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Go: Update generated wrapper functions for TensorFlow ops.

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PiperOrigin-RevId: 252558885
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A. Unique TensorFlower 2019-06-10 23:45:34 -07:00 committed by TensorFlower Gardener
parent 2177c5597b
commit 797941af07
1 changed files with 257 additions and 0 deletions
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257
tensorflow/go/op/wrappers.go
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@ -4509,6 +4509,116 @@ func CTCGreedyDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output,
return op.Output(0), op.Output(1), op.Output(2), op.Output(3)
}
// CudnnRNNCanonicalToParamsV2Attr is an optional argument to CudnnRNNCanonicalToParamsV2.
type CudnnRNNCanonicalToParamsV2Attr func(optionalAttr)
// CudnnRNNCanonicalToParamsV2RnnMode sets the optional rnn_mode attribute to value.
// If not specified, defaults to "lstm"
func CudnnRNNCanonicalToParamsV2RnnMode(value string) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["rnn_mode"] = value
}
}
// CudnnRNNCanonicalToParamsV2InputMode sets the optional input_mode attribute to value.
// If not specified, defaults to "linear_input"
func CudnnRNNCanonicalToParamsV2InputMode(value string) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["input_mode"] = value
}
}
// CudnnRNNCanonicalToParamsV2Direction sets the optional direction attribute to value.
// If not specified, defaults to "unidirectional"
func CudnnRNNCanonicalToParamsV2Direction(value string) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["direction"] = value
}
}
// CudnnRNNCanonicalToParamsV2Dropout sets the optional dropout attribute to value.
// If not specified, defaults to 0
func CudnnRNNCanonicalToParamsV2Dropout(value float32) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["dropout"] = value
}
}
// CudnnRNNCanonicalToParamsV2Seed sets the optional seed attribute to value.
// If not specified, defaults to 0
func CudnnRNNCanonicalToParamsV2Seed(value int64) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["seed"] = value
}
}
// CudnnRNNCanonicalToParamsV2Seed2 sets the optional seed2 attribute to value.
// If not specified, defaults to 0
func CudnnRNNCanonicalToParamsV2Seed2(value int64) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["seed2"] = value
}
}
// CudnnRNNCanonicalToParamsV2NumProj sets the optional num_proj attribute to value.
// If not specified, defaults to 0
func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2Attr {
return func(m optionalAttr) {
m["num_proj"] = value
}
}
// Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM.
//
// Writes a set of weights into the opaque params buffer so they can be used in
// upcoming training or inferences.
//
// Note that the params buffer may not be compatible across different GPUs. So any
// save and restoration should be converted to and from the canonical weights and
// biases.
//
// 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.
// weights: the canonical form of weights that can be used for saving
// and restoration. They are more likely to be compatible across different
// generations.
// 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_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
// The actual computation before the first layer. 'skip_input' is only allowed
// when input_size == num_units; 'auto_select' implies 'skip_input' when
// input_size == num_units; otherwise, it implies 'linear_input'.
// direction: Indicates whether a bidirectional model will be used.
// dir = (direction == bidirectional) ? 2 : 1
// dropout: dropout probability. When set to 0., dropout is disabled.
// seed: the 1st part of a seed to initialize dropout.
// seed2: the 2nd part of a seed to initialize dropout.
// num_proj: The output dimensionality for the projection matrices. If None or 0,
// no projection is performed.
func CudnnRNNCanonicalToParamsV2(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsV2Attr) (params tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
Type: "CudnnRNNCanonicalToParamsV2",
Input: []tf.Input{
num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases),
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
return op.Output(0)
}
// CudnnRNNCanonicalToParamsAttr is an optional argument to CudnnRNNCanonicalToParams.
type CudnnRNNCanonicalToParamsAttr func(optionalAttr)
@ -4610,6 +4720,129 @@ func CudnnRNNCanonicalToParams(scope *Scope, num_layers tf.Output, num_units tf.
return op.Output(0)
}
// CudnnRNNParamsToCanonicalV2Attr is an optional argument to CudnnRNNParamsToCanonicalV2.
type CudnnRNNParamsToCanonicalV2Attr func(optionalAttr)
// CudnnRNNParamsToCanonicalV2RnnMode sets the optional rnn_mode attribute to value.
// If not specified, defaults to "lstm"
func CudnnRNNParamsToCanonicalV2RnnMode(value string) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["rnn_mode"] = value
}
}
// CudnnRNNParamsToCanonicalV2InputMode sets the optional input_mode attribute to value.
// If not specified, defaults to "linear_input"
func CudnnRNNParamsToCanonicalV2InputMode(value string) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["input_mode"] = value
}
}
// CudnnRNNParamsToCanonicalV2Direction sets the optional direction attribute to value.
// If not specified, defaults to "unidirectional"
func CudnnRNNParamsToCanonicalV2Direction(value string) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["direction"] = value
}
}
// CudnnRNNParamsToCanonicalV2Dropout sets the optional dropout attribute to value.
