Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 339911119 Change-Id: Iddf765ff3e58ea67c0cdf3b1ffe3065a92e8dcfc
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
1c47355978
commit
baf2bfa96f
@ -29727,6 +29727,21 @@ func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...Ra
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return op.Output(0)
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}
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// Computes the derivative of a Gamma random sample w.r.t. `alpha`.
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func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output 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: "RandomGammaGrad",
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Input: []tf.Input{
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alpha, sample,
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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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// 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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@ -29917,6 +29932,364 @@ func ParameterizedTruncatedNormal(scope *Scope, shape tf.Output, means tf.Output
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return op.Output(0)
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}
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// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool.
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type FractionalMaxPoolAttr func(optionalAttr)
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// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value.
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//
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// value: When set to True, generates the pooling sequence in a
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// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
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// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
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// difference between pseudorandom and random.
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// If not specified, defaults to false
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func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr {
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return func(m optionalAttr) {
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m["pseudo_random"] = value
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}
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}
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// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value.
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//
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// value: When set to True, it means when pooling, the values at the boundary
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// of adjacent pooling cells are used by both cells. For example:
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//
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// `index 0 1 2 3 4`
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//
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// `value 20 5 16 3 7`
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//
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// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
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// The result would be [20, 16] for fractional max pooling.
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// If not specified, defaults to false
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func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr {
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return func(m optionalAttr) {
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m["overlapping"] = value
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}
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}
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// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value.
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//
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// value: When set to True, a fixed pooling region will be used when
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// iterating over a FractionalMaxPool node in the computation graph. Mainly used
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// in unit test to make FractionalMaxPool deterministic.
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// If not specified, defaults to false
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func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr {
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return func(m optionalAttr) {
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m["deterministic"] = value
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}
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}
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// FractionalMaxPoolSeed 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 FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr {
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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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// FractionalMaxPoolSeed2 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 FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr {
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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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// Performs fractional max pooling on the input.
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//
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// Fractional max pooling is slightly different than regular max pooling. In
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// regular max pooling, you downsize an input set by taking the maximum value of
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// smaller N x N subsections of the set (often 2x2), and try to reduce the set by
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// a factor of N, where N is an integer. Fractional max pooling, as you might
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// expect from the word "fractional", means that the overall reduction ratio N
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// does not have to be an integer.
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//
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// The sizes of the pooling regions are generated randomly but are fairly uniform.
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// For example, let's look at the height dimension, and the constraints on the
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// list of rows that will be pool boundaries.
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//
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// First we define the following:
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//
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// 1. input_row_length : the number of rows from the input set
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// 2. output_row_length : which will be smaller than the input
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// 3. alpha = input_row_length / output_row_length : our reduction ratio
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// 4. K = floor(alpha)
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// 5. row_pooling_sequence : this is the result list of pool boundary rows
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//
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// Then, row_pooling_sequence should satisfy:
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//
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// 1. a[0] = 0 : the first value of the sequence is 0
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// 2. a[end] = input_row_length : the last value of the sequence is the size
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// 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
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// 4. length(row_pooling_sequence) = output_row_length+1
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//
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// For more details on fractional max pooling, see this paper:
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// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071)
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//
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// Arguments:
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// value: 4-D with shape `[batch, height, width, channels]`.
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// pooling_ratio: Pooling ratio for each dimension of `value`, currently only
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// supports row and col dimension and should be >= 1.0. For example, a valid
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// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
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// must be 1.0 because we don't allow pooling on batch and channels
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// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
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// respectively.
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//
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// Returns:
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// output: output tensor after fractional max pooling.
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// row_pooling_sequence: row pooling sequence, needed to calculate gradient.
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// col_pooling_sequence: column pooling sequence, needed to calculate gradient.
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func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
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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{}{"pooling_ratio": pooling_ratio}
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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: "FractionalMaxPool",
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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), op.Output(1), op.Output(2)
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}
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// Computes the reciprocal of x element-wise.
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//
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// I.e., \\(y = 1 / x\\).
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func Reciprocal(scope *Scope, x tf.Output) (y 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: "Reciprocal",
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Input: []tf.Input{
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x,
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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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// LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdagradParametersGradAccumDebug.
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type LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr)
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// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value.
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// If not specified, defaults to -1
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func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
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return func(m optionalAttr) {
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m["table_id"] = value
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}
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}
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// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value.
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// If not specified, defaults to ""
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func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
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return func(m optionalAttr) {
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m["table_name"] = value
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}
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}
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// LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig sets the optional config attribute to value.
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// If not specified, defaults to ""
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func LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
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return func(m optionalAttr) {
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m["config"] = value
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}
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}
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// Load Adagrad embedding parameters with debug support.
