GRUSequence#
Versioned name: GRUSequence-5
Category: Sequence processing
Short description: GRUSequence operation represents a series of GRU cells. Each cell is implemented as GRUCell operation.
Detailed description
A single cell in the sequence is implemented in the same way as in GRUCell operation. GRUSequence
represents a sequence of GRU cells. The sequence can be connected differently depending on
direction
attribute that specifies the direction of traversing of input data along sequence
dimension or specifies whether it should be a bidirectional sequence. The most of the attributes
are in sync with the specification of ONNX GRU operator defined
GRUCell
Attributes
hidden_size
Description: hidden_size specifies hidden state size.
Range of values: a positive integer
Type: int
Required: yes
activations
Description: activations specifies activation functions for gates, there are two gates, so two activation functions should be specified as a value for this attributes
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 attributes of functions; applicability and meaning of these attributes depends on chosen activation functions
Range of values: a list of floating-point numbers
Type:
float[]
Default value: None
Required: no
clip
Description: clip specifies bound values [-C, C] for tensor clipping. Clipping is performed 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
direction
Description: Specify if the RNN is forward, reverse, or bidirectional. If it is one of forward or reverse then
num_directions = 1
, if it is bidirectional, thennum_directions = 2
. Thisnum_directions
value specifies input/output shape requirements.Range of values: forward, reverse, bidirectional
Type:
string
Required: yes
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
- 3D tensor of type T1[batch_size, seq_length, input_size]
, input data. It differs from GRUCell 1st input only by additional axis with sizeseq_length
. Required.2:
initial_hidden_state
- 3D tensor of type T1[batch_size, num_directions, hidden_size]
, input hidden state data. Required.3:
sequence_lengths
- 1D tensor of type T2[batch_size]
, specifies real sequence lengths for each batch element. In case of negative values in this input, the operation behavior is undefined. Required.4:
W
- 3D tensor of type T1[num_directions, 3 * hidden_size, input_size]
, the weights for matrix multiplication, gate order: zrh. Required.5:
R
- 3D tensor of type T1[num_directions, 3 * hidden_size, hidden_size]
, the recurrence weights for matrix multiplication, gate order: zrh. Required.6:
B
- 2D tensor of type T. If linear_before_reset is set to 1, then the shape is[num_directions, 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[num_directions, 3 * hidden_size]
, the sum of biases (weights and recurrence weights). Required.
Outputs
1:
Y
- 4D tensor of type T1[batch_size, num_directions, seq_len, hidden_size]
, concatenation of all the intermediate output values of the hidden.2:
Ho
- 3D tensor of type T1[batch_size, num_directions, hidden_size]
, the last output value of hidden state.
Types
T1: any supported floating-point type.
T2: any supported integer type.
Example
<layer ... type="GRUSequence" ...>
<data hidden_size="128"/>
<input>
<port id="0">
<dim>1</dim>
<dim>4</dim>
<dim>16</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
</port>
<port id="2">
<dim>1</dim>
</port>
<port id="3">
<dim>1</dim>
<dim>384</dim>
<dim>16</dim>
</port>
<port id="4">
<dim>1</dim>
<dim>384</dim>
<dim>128</dim>
</port>
<port id="5">
<dim>1</dim>
<dim>384</dim>
</port>
</input>
<output>
<port id="6">
<dim>1</dim>
<dim>1</dim>
<dim>4</dim>
<dim>128</dim>
</port>
<port id="7">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
</port>
</output>
</layer>