T
- data type for params()
output@Operator public final class CudnnRNNCanonicalToParamsV2<T extends Number> extends PrimitiveOp implements Operand<T>
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.
Modifier and Type | Class and Description |
---|---|
static class |
CudnnRNNCanonicalToParamsV2.Options
Optional attributes for
CudnnRNNCanonicalToParamsV2 |
operation
Modifier and Type | Method and Description |
---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T extends Number> |
create(Scope scope,
Operand<Integer> numLayers,
Operand<Integer> numUnits,
Operand<Integer> inputSize,
Iterable<Operand<T>> weights,
Iterable<Operand<T>> biases,
CudnnRNNCanonicalToParamsV2.Options... options)
Factory method to create a class wrapping a new CudnnRNNCanonicalToParamsV2 operation.
|
static CudnnRNNCanonicalToParamsV2.Options |
direction(String direction) |
static CudnnRNNCanonicalToParamsV2.Options |
dropout(Float dropout) |
static CudnnRNNCanonicalToParamsV2.Options |
inputMode(String inputMode) |
static CudnnRNNCanonicalToParamsV2.Options |
numProj(Long numProj) |
Output<T> |
params() |
static CudnnRNNCanonicalToParamsV2.Options |
rnnMode(String rnnMode) |
static CudnnRNNCanonicalToParamsV2.Options |
seed(Long seed) |
static CudnnRNNCanonicalToParamsV2.Options |
seed2(Long seed2) |
equals, hashCode, op, toString
public static <T extends Number> CudnnRNNCanonicalToParamsV2<T> create(Scope scope, Operand<Integer> numLayers, Operand<Integer> numUnits, Operand<Integer> inputSize, Iterable<Operand<T>> weights, Iterable<Operand<T>> biases, CudnnRNNCanonicalToParamsV2.Options... options)
scope
- current scopenumLayers
- numUnits
- inputSize
- weights
- biases
- options
- carries optional attributes valuespublic static CudnnRNNCanonicalToParamsV2.Options rnnMode(String rnnMode)
rnnMode
- public static CudnnRNNCanonicalToParamsV2.Options inputMode(String inputMode)
inputMode
- public static CudnnRNNCanonicalToParamsV2.Options direction(String direction)
direction
- public static CudnnRNNCanonicalToParamsV2.Options dropout(Float dropout)
dropout
- public static CudnnRNNCanonicalToParamsV2.Options seed(Long seed)
seed
- public static CudnnRNNCanonicalToParamsV2.Options seed2(Long seed2)
seed2
- public static CudnnRNNCanonicalToParamsV2.Options numProj(Long numProj)
numProj
- public Output<T> asOutput()
Operand
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
asOutput
in interface Operand<T extends Number>
OperationBuilder.addInput(Output)
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