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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 297909837
Change-Id: Ic30a2b2c725f6815818ce0f61748f0dd8f6c38ac
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A. Unique TensorFlower 2020-02-28 12:57:54 -08:00 committed by TensorFlower Gardener
parent 8443eb5ce4
commit e7e48f5a85
1 changed files with 36 additions and 36 deletions
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tensorflow/go/op/wrappers.go
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@ -3580,7 +3580,7 @@ func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedT
// l1: l1 regularization factor on leaf weights, per instance based. // l1: l1 regularization factor on leaf weights, per instance based.
// l2: l2 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. // 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. // logits_dimension: The dimension of logit, i.e., number of classes.
// //
// Returns: // Returns:
@ -3677,7 +3677,7 @@ func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Outp
// l1: l1 regularization factor on leaf weights, per instance based. // l1: l1 regularization factor on leaf weights, per instance based.
// l2: l2 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. // 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. // max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
// //
// Returns: // Returns:
@ -3730,7 +3730,7 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out
// Checks whether a tree ensemble has been initialized. // Checks whether a tree ensemble has been initialized.
// //
// Arguments: // 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. // Returns output boolean on whether it is initialized or not.
func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) { 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_layers: Specifies the number of layers in the RNN model.
// num_units: Specifies the size of the hidden state. // num_units: Specifies the size of the hidden state.
// input_size: Specifies the size of the input 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. // num_params_biases: number of bias parameter vector for all layers.
// weights: the canonical form of weights that can be used for saving // weights: the canonical form of weights that can be used for saving
// and restoration. They are more likely to be compatible across different // 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. // l1: l1 regularization factor on leaf weights, per instance based.
// l2: l2 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. // 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. // logits_dimension: The dimension of logit, i.e., number of classes.
// //
// Returns: // Returns:
@ -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 // element is a bit which is set to 1 if the input tensor has an
// infinity or nan value, or zero otherwise. // 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 // element is the tensor_id, if provided, and -1 otherwise. The
// remaining four slots are the total number of elements, -infs, // remaining four slots are the total number of elements, -infs,
// +infs, and nans in the input tensor respectively. // +infs, and nans in the input tensor respectively.
@ -14089,11 +14089,11 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional
// //
// Arguments: // Arguments:
// superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of // 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 // maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal
// matrices to the left of multiplication. // matrices to the left of multiplication.
// subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal // 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 // rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of
// multiplication. // multiplication.
// //
@ -17710,7 +17710,7 @@ func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2
// biases: the canonical form of biases that can be used for saving // biases: the canonical form of biases that can be used for saving
// and restoration. They are more likely to be compatible across different // and restoration. They are more likely to be compatible across different
// generations. // 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. // num_params_biases: number of bias parameter vector for all layers.
// rnn_mode: Indicates the type of the RNN model. // rnn_mode: Indicates the type of the RNN model.
// input_mode: Indicate whether there is a linear projection between the input and // input_mode: Indicate whether there is a linear projection between the input and
@ -31238,8 +31238,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
// linear: Should be from a Variable(). // linear: Should be from a Variable().
// grad: The gradient. // grad: The gradient.
// lr: Scaling factor. Must be a scalar. // lr: Scaling factor. Must be a scalar.
// l1: L1 regulariation. 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. // lr_power: Scaling factor. Must be a scalar.
// //
@ -36706,8 +36706,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
// linear: Should be from a Variable(). // linear: Should be from a Variable().
// grad: The gradient. // grad: The gradient.
// lr: Scaling factor. Must be a scalar. // lr: Scaling factor. Must be a scalar.
// l1: L1 regulariation. Must be a scalar. // l1: L1 regularization. Must be a scalar.
// l2: L2 regulariation. Must be a scalar. // l2: L2 regularization. Must be a scalar.
// lr_power: Scaling factor. Must be a scalar. // lr_power: Scaling factor. Must be a scalar.
// //
// Returns the created operation. // 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. // indices: A vector of indices into the first dimension of var and accum.
// lr: Scaling factor. Must be a scalar. // lr: Scaling factor. Must be a scalar.
// l1: L1 regularization. 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. // lr_power: Scaling factor. Must be a scalar.
// //