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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 294979287
Change-Id: I8eb4d95787a9e6b5a89373d3018d254ed39eebc7
This commit is contained in:
A. Unique TensorFlower 2020-02-13 12:53:18 -08:00 committed by TensorFlower Gardener
parent 622e23a9ea
commit d8738dc988
1 changed files with 125 additions and 34 deletions
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159
tensorflow/go/op/wrappers.go
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@ -2376,40 +2376,6 @@ func MatrixSetDiagV2(scope *Scope, input tf.Output, diagonal tf.Output, k tf.Out
return op.Output(0)
}
// Returns a batched matrix tensor with new batched diagonal values.
//
// Given `input` and `diagonal`, this operation returns a tensor with the
// same shape and values as `input`, except for the main diagonal of the
// innermost matrices. These will be overwritten by the values in `diagonal`.
//
// The output is computed as follows:
//
// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a
// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
//
// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
//
// Arguments:
// input: Rank `k+1`, where `k >= 1`.
// diagonal: Rank `k`, where `k >= 1`.
//
// Returns Rank `k+1`, with `output.shape = input.shape`.
func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
Type: "MatrixSetDiag",
Input: []tf.Input{
input, diagonal,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// Returns a diagonal tensor with a given diagonal values.
//
// Given a `diagonal`, this operation returns a tensor with the `diagonal` and
@ -10423,6 +10389,131 @@ func ExperimentalParseExampleDataset(scope *Scope, input_dataset tf.Output, num_
return op.Output(0)
}
// Returns a batched matrix tensor with new batched diagonal values.
//
// Given `input` and `diagonal`, this operation returns a tensor with the
// same shape and values as `input`, except for the main diagonal of the
// innermost matrices. These will be overwritten by the values in `diagonal`.
//
// The output is computed as follows:
//
// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a
// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
//
// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
//
// Arguments:
// input: Rank `k+1`, where `k >= 1`.
// diagonal: Rank `k`, where `k >= 1`.
//
// Returns Rank `k+1`, with `output.shape = input.shape`.
func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) {
if scope.Err() != nil {
return
}
opspec := tf.OpSpec{
Type: "MatrixSetDiag",
Input: []tf.Input{
input, diagonal,
},
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// ParseExampleDatasetV2Attr is an optional argument to ParseExampleDatasetV2.
type ParseExampleDatasetV2Attr func(optionalAttr)
// ParseExampleDatasetV2Deterministic sets the optional deterministic attribute to value.
//
// value: A string indicating the op-level determinism to use. Deterministic controls
// whether the dataset is allowed to return elements out of order if the next
// element to be returned isn't available, but a later element is. Options are
// "true", "false", and "default". "default" indicates that determinism should be
// decided by the `experimental_deterministic` parameter of `tf.data.Options`.
// If not specified, defaults to "default"
func ParseExampleDatasetV2Deterministic(value string) ParseExampleDatasetV2Attr {
return func(m optionalAttr) {
m["deterministic"] = value
}
}
// ParseExampleDatasetV2RaggedKeys sets the optional ragged_keys attribute to value.
// If not specified, defaults to {}
//
// REQUIRES: len(value) >= 0
func ParseExampleDatasetV2RaggedKeys(value []string) ParseExampleDatasetV2Attr {
return func(m optionalAttr) {
m["ragged_keys"] = value
}
}
// ParseExampleDatasetV2RaggedValueTypes sets the optional ragged_value_types attribute to value.
// If not specified, defaults to {}
//
// REQUIRES: len(value) >= 0
func ParseExampleDatasetV2RaggedValueTypes(value []tf.DataType) ParseExampleDatasetV2Attr {
return func(m optionalAttr) {
m["ragged_value_types"] = value
}
}
// ParseExampleDatasetV2RaggedSplitTypes sets the optional ragged_split_types attribute to value.
// If not specified, defaults to {}
//
// REQUIRES: len(value) >= 0
func ParseExampleDatasetV2RaggedSplitTypes(value []tf.DataType) ParseExampleDatasetV2Attr {
return func(m optionalAttr) {
m["ragged_split_types"] = value
}
}
// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
//
// Arguments:
//
//
// dense_defaults: A dict mapping string keys to `Tensor`s.
// The keys of the dict must match the dense_keys of the feature.
// sparse_keys: A list of string keys in the examples features.
// The results for these keys will be returned as `SparseTensor` objects.
// dense_keys: A list of Ndense string Tensors (scalars).
// The keys expected in the Examples features associated with dense values.
// sparse_types: A list of `DTypes` of the same length as `sparse_keys`.
// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),
// and `tf.string` (`BytesList`) are supported.
// dense_shapes: List of tuples with the same length as `dense_keys`.
// The shape of the data for each dense feature referenced by `dense_keys`.
// Required for any input tensors identified by `dense_keys`. Must be
// either fully defined, or may contain an unknown first dimension.
// An unknown first dimension means the feature is treated as having
// a variable number of blocks, and the output shape along this dimension
// is considered unknown at graph build time. Padding is applied for
// minibatch elements smaller than the maximum number of blocks for the
// given feature along this dimension.
// output_types: The type list for the return values.
// output_shapes: The list of shapes being produced.
func ParseExampleDatasetV2(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ParseExampleDatasetV2Attr) (handle tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
Type: "ParseExampleDatasetV2",
Input: []tf.Input{
input_dataset, num_parallel_calls, tf.OutputList(dense_defaults),
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping.
type GenerateVocabRemappingAttr func(optionalAttr)