diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index c6d67c9ad44..a6ee1a13b6e 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -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: <= -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) }