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.
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: sigmoid,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
- 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). Required.
Outputs
- 1:
Ho
- 2D tensor of type T [batch_size, hidden_size]
, hidden state output.
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>