GRUCell#
Versioned name: GRUCell-3
Category: Sequence processing
Short description: GRUCell represents a single GRU Cell that computes the output using the formula described in the paper.
Detailed description: GRUCell computes the output Ht for the current time step based on the followint formula:
Formula:
* - matrix multiplication
(.) - Hadamard product(element-wise)
[,] - concatenation
f, g - are activation functions.
zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
Ht = (1 - zt) (.) ht + zt (.) Ht-1
Attributes
hidden_size
Description: hidden_size specifies hidden state size.
Range of values: a positive integer
Type:
int
Required: yes
activations
Description: activation functions for gates
Range of values: any combination of relu, sigmoid, tanh
Type: a list of strings
Default value: sigmoid for f, tanh for g
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
linear_before_reset
Description: linear_before_reset flag denotes if the layer behaves according to the modification of GRUCell described in the formula in the ONNX documentation.
Range of values: true or false
Type:
boolean
Default value: false
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 of type T[3 * hidden_size, input_size]
, the weights for matrix multiplication, gate order: zrh. Required.4:
R
- 2D tensor of type T[3 * hidden_size, hidden_size]
, the recurrence weights for matrix multiplication, gate order: zrh. Required.5:
B
- 1D tensor of type T. If linear_before_reset is set to 1, then the shape is[4 * hidden_size]
- the sum of biases for z and r gates (weights and recurrence weights), the biases for h gate are placed separately. Otherwise the shape is[3 * hidden_size]
, the sum of biases (weights and recurrence weights). Optional.
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="GRUCell" ...>
<data hidden_size="128" linear_before_reset="1"/>
<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>384</dim>
<dim>16</dim>
</port>
<port id="3">
<dim>384</dim>
<dim>128</dim>
</port>
<port id="4">
<dim>768</dim>
</port>
</input>
<output>
<port id="5">
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>