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//
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// An op that loads optimization parameters into HBM for embedding. Must be
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// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
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// embedding table configuration. For example, this op is used to install
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// parameters that are loaded from a checkpoint before a training loop is
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// executed.
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//
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// Arguments:
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// parameters: Value of parameters used in the Adagrad optimization algorithm.
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// accumulators: Value of accumulators used in the Adagrad optimization algorithm.
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// gradient_accumulators: Value of gradient_accumulators used in the Adagrad optimization algorithm.
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//
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//
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//
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// Returns the created operation.
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func LoadTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr) (o *tf.Operation) {
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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_shards": num_shards, "shard_id": shard_id}
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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: "LoadTPUEmbeddingAdagradParametersGradAccumDebug",
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Input: []tf.Input{
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parameters, accumulators, gradient_accumulators,
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},
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Attrs: attrs,
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}
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return scope.AddOperation(opspec)
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}
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// MapPeekAttr is an optional argument to MapPeek.
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type MapPeekAttr func(optionalAttr)
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// MapPeekCapacity sets the optional capacity attribute to value.
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// If not specified, defaults to 0
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//
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// REQUIRES: value >= 0
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func MapPeekCapacity(value int64) MapPeekAttr {
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return func(m optionalAttr) {
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m["capacity"] = value
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}
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}
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// MapPeekMemoryLimit sets the optional memory_limit attribute to value.
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// If not specified, defaults to 0
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//
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// REQUIRES: value >= 0
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func MapPeekMemoryLimit(value int64) MapPeekAttr {
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return func(m optionalAttr) {
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m["memory_limit"] = value
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}
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}
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// MapPeekContainer sets the optional container attribute to value.
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// If not specified, defaults to ""
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func MapPeekContainer(value string) MapPeekAttr {
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return func(m optionalAttr) {
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m["container"] = value
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}
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}
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// MapPeekSharedName sets the optional shared_name attribute to value.
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// If not specified, defaults to ""
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func MapPeekSharedName(value string) MapPeekAttr {
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return func(m optionalAttr) {
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m["shared_name"] = value
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}
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}
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// Op peeks at the values at the specified key. If the
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//
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// underlying container does not contain this key
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// this op will block until it does.
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func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []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{}{"dtypes": dtypes}
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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: "MapPeek",
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Input: []tf.Input{
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key, indices,
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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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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
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scope.UpdateErr("MapPeek", err)
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return
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}
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return values
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}
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// RetrieveTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingCenteredRMSPropParameters.
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type RetrieveTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr)
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// RetrieveTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value.
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// If not specified, defaults to -1
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func RetrieveTPUEmbeddingCenteredRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
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return func(m optionalAttr) {
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m["table_id"] = value
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}
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}
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// RetrieveTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value.
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// If not specified, defaults to ""
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func RetrieveTPUEmbeddingCenteredRMSPropParametersTableName(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
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return func(m optionalAttr) {
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m["table_name"] = value
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}
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}
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// RetrieveTPUEmbeddingCenteredRMSPropParametersConfig sets the optional config attribute to value.
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// If not specified, defaults to ""
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func RetrieveTPUEmbeddingCenteredRMSPropParametersConfig(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
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return func(m optionalAttr) {
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m["config"] = value
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}
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}
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// Retrieve centered RMSProp embedding parameters.
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//
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// An op that retrieves optimization parameters from embedding to host
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// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
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// the correct embedding table configuration. For example, this op is
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// used to retrieve updated parameters before saving a checkpoint.
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//
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// Returns:
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// parameters: Parameter parameters updated by the centered RMSProp optimization algorithm.
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// ms: Parameter ms updated by the centered RMSProp optimization algorithm.
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// mom: Parameter mom updated by the centered RMSProp optimization algorithm.
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// mg: Parameter mg updated by the centered RMSProp optimization algorithm.
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func RetrieveTPUEmbeddingCenteredRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingCenteredRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, mg 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_shards": num_shards, "shard_id": shard_id}
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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: "RetrieveTPUEmbeddingCenteredRMSPropParameters",
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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), op.Output(3)
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}
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// Returns x + y element-wise.
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//
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// *NOTE*: `RiscAdd` does not supports broadcasting.
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//
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// Given two input tensors, the `tf.risc_add` operation computes the sum for every element in the tensor.
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//
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// Both input and output have a range `(-inf, inf)`.
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//
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func RiscAdd(scope *Scope, x tf.Output, y tf.Output) (z 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: "RiscAdd",
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Input: []tf.Input{
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x, y,
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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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// QuantizedMatMulAttr is an optional argument to QuantizedMatMul.
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type QuantizedMatMulAttr func(optionalAttr)
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@ -38780,21 +39153,6 @@ func RaggedTensorToTensor(scope *Scope, shape tf.Output, values tf.Output, defau
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return op.Output(0)
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}
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// Computes the derivative of a Gamma random sample w.r.t. `alpha`.
