Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 297909837 Change-Id: Ic30a2b2c725f6815818ce0f61748f0dd8f6c38ac
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
8443eb5ce4
commit
e7e48f5a85
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@ -3580,7 +3580,7 @@ func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedT
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// logits_dimension: The dimension of logit, i.e., number of classes.
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//
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// Returns:
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@ -3677,7 +3677,7 @@ func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Outp
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
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//
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// Returns:
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@ -3730,7 +3730,7 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out
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// Checks whether a tree ensemble has been initialized.
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//
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// Arguments:
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// tree_ensemble_handle: Handle to the tree ensemble resouce.
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// tree_ensemble_handle: Handle to the tree ensemble resource.
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//
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// Returns output boolean on whether it is initialized or not.
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func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) {
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@ -5126,7 +5126,7 @@ func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2
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// num_layers: Specifies the number of layers in the RNN model.
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// num_units: Specifies the size of the hidden state.
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// input_size: Specifies the size of the input state.
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// num_params_weigths: number of weight parameter matrix for all layers.
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// num_params_weights: number of weight parameter matrix for all layers.
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// num_params_biases: number of bias parameter vector for all layers.
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// weights: the canonical form of weights that can be used for saving
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// and restoration. They are more likely to be compatible across different
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@ -8344,7 +8344,7 @@ func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCa
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// l1: l1 regularization factor on leaf weights, per instance based.
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// l2: l2 regularization factor on leaf weights, per instance based.
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// tree_complexity: adjustment to the gain, per leaf based.
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// min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
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// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
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// logits_dimension: The dimension of logit, i.e., number of classes.
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//
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// Returns:
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@ -11692,7 +11692,7 @@ func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2d
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -11949,7 +11949,7 @@ func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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@ -11960,7 +11960,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
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return func(m optionalAttr) {
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m["area_range"] = value
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@ -12166,7 +12166,7 @@ func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBo
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//
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// value: The cropped area of the image must have an aspect ratio =
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// width / height within this range.
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// If not specified, defaults to {f:0.75 f:1.33}
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// If not specified, defaults to {f:0.75 f:1.33}
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func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
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return func(m optionalAttr) {
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m["aspect_ratio_range"] = value
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@ -12177,7 +12177,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted
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//
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// value: The cropped area of the image must contain a fraction of the
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// supplied image within this range.
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// If not specified, defaults to {f:0.05 f:1}
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// If not specified, defaults to {f:0.05 f:1}
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func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
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return func(m optionalAttr) {
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m["area_range"] = value
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@ -13731,7 +13731,7 @@ func DebugNumericSummaryV2OutputDtype(value tf.DataType) DebugNumericSummaryV2At
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// element is a bit which is set to 1 if the input tensor has an
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// infinity or nan value, or zero otherwise.
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//
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// 3 (CONCISE_HEALTH): Ouput a float32/64 tensor of shape [5]. The 1st
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// 3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st
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// element is the tensor_id, if provided, and -1 otherwise. The
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// remaining four slots are the total number of elements, -infs,
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// +infs, and nans in the input tensor respectively.
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@ -14089,11 +14089,11 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional
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//
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// Arguments:
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// superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of
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// tri-diagonal matrices to the left of multiplication. Last element is ingored.
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// tri-diagonal matrices to the left of multiplication. Last element is ignored.
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// maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal
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// matrices to the left of multiplication.
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// subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal
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// matrices to the left of multiplication. First element is ingored.
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// matrices to the left of multiplication. First element is ignored.
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// rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of
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// multiplication.
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//
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@ -17710,7 +17710,7 @@ func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2
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// biases: the canonical form of biases that can be used for saving
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// and restoration. They are more likely to be compatible across different
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// generations.
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// num_params_weigths: number of weight parameter matrix for all layers.
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// num_params_weights: number of weight parameter matrix for all layers.
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// num_params_biases: number of bias parameter vector for all layers.
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// rnn_mode: Indicates the type of the RNN model.
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// input_mode: Indicate whether there is a linear projection between the input and
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@ -19018,7 +19018,7 @@ func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
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// ImageSummaryBadColor sets the optional bad_color attribute to value.
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//
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// value: Color to use for pixels with non-finite values.
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// 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}
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// 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}
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func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
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return func(m optionalAttr) {
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m["bad_color"] = value
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@ -20089,7 +20089,7 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -21357,7 +21357,7 @@ func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr {
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22065,7 +22065,7 @@ func Conv2DDataFormat(value string) Conv2DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DDilations(value []int64) Conv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22261,7 +22261,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataTy
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// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22330,7 +22330,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) Quantized
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// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22445,7 +22445,7 @@ func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwi
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// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22504,7 +22504,7 @@ func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2D
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// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value.
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//
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// value: List of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -22678,7 +22678,7 @@ func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChann
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// QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value.
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//
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// value: list of dilation values.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -23059,7 +23059,7 @@ func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25499,7 +25499,7 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi
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type Conv3DBackpropFilterAttr func(optionalAttr)
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// Conv3DBackpropFilterDilations sets the optional dilations attribute to value.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25831,7 +25831,7 @@ func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dN
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -25881,7 +25881,7 @@ func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, fil
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type Conv3DBackpropInputAttr func(optionalAttr)
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// Conv3DBackpropInputDilations sets the optional dilations attribute to value.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -26131,7 +26131,7 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -26761,7 +26761,7 @@ func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -27826,7 +27826,7 @@ func Conv3DDataFormat(value string) Conv3DAttr {
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// filter element on that dimension. The dimension order is determined by the
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// value of `data_format`, see above for details. Dilations in the batch and
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// depth dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
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func Conv3DDilations(value []int64) Conv3DAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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@ -31238,8 +31238,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
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// linear: Should be from a Variable().
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// grad: The gradient.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regulariation. Must be a scalar.
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// l2: L2 shrinkage regulariation. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 shrinkage regularization. Must be a scalar.
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//
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// lr_power: Scaling factor. Must be a scalar.
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//
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@ -36706,8 +36706,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
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// linear: Should be from a Variable().
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// grad: The gradient.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regulariation. Must be a scalar.
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// l2: L2 regulariation. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 regularization. Must be a scalar.
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// lr_power: Scaling factor. Must be a scalar.
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//
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// Returns the created operation.
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@ -43228,7 +43228,7 @@ func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2At
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// indices: A vector of indices into the first dimension of var and accum.
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// lr: Scaling factor. Must be a scalar.
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// l1: L1 regularization. Must be a scalar.
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// l2: L2 shrinkage regulariation. Must be a scalar.
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// l2: L2 shrinkage regularization. Must be a scalar.
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//
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// lr_power: Scaling factor. Must be a scalar.
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//
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@ -45536,7 +45536,7 @@ func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
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// element on that dimension. The dimension order is determined by the value of
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// `data_format`, see above for details. Dilations in the batch and depth
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// dimensions must be 1.
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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// If not specified, defaults to {i:1 i:1 i:1 i:1}
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func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
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return func(m optionalAttr) {
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m["dilations"] = value
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Reference in New Issue