RNNCell¶
Versioned name : RNNCell-3
Category : Sequence processing
Short description : RNNCell represents a single RNN cell that computes the output using the formula described in the article.
Attributes
hidden_size
Description : hidden_size specifies hidden state size.
Range of values : a positive integer
Type :
int
Default value : None
Required : yes
activations
Description : activation functions for gates
Range of values : any combination of relu, sigmoid, tanh
Type : a list of strings
Default value : tanh
Required : no
activations_alpha, activations_beta
Description : activations_alpha, activations_beta functions attributes
Range of values : a list of floating-point numbers
Type :
float[]
Default value : None
Required : no
clip
Description : clip specifies value for tensor clipping to be in [-C, C] before activations
Range of values : a positive floating-point number
Type :
float
Default value : infinity that means that the clipping is not applied
Required : no
Inputs
1 :
X
- 2D tensor of type T[batch_size, input_size]
, input data. Required.2 :
initial_hidden_state
- 2D tensor of type T[batch_size, hidden_size]
. Required.3 :
W
- 2D tensor tensor of type T[hidden_size, input_size]
, the weights for matrix multiplication. Required.4 :
R
- 2D tensor tensor of type T[hidden_size, hidden_size]
, the recurrence weights for matrix multiplication. Required.5 :
B
1D tensor tensor of type T[hidden_size]
, the sum of biases (weights and recurrence weights). Required.
Outputs
1 :
Ho
- 2D tensor of type T[batch_size, hidden_size]
, the last output value of hidden state.
Types
T : any supported floating point type.
Example
<layer ... type="RNNCell" ...>
<data hidden_size="128"/>
<input>
<port id="0">
<dim>1</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>128</dim>
</port>
<port id="2">
<dim>128</dim>
<dim>16</dim>
</port>
<port id="3">
<dim>128</dim>
<dim>128</dim>
</port>
<port id="4">
<dim>128</dim>
</port>
</input>
<output>
<port id="5">
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>