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func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output 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: "RandomGammaGrad",
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Input: []tf.Input{
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alpha, sample,
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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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// LRNGradAttr is an optional argument to LRNGrad.
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type LRNGradAttr func(optionalAttr)
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@ -41410,215 +41768,6 @@ func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Outpu
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return scope.AddOperation(opspec)
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}
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// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool.
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type FractionalMaxPoolAttr func(optionalAttr)
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// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value.
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//
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// value: When set to True, generates the pooling sequence in a
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// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
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// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
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// difference between pseudorandom and random.
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// If not specified, defaults to false
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func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr {
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return func(m optionalAttr) {
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m["pseudo_random"] = value
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}
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}
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// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value.
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//
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// value: When set to True, it means when pooling, the values at the boundary
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// of adjacent pooling cells are used by both cells. For example:
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//
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// `index 0 1 2 3 4`
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//
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// `value 20 5 16 3 7`
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//
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// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
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// The result would be [20, 16] for fractional max pooling.
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// If not specified, defaults to false
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func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr {
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return func(m optionalAttr) {
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m["overlapping"] = value
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}
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}
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|
||||
// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value.
|
||||
//
|
||||
// value: When set to True, a fixed pooling region will be used when
|
||||
// iterating over a FractionalMaxPool node in the computation graph. Mainly used
|
||||
// in unit test to make FractionalMaxPool deterministic.
|
||||
// If not specified, defaults to false
|
||||
func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["deterministic"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// FractionalMaxPoolSeed sets the optional seed attribute to value.
|
||||
//
|
||||
// value: If either seed or seed2 are set to be non-zero, the random number
|
||||
// generator is seeded by the given seed. Otherwise, it is seeded by a
|
||||
// random seed.
|
||||
// If not specified, defaults to 0
|
||||
func FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["seed"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// FractionalMaxPoolSeed2 sets the optional seed2 attribute to value.
|
||||
//
|
||||
// value: An second seed to avoid seed collision.
|
||||
// If not specified, defaults to 0
|
||||
func FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["seed2"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Performs fractional max pooling on the input.
|
||||
//
|
||||
// Fractional max pooling is slightly different than regular max pooling. In
|
||||
// regular max pooling, you downsize an input set by taking the maximum value of
|
||||
// smaller N x N subsections of the set (often 2x2), and try to reduce the set by
|
||||
// a factor of N, where N is an integer. Fractional max pooling, as you might
|
||||
// expect from the word "fractional", means that the overall reduction ratio N
|
||||
// does not have to be an integer.
|
||||
//
|
||||
// The sizes of the pooling regions are generated randomly but are fairly uniform.
|
||||
// For example, let's look at the height dimension, and the constraints on the
|
||||
// list of rows that will be pool boundaries.
|
||||
//
|
||||
// First we define the following:
|
||||
//
|
||||
// 1. input_row_length : the number of rows from the input set
|
||||
// 2. output_row_length : which will be smaller than the input
|
||||
// 3. alpha = input_row_length / output_row_length : our reduction ratio
|
||||
// 4. K = floor(alpha)
|
||||
// 5. row_pooling_sequence : this is the result list of pool boundary rows
|
||||
//
|
||||
// Then, row_pooling_sequence should satisfy:
|
||||
//
|
||||
// 1. a[0] = 0 : the first value of the sequence is 0
|
||||
// 2. a[end] = input_row_length : the last value of the sequence is the size
|
||||
// 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
|
||||
// 4. length(row_pooling_sequence) = output_row_length+1
|
||||
//
|
||||
// For more details on fractional max pooling, see this paper:
|
||||
// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071)
|
||||
//
|
||||
// Arguments:
|
||||
// value: 4-D with shape `[batch, height, width, channels]`.
|
||||
// pooling_ratio: Pooling ratio for each dimension of `value`, currently only
|
||||
// supports row and col dimension and should be >= 1.0. For example, a valid
|
||||
// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
|
||||
// must be 1.0 because we don't allow pooling on batch and channels
|
||||
// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
|
||||
// respectively.
|
||||
//
|
||||
// Returns:
|
||||
// output: output tensor after fractional max pooling.
|
||||
// row_pooling_sequence: row pooling sequence, needed to calculate gradient.
|
||||
// col_pooling_sequence: column pooling sequence, needed to calculate gradient.
|
||||
func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"pooling_ratio": pooling_ratio}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "FractionalMaxPool",
|
||||
Input: []tf.Input{
|
||||
value,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0), op.Output(1), op.Output(2)
|
||||
}
|
||||
|
||||
// Computes the reciprocal of x element-wise.
|
||||
//
|
||||
// I.e., \\(y = 1 / x\\).
