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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 290650771
Change-Id: Ic50022d5ac2849a64c315b0e5751a885cb1e66f0
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A. Unique TensorFlower 2020-01-20 14:46:21 -08:00 committed by TensorFlower Gardener
parent f04ae382e8
commit a901c88061
1 changed files with 14 additions and 14 deletions
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28
tensorflow/go/op/wrappers.go
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@ -3614,7 +3614,7 @@ 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: minimum avg of hessians in a node before required for the node to be considered for splitting.
// min_node_weight: mininum 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:
@ -3711,7 +3711,7 @@ 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: minimum avg of hessians in a node before required for the node to be considered for splitting.
// min_node_weight: mininum 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:
@ -3764,7 +3764,7 @@ 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 resource.
// tree_ensemble_handle: Handle to the tree ensemble resouce.
//
// Returns output boolean on whether it is initialized or not.
func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) {
@ -5160,7 +5160,7 @@ 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_weights: number of weight parameter matrix for all layers.
// num_params_weigths: 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
@ -8378,7 +8378,7 @@ 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: minimum avg of hessians in a node before required for the node to be considered for splitting.
// min_node_weight: mininum 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:
@ -13774,7 +13774,7 @@ 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): Output a float32/64 tensor of shape [5]. The 1st
// 3 (CONCISE_HEALTH): Ouput 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.
@ -14132,11 +14132,11 @@ 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 ignored.
// tri-diagonal matrices to the left of multiplication. Last element is ingored.
// 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 ignored.
// matrices to the left of multiplication. First element is ingored.
// rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of
// multiplication.
//
@ -17744,7 +17744,7 @@ 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_weights: number of weight parameter matrix for all layers.
// num_params_weigths: 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
@ -30968,8 +30968,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
// linear: Should be from a Variable().
// grad: The gradient.
// lr: Scaling factor. Must be a scalar.
// l1: L1 regularization. Must be a scalar.
// l2: L2 shrinkage regularization. Must be a scalar.
// l1: L1 regulariation. Must be a scalar.
// l2: L2 shrinkage regulariation. Must be a scalar.
//
// lr_power: Scaling factor. Must be a scalar.
//
@ -36345,8 +36345,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
// linear: Should be from a Variable().
// grad: The gradient.
// lr: Scaling factor. Must be a scalar.
// l1: L1 regularization. Must be a scalar.
// l2: L2 regularization. Must be a scalar.
// l1: L1 regulariation. Must be a scalar.
// l2: L2 regulariation. Must be a scalar.
// lr_power: Scaling factor. Must be a scalar.
//
// Returns the created operation.
@ -42995,7 +42995,7 @@ 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 regularization. Must be a scalar.
// l2: L2 shrinkage regulariation. Must be a scalar.
//
// lr_power: Scaling factor. Must be a scalar.
//