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
int
float[]
float
num_directions = 1
, if it is bidirectional, then num_directions = 2
. This num_directions
value specifies input/output shape requirements.string
boolean
Inputs
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 size seq_length
. Required.initial_hidden_state
- 3D tensor of type T1 [batch_size, num_directions, hidden_size]
, input hidden state data. Required.sequence_lengths
- 1D tensor of type T2 [batch_size]
, specifies real sequence lengths for each batch element. Required.W
- 3D tensor of type T1 [num_directions, 3 * hidden_size, input_size]
, the weights for matrix multiplication, gate order: zrh. Required.R
- 3D tensor of type T1 [num_directions, 3 * hidden_size, hidden_size]
, the recurrence weights for matrix multiplication, gate order: zrh. Required.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
Y
- 4D tensor of type T1 [batch_size, num_directions, seq_len, hidden_size]
, concatenation of all the intermediate output values of the hidden.Ho
- 3D tensor of type T1 [batch_size, num_directions, hidden_size]
, the last output value of hidden state.Types
Example