|
||||
func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "Reciprocal",
|
||||
Input: []tf.Input{
|
||||
x,
|
||||
},
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdagradParametersGradAccumDebug.
|
||||
type LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr)
|
||||
|
||||
// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value.
|
||||
// If not specified, defaults to -1
|
||||
func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["table_id"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["table_name"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig sets the optional config attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["config"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Load Adagrad embedding parameters with debug support.
|
||||
//
|
||||
// An op that loads optimization parameters into HBM for embedding. Must be
|
||||
// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
|
||||
// embedding table configuration. For example, this op is used to install
|
||||
// parameters that are loaded from a checkpoint before a training loop is
|
||||
// executed.
|
||||
//
|
||||
// Arguments:
|
||||
// parameters: Value of parameters used in the Adagrad optimization algorithm.
|
||||
// accumulators: Value of accumulators used in the Adagrad optimization algorithm.
|
||||
// gradient_accumulators: Value of gradient_accumulators used in the Adagrad optimization algorithm.
|
||||
//
|
||||
//
|
||||
//
|
||||
// Returns the created operation.
|
||||
func LoadTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr) (o *tf.Operation) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "LoadTPUEmbeddingAdagradParametersGradAccumDebug",
|
||||
Input: []tf.Input{
|
||||
parameters, accumulators, gradient_accumulators,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
return scope.AddOperation(opspec)
|
||||
}
|
||||
|
||||
// Strip leading and trailing whitespaces from the Tensor.
|
||||
//
|
||||
// Arguments:
|
||||
@ -45714,133 +45863,6 @@ func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_ke
|
||||
return sparse_indices, sparse_values, sparse_shapes, dense_values
|
||||
}
|
||||
|
||||
// MapPeekAttr is an optional argument to MapPeek.
|
||||
type MapPeekAttr func(optionalAttr)
|
||||
|
||||
// MapPeekCapacity sets the optional capacity attribute to value.
|
||||
// If not specified, defaults to 0
|
||||
//
|
||||
// REQUIRES: value >= 0
|
||||
func MapPeekCapacity(value int64) MapPeekAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["capacity"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// MapPeekMemoryLimit sets the optional memory_limit attribute to value.
|
||||
// If not specified, defaults to 0
|
||||
//
|
||||
// REQUIRES: value >= 0
|
||||
func MapPeekMemoryLimit(value int64) MapPeekAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["memory_limit"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// MapPeekContainer sets the optional container attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func MapPeekContainer(value string) MapPeekAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["container"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// MapPeekSharedName sets the optional shared_name attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func MapPeekSharedName(value string) MapPeekAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["shared_name"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Op peeks at the values at the specified key. If the
|
||||
//
|
||||
// underlying container does not contain this key
|
||||
// this op will block until it does.
|
||||
func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"dtypes": dtypes}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "MapPeek",
|
||||
Input: []tf.Input{
|
||||
key, indices,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
var idx int
|
||||
var err error
|
||||
if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
|
||||
scope.UpdateErr("MapPeek", err)
|
||||
return
|
||||
}
|
||||
return values
|
||||
}
|
||||
|
||||
// RetrieveTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingCenteredRMSPropParameters.
|
||||
type RetrieveTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr)
|
||||
|
||||
// RetrieveTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value.
|
||||
// If not specified, defaults to -1
|
||||
func RetrieveTPUEmbeddingCenteredRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["table_id"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// RetrieveTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func RetrieveTPUEmbeddingCenteredRMSPropParametersTableName(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["table_name"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// RetrieveTPUEmbeddingCenteredRMSPropParametersConfig sets the optional config attribute to value.
|
||||
// If not specified, defaults to ""
|
||||
func RetrieveTPUEmbeddingCenteredRMSPropParametersConfig(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["config"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Retrieve centered RMSProp embedding parameters.
|
||||
//
|
||||
// An op that retrieves optimization parameters from embedding to host
|
||||
// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
|
||||
// the correct embedding table configuration. For example, this op is
|
||||
// used to retrieve updated parameters before saving a checkpoint.
|
||||
//
|
||||
// Returns:
|
||||
// parameters: Parameter parameters updated by the centered RMSProp optimization algorithm.
|
||||
// ms: Parameter ms updated by the centered RMSProp optimization algorithm.
|
||||
// mom: Parameter mom updated by the centered RMSProp optimization algorithm.
|
||||
// mg: Parameter mg updated by the centered RMSProp optimization algorithm.
|
||||
func RetrieveTPUEmbeddingCenteredRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingCenteredRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "RetrieveTPUEmbeddingCenteredRMSPropParameters",
|
||||
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
|
||||
}
|
||||
|
||||
// Records the latency of producing `input_dataset` elements in a StatsAggregator.
|
||||
func LatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user