From e7e48f5a85898f8bac5d3475fbf230c286382736 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 28 Feb 2020 12:57:54 -0800 Subject: [PATCH] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 297909837 Change-Id: Ic30a2b2c725f6815818ce0f61748f0dd8f6c38ac --- tensorflow/go/op/wrappers.go | 72 ++++++++++++++++++------------------ 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 4f71c7398ec..d10dfc3b5f5 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -3580,7 +3580,7 @@ func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedT // l1: l1 regularization factor on leaf weights, per instance based. // l2: l2 regularization factor on leaf weights, per instance based. // tree_complexity: adjustment to the gain, per leaf based. -// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. // logits_dimension: The dimension of logit, i.e., number of classes. // // Returns: @@ -3677,7 +3677,7 @@ func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Outp // l1: l1 regularization factor on leaf weights, per instance based. // l2: l2 regularization factor on leaf weights, per instance based. // tree_complexity: adjustment to the gain, per leaf based. -// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. // max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors. // // Returns: @@ -3730,7 +3730,7 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out // Checks whether a tree ensemble has been initialized. // // Arguments: -// tree_ensemble_handle: Handle to the tree ensemble resouce. +// tree_ensemble_handle: Handle to the tree ensemble resource. // // Returns output boolean on whether it is initialized or not. func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) { @@ -5126,7 +5126,7 @@ func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2 // num_layers: Specifies the number of layers in the RNN model. // num_units: Specifies the size of the hidden state. // input_size: Specifies the size of the input state. -// num_params_weigths: number of weight parameter matrix for all layers. +// num_params_weights: number of weight parameter matrix for all layers. // num_params_biases: number of bias parameter vector for all layers. // weights: the canonical form of weights that can be used for saving // and restoration. They are more likely to be compatible across different @@ -8344,7 +8344,7 @@ func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCa // l1: l1 regularization factor on leaf weights, per instance based. // l2: l2 regularization factor on leaf weights, per instance based. // tree_complexity: adjustment to the gain, per leaf based. -// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. // logits_dimension: The dimension of logit, i.e., number of classes. // // Returns: @@ -11692,7 +11692,7 @@ 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 @@ -11949,7 +11949,7 @@ 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 @@ -11960,7 +11960,7 @@ 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 @@ -12166,7 +12166,7 @@ func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBo // // 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 SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { m["aspect_ratio_range"] = value @@ -12177,7 +12177,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted // // 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 SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { m["area_range"] = value @@ -13731,7 +13731,7 @@ func DebugNumericSummaryV2OutputDtype(value tf.DataType) DebugNumericSummaryV2At // element is a bit which is set to 1 if the input tensor has an // infinity or nan value, or zero otherwise. // -// 3 (CONCISE_HEALTH): Ouput a float32/64 tensor of shape [5]. The 1st +// 3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st // element is the tensor_id, if provided, and -1 otherwise. The // remaining four slots are the total number of elements, -infs, // +infs, and nans in the input tensor respectively. @@ -14089,11 +14089,11 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional // // Arguments: // superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of -// tri-diagonal matrices to the left of multiplication. Last element is ingored. +// tri-diagonal matrices to the left of multiplication. Last element is ignored. // maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal // matrices to the left of multiplication. // subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal -// matrices to the left of multiplication. First element is ingored. +// matrices to the left of multiplication. First element is ignored. // rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of // multiplication. // @@ -17710,7 +17710,7 @@ func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2 // biases: the canonical form of biases that can be used for saving // and restoration. They are more likely to be compatible across different // generations. -// num_params_weigths: number of weight parameter matrix for all layers. +// num_params_weights: number of weight parameter matrix for all layers. // num_params_biases: number of bias parameter vector for all layers. // rnn_mode: Indicates the type of the RNN model. // input_mode: Indicate whether there is a linear projection between the input and @@ -19018,7 +19018,7 @@ 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 @@ -20089,7 +20089,7 @@ 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 @@ -21357,7 +21357,7 @@ 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 @@ -22065,7 +22065,7 @@ 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 @@ -22261,7 +22261,7 @@ 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 @@ -22330,7 +22330,7 @@ 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 @@ -22445,7 +22445,7 @@ 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 @@ -22504,7 +22504,7 @@ 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 @@ -22678,7 +22678,7 @@ 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 @@ -23059,7 +23059,7 @@ 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 @@ -25499,7 +25499,7 @@ 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 @@ -25831,7 +25831,7 @@ 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 @@ -25881,7 +25881,7 @@ func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, fil 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 @@ -26131,7 +26131,7 @@ 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 @@ -26761,7 +26761,7 @@ 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 @@ -27826,7 +27826,7 @@ 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 @@ -31238,8 +31238,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr { // linear: Should be from a Variable(). // grad: The gradient. // lr: Scaling factor. Must be a scalar. -// l1: L1 regulariation. Must be a scalar. -// l2: L2 shrinkage regulariation. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 shrinkage regularization. Must be a scalar. // // lr_power: Scaling factor. Must be a scalar. // @@ -36706,8 +36706,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { // linear: Should be from a Variable(). // grad: The gradient. // lr: Scaling factor. Must be a scalar. -// l1: L1 regulariation. Must be a scalar. -// l2: L2 regulariation. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. // lr_power: Scaling factor. Must be a scalar. // // Returns the created operation. @@ -43228,7 +43228,7 @@ func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2At // indices: A vector of indices into the first dimension of var and accum. // lr: Scaling factor. Must be a scalar. // l1: L1 regularization. Must be a scalar. -// l2: L2 shrinkage regulariation. Must be a scalar. +// l2: L2 shrinkage regularization. Must be a scalar. // // lr_power: Scaling factor. Must be a scalar. // @@ -45536,7 +45536,7 @@ 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