.
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//
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// For each batch, this op picks a single set of sampled candidate labels.
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//
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// The advantages of sampling candidates per-batch are simplicity and the
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// possibility of efficient dense matrix multiplication. The disadvantage is that
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// the sampled candidates must be chosen independently of the context and of the
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// true labels.
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//
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// Arguments:
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// true_classes: A batch_size * num_true matrix, in which each row contains the
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// IDs of the num_true target_classes in the corresponding original label.
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// num_true: Number of true labels per context.
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// num_sampled: Number of candidates to produce.
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// unique: If unique is true, we sample with rejection, so that all sampled
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// candidates in a batch are unique. This requires some approximation to
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// estimate the post-rejection sampling probabilities.
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//
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// Returns A vector of length num_sampled, in which each element is
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// the ID of a sampled candidate.A batch_size * num_true matrix, representing
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// the number of times each candidate is expected to occur in a batch
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// of sampled candidates. If unique=true, then this is a probability.A vector of length num_sampled, for each sampled
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// candidate representing the number of times the candidate is expected
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// to occur in a batch of sampled candidates. If unique=true, then this is a
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// probability.
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func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) {
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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{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique}
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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: "AllCandidateSampler",
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Input: []tf.Input{
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true_classes,
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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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// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg.
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type DecodeAndCropJpegAttr func(optionalAttr)
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// DecodeAndCropJpegChannels sets the optional channels attribute to value.
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//
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// value: Number of color channels for the decoded image.
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// If not specified, defaults to 0
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func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["channels"] = value
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}
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}
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// DecodeAndCropJpegRatio sets the optional ratio attribute to value.
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//
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// value: Downscaling ratio.
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// If not specified, defaults to 1
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func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["ratio"] = value
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}
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}
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// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value.
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//
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// value: If true use a slower but nicer upscaling of the
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// chroma planes (yuv420/422 only).
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// If not specified, defaults to true
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func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["fancy_upscaling"] = value
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}
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}
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// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value.
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//
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// value: If true try to recover an image from truncated input.
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// If not specified, defaults to false
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func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["try_recover_truncated"] = value
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}
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}
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// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value.
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//
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// value: The minimum required fraction of lines before a truncated
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// input is accepted.
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// If not specified, defaults to 1
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func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["acceptable_fraction"] = value
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}
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}
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// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value.
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//
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// value: string specifying a hint about the algorithm used for
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// decompression. Defaults to "" which maps to a system-specific
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// default. Currently valid values are ["INTEGER_FAST",
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// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
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// jpeg library changes to a version that does not have that specific
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// option.)
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// If not specified, defaults to ""
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func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr {
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return func(m optionalAttr) {
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m["dct_method"] = value
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}
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}
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// Decode and Crop a JPEG-encoded image to a uint8 tensor.
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//
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// The attr `channels` indicates the desired number of color channels for the
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// decoded image.
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//
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// Accepted values are:
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//
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// * 0: Use the number of channels in the JPEG-encoded image.
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// * 1: output a grayscale image.
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// * 3: output an RGB image.
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//
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// If needed, the JPEG-encoded image is transformed to match the requested number
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// of color channels.
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//
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// The attr `ratio` allows downscaling the image by an integer factor during
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// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
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// downscaling the image later.
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//
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//
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// It is equivalent to a combination of decode and crop, but much faster by only
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// decoding partial jpeg image.
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//
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// Arguments:
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// contents: 0-D. The JPEG-encoded image.
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// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
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//
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// Returns 3-D with shape `[height, width, channels]`..
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func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image 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: "DecodeAndCropJpeg",
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Input: []tf.Input{
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contents, crop_window,
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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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// DecodeJpegAttr is an optional argument to DecodeJpeg.
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type DecodeJpegAttr func(optionalAttr)
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@ -7092,6 +7208,7 @@ func DecodeJpegDctMethod(value string) DecodeJpegAttr {
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// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
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// downscaling the image later.
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//
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//
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// This op also supports decoding PNGs and non-animated GIFs since the interface is
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// the same, though it is cleaner to use `tf.image.decode_image`.
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//
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