experiments
/
STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Go: Update generated wrapper functions for TensorFlow ops. 

PiperOrigin-RevId: 311562488
Change-Id: I7dd029345a87fd0c982a8bbedefc29df8a5fd563
This commit is contained in:
A. Unique TensorFlower 2020-05-14 10:44:05 -07:00 committed by TensorFlower Gardener
parent c3d351abd2
commit 8098b12009
1 changed files with 37 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

80
tensorflow/go/op/wrappers.go
View File

@ -4715,7 +4715,7 @@ type DenseCountSparseOutputAttr func(optionalAttr)
// DenseCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// value: Minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -4727,7 +4727,7 @@ func DenseCountSparseOutputMinlength(value int64) DenseCountSparseOutputAttr {
// DenseCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// value: Maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -4742,20 +4742,20 @@ func DenseCountSparseOutputMaxlength(value int64) DenseCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// values: int32 or int64; Tensor containing data to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
// values: Tensor containing data to count.
// weights: A Tensor of the same shape as indices containing per-index weight values. May
// also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
//
// Returns:
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// output_dense_shape: int64; shape tensor for the resulting sparse tensor object.
func DenseCountSparseOutput(scope *Scope, values tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...DenseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
func DenseCountSparseOutput(scope *Scope, values tf.Output, weights tf.Output, binary_output bool, optional ...DenseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
attrs := map[string]interface{}{"binary_output": binary_output}
for _, a := range optional {
a(attrs)
}
@ -8607,7 +8607,7 @@ type RaggedCountSparseOutputAttr func(optionalAttr)
// RaggedCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// value: Minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -8619,7 +8619,7 @@ func RaggedCountSparseOutputMinlength(value int64) RaggedCountSparseOutputAttr {
// RaggedCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// value: Maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -8634,33 +8634,27 @@ func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// splits: int64; Tensor containing the row splits of the ragged tensor to count.
// values: int32 or int64; Tensor containing values of the sparse tensor to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
// splits: Tensor containing the row splits of the ragged tensor to count.
// values: Tensor containing values of the sparse tensor to count.
// weights: A Tensor of the same shape as indices containing per-index weight values.
// May also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
//
// Returns:
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// END
// }
// out_arg {
// name: "output_dense_shape"
// description: <<END
// int64; shape tensor for the resulting sparse tensor object.
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
// END
// }
// attr {
// name: "T"
// description: <<END
// dtype; dtype of the input values tensor.
// output_dense_shape
func RaggedCountSparseOutput(scope *Scope, splits tf.Output, values tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...RaggedCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// Dtype of the input values tensor.
func RaggedCountSparseOutput(scope *Scope, splits tf.Output, values tf.Output, weights tf.Output, binary_output bool, optional ...RaggedCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
attrs := map[string]interface{}{"binary_output": binary_output}
for _, a := range optional {
a(attrs)
}
@ -13706,7 +13700,7 @@ type SparseCountSparseOutputAttr func(optionalAttr)
// SparseCountSparseOutputMinlength sets the optional minlength attribute to value.
//
// value: int32; minimum value to count. Can be set to -1 for no minimum.
// value: Minimum value to count. Can be set to -1 for no minimum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -13718,7 +13712,7 @@ func SparseCountSparseOutputMinlength(value int64) SparseCountSparseOutputAttr {
// SparseCountSparseOutputMaxlength sets the optional maxlength attribute to value.
//
// value: int32; maximum value to count. Can be set to -1 for no maximum.
// value: Maximum value to count. Can be set to -1 for no maximum.
// If not specified, defaults to -1
//
// REQUIRES: value >= -1
@ -13733,22 +13727,22 @@ func SparseCountSparseOutputMaxlength(value int64) SparseCountSparseOutputAttr {
// Counts the number of times each value occurs in the input.
//
// Arguments:
// indices: int64; Tensor containing the indices of the sparse tensor to count.
// values: int32 or int64; Tensor containing values of the sparse tensor to count.
// dense_shape: int64; Tensor containing the dense shape of the sparse tensor to count.
// weights: float32; Optional rank 1 Tensor (shape=[max_values]) with weights for each count value.
// binary_count: bool; whether to output the number of occurrences of each value or 1.
// output_type: dtype; dtype of the output values tensor.
// indices: Tensor containing the indices of the sparse tensor to count.
// values: Tensor containing values of the sparse tensor to count.
// dense_shape: Tensor containing the dense shape of the sparse tensor to count.
// weights: A Tensor of the same shape as indices containing per-index weight values.
// May also be the empty tensor if no weights are used.
// binary_output: Whether to output the number of occurrences of each value or 1.
//
// Returns:
// output_indices: int64; indices tensor for the resulting sparse tensor object.
// output_values: int64 or float32; values tensor for the resulting sparse tensor object.
// output_dense_shape: int64; shape tensor for the resulting sparse tensor object.
func SparseCountSparseOutput(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, weights tf.Output, binary_count bool, output_type tf.DataType, optional ...SparseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
// output_indices: Indices tensor for the resulting sparse tensor object.
// output_values: Values tensor for the resulting sparse tensor object.
// output_dense_shape: Shape tensor for the resulting sparse tensor object.
func SparseCountSparseOutput(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, weights tf.Output, binary_output bool, optional ...SparseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type}
attrs := map[string]interface{}{"binary_output": binary_output}
for _, a := range optional {
a(attrs)
}