diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index e6725269279..c6d67c9ad44 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: Minimum value to count. Can be set to -1 for no minimum. +// value: int32; 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: Maximum value to count. Can be set to -1 for no maximum. +// value: int32; 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: 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. +// 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. // // Returns: -// 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) { +// 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) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"binary_output": binary_output} + attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type} for _, a := range optional { a(attrs) } @@ -8607,7 +8607,7 @@ type RaggedCountSparseOutputAttr func(optionalAttr) // RaggedCountSparseOutputMinlength sets the optional minlength attribute to value. // -// value: Minimum value to count. Can be set to -1 for no minimum. +// value: int32; 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: Maximum value to count. Can be set to -1 for no maximum. +// value: int32; maximum value to count. Can be set to -1 for no maximum. // If not specified, defaults to -1 // // REQUIRES: value >= -1 @@ -8634,27 +8634,33 @@ func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr { // Counts the number of times each value occurs in the input. // // Arguments: -// 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. +// 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. // // Returns: -// 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. +// 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 @@ -13712,7 +13718,7 @@ func SparseCountSparseOutputMinlength(value int64) SparseCountSparseOutputAttr { // SparseCountSparseOutputMaxlength sets the optional maxlength attribute to value. // -// value: Maximum value to count. Can be set to -1 for no maximum. +// value: int32; maximum value to count. Can be set to -1 for no maximum. // If not specified, defaults to -1 // // REQUIRES: value >= -1 @@ -13727,22 +13733,22 @@ func SparseCountSparseOutputMaxlength(value int64) SparseCountSparseOutputAttr { // Counts the number of times each value occurs in the input. // // Arguments: -// 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. +// 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. // // Returns: -// 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) { +// 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) { if scope.Err() != nil { return } - attrs := map[string]interface{}{"binary_output": binary_output} + attrs := map[string]interface{}{"binary_count": binary_count, "output_type": output_type} for _, a := range optional { a(attrs) } @@ -18969,7 +18975,7 @@ func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2 // // value: The cropped area of the image must have an aspect ratio = // width / height within this range. -// If not specified, defaults to {f:0.75 f:1.33} +// If not specified, defaults to {f:0.75 f:1.33} func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["aspect_ratio_range"] = value @@ -18980,7 +18986,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort // // value: The cropped area of the image must contain a fraction of the // supplied image within this range. -// If not specified, defaults to {f:0.05 f:1} +// If not specified, defaults to {f:0.05 f:1} func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { m["area_range"] = value @@ -19384,7 +19390,7 @@ func ImageSummaryMaxImages(value int64) ImageSummaryAttr { // ImageSummaryBadColor sets the optional bad_color attribute to value. // // value: Color to use for pixels with non-finite values. -// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255} +// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255} func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { return func(m optionalAttr) { m["bad_color"] = value @@ -20455,7 +20461,7 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { // filter element on that dimension. The dimension order is determined by the // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { return func(m optionalAttr) { m["dilations"] = value @@ -21627,7 +21633,7 @@ func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr { // element on that dimension. The dimension order is determined by the value of // `data_format`, see above for details. Dilations in the batch and depth // dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22335,7 +22341,7 @@ func Conv2DDataFormat(value string) Conv2DAttr { // filter element on that dimension. The dimension order is determined by the // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func Conv2DDilations(value []int64) Conv2DAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22531,7 +22537,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataTy // QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value. // // value: List of dilation values. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22600,7 +22606,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) Quantized // QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value. // // value: List of dilation values. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22715,7 +22721,7 @@ func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwi // QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value. // // value: List of dilation values. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22774,7 +22780,7 @@ func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2D // QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value. // // value: List of dilation values. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr { return func(m optionalAttr) { m["dilations"] = value @@ -22948,7 +22954,7 @@ func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChann // QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value. // // value: list of dilation values. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr { return func(m optionalAttr) { m["dilations"] = value @@ -23325,7 +23331,7 @@ func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr { // filter element on that dimension. The dimension order is determined by the // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr { return func(m optionalAttr) { m["dilations"] = value @@ -25648,7 +25654,7 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi type Conv3DBackpropFilterAttr func(optionalAttr) // Conv3DBackpropFilterDilations sets the optional dilations attribute to value. -// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr { return func(m optionalAttr) { m["dilations"] = value @@ -25711,7 +25717,7 @@ func Conv3DDataFormat(value string) Conv3DAttr { // filter element on that dimension. The dimension order is determined by the // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} func Conv3DDilations(value []int64) Conv3DAttr { return func(m optionalAttr) { m["dilations"] = value @@ -25962,7 +25968,7 @@ func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dN // element on that dimension. The dimension order is determined by the value of // `data_format`, see above for details. Dilations in the batch and depth // dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { return func(m optionalAttr) { m["dilations"] = value @@ -26446,7 +26452,7 @@ func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr { // filter element on that dimension. The dimension order is determined by the // value of `data_format`, see above for details. Dilations in the batch and // depth dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr { return func(m optionalAttr) { m["dilations"] = value @@ -45534,7 +45540,7 @@ func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2d // element on that dimension. The dimension order is determined by the value of // `data_format`, see above for details. Dilations in the batch and depth // dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr { return func(m optionalAttr) { m["dilations"] = value @@ -47474,7 +47480,7 @@ func LoadTPUEmbeddingFTRLParameters(scope *Scope, parameters tf.Output, accumula type Conv3DBackpropInputAttr func(optionalAttr) // Conv3DBackpropInputDilations sets the optional dilations attribute to value. -// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1} func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr { return func(m optionalAttr) { m["dilations"] = value @@ -47545,7 +47551,7 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr { // element on that dimension. The dimension order is determined by the value of // `data_format`, see above for details. Dilations in the batch and depth // dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr { return func(m optionalAttr) { m["dilations"] = value @@ -48534,7 +48540,7 @@ func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr { // element on that dimension. The dimension order is determined by the value of // `data_format`, see above for details. Dilations in the batch and depth // dimensions must be 1. -// If not specified, defaults to {i:1 i:1 i:1 i:1} +// If not specified, defaults to {i:1 i:1 i:1 i:1} func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr { return func(m optionalAttr) { m["dilations"] = value