// If not specified, defaults to 0
func CudnnRNNParamsToCanonicalV2Dropout(value float32) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["dropout"] = value
}
}
// CudnnRNNParamsToCanonicalV2Seed sets the optional seed attribute to value.
// If not specified, defaults to 0
func CudnnRNNParamsToCanonicalV2Seed(value int64) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["seed"] = value
}
}
// CudnnRNNParamsToCanonicalV2Seed2 sets the optional seed2 attribute to value.
// If not specified, defaults to 0
func CudnnRNNParamsToCanonicalV2Seed2(value int64) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["seed2"] = value
}
}
// CudnnRNNParamsToCanonicalV2NumProj sets the optional num_proj attribute to value.
// If not specified, defaults to 0
func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2Attr {
return func(m optionalAttr) {
m["num_proj"] = value
}
}
// Retrieves CudnnRNN params in canonical form. It supports the projection in LSTM.
//
// Retrieves a set of weights from the opaque params buffer that can be saved and
// restored in a way compatible with future runs.
//
// Note that the params buffer may not be compatible across different GPUs. So any
// save and restoration should be converted to and from the canonical weights and
// biases.
//
// 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_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
// generations.
// biases: the canonical form of biases that can be used for saving
// and restoration. They are more likely to be compatible across different
// generations.
// rnn_mode: Indicates the type of the RNN model.
// input_mode: Indicate whether there is a linear projection between the input and
// The actual computation before the first layer. 'skip_input' is only allowed
// when input_size == num_units; 'auto_select' implies 'skip_input' when
// input_size == num_units; otherwise, it implies 'linear_input'.
// direction: Indicates whether a bidirectional model will be used.
// dir = (direction == bidirectional) ? 2 : 1
// dropout: dropout probability. When set to 0., dropout is disabled.
// seed: the 1st part of a seed to initialize dropout.
// seed2: the 2nd part of a seed to initialize dropout.
// num_proj: The output dimensionality for the projection matrices. If None or 0,
// no projection is performed.
func CudnnRNNParamsToCanonicalV2(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params_weights int64, num_params_biases int64, optional ...CudnnRNNParamsToCanonicalV2Attr) (weights []tf.Output, biases []tf.Output) {
if scope.Err() != nil {
return
}
attrs := map[string]interface{}{"num_params_weights": num_params_weights, "num_params_biases": num_params_biases}
for _, a := range optional {
a(attrs)
}
opspec := tf.OpSpec{
Type: "CudnnRNNParamsToCanonicalV2",
Input: []tf.Input{
num_layers, num_units, input_size, params,
},
Attrs: attrs,
}
op := scope.AddOperation(opspec)
if scope.Err() != nil {
return
}
var idx int
var err error
if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil {
scope.UpdateErr("CudnnRNNParamsToCanonicalV2", err)
return
}
if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil {
scope.UpdateErr("CudnnRNNParamsToCanonicalV2", err)
return
}
return weights, biases
}
// CudnnRNNBackpropAttr is an optional argument to CudnnRNNBackprop.
type CudnnRNNBackpropAttr func(optionalAttr)
@ -14047,6 +14280,14 @@ func CudnnRNNBackpropV3Seed2(value int64) CudnnRNNBackpropV3Attr {
}
}
// CudnnRNNBackpropV3NumProj sets the optional num_proj attribute to value.
// If not specified, defaults to 0
func CudnnRNNBackpropV3NumProj(value int64) CudnnRNNBackpropV3Attr {
return func(m optionalAttr) {
m["num_proj"] = value
}
}
// CudnnRNNBackpropV3TimeMajor sets the optional time_major attribute to value.
// If not specified, defaults to true
func CudnnRNNBackpropV3TimeMajor(value bool) CudnnRNNBackpropV3Attr {
@ -20107,6 +20348,14 @@ func CudnnRNNParamsSizeSeed2(value int64) CudnnRNNParamsSizeAttr {
}
}
// CudnnRNNParamsSizeNumProj sets the optional num_proj attribute to value.
// If not specified, defaults to 0
func CudnnRNNParamsSizeNumProj(value int64) CudnnRNNParamsSizeAttr {
return func(m optionalAttr) {
m["num_proj"] = value
}
}
// Computes size of weights that can be used by a Cudnn RNN model.
//
// Return the params size that can be used by the Cudnn RNN model. Subsequent
@ -23351,6 +23600,14 @@ func CudnnRNNV3Seed2(value int64) CudnnRNNV3Attr {
}
}
// CudnnRNNV3NumProj sets the optional num_proj attribute to value.
// If not specified, defaults to 0
func CudnnRNNV3NumProj(value int64) CudnnRNNV3Attr {
return func(m optionalAttr) {
m["num_proj"] = value
}
}
// CudnnRNNV3IsTraining sets the optional is_training attribute to value.
// If not specified, defaults to true
func CudnnRNNV3IsTraining(value bool) CudnnRNNV3Attr {