openvino.runtime.opset6.rnn_sequence#
- openvino.runtime.opset6.rnn_sequence(X: Node | int | float | ndarray, initial_hidden_state: Node | int | float | ndarray, sequence_lengths: Node | int | float | ndarray, W: Node | int | float | ndarray, R: Node | int | float | ndarray, B: Node | int | float | ndarray, hidden_size: int, direction: str, activations: List[str] | None = None, activations_alpha: List[float] | None = None, activations_beta: List[float] | None = None, clip: float = 0.0, name: str | None = None) Node #
Return a node which performs RNNSequence operation.
- Parameters:
X – The input tensor. Shape: [batch_size, seq_length, input_size].
initial_hidden_state – The hidden state tensor. Shape: [batch_size, num_directions, hidden_size].
sequence_lengths – Specifies real sequence lengths for each batch element. Shape: [batch_size]. Integer type.
W – Tensor with weights for matrix multiplication operation with input portion of data. Shape: [num_directions, hidden_size, input_size].
R – The tensor with weights for matrix multiplication operation with hidden state. Shape: [num_directions, hidden_size, hidden_size].
B – The sum of biases (weight and recurrence). Shape: [num_directions, hidden_size].
hidden_size – Specifies hidden state size.
direction – Specifies if the RNN is forward, reverse, or bidirectional.
activations – The list of three activation functions for gates.
activations_alpha – The list of alpha parameters for activation functions.
activations_beta – The list of beta parameters for activation functions.
clip – Specifies bound values [-C, C] for tensor clipping performed before activations.
name – An optional name of the output node.
- Returns:
The new node represents RNNSequence. Node outputs count: 2.