openvino.runtime.opset12.lstm_cell

openvino.runtime.opset12.lstm_cell(X: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], initial_hidden_state: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], initial_cell_state: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], W: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], R: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], B: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], hidden_size: int, activations: Optional[List[str]] = None, activations_alpha: Optional[List[float]] = None, activations_beta: Optional[List[float]] = None, clip: float = 0.0, name: Optional[str] = None) → openvino._pyopenvino.Node

Return a node which performs LSTMCell operation.

Parameters

• X – The input tensor with shape: [batch_size, input_size].

• initial_hidden_state – The hidden state tensor with shape: [batch_size, hidden_size].

• initial_cell_state – The cell state tensor with shape: [batch_size, hidden_size].

• W – The weight tensor with shape: [4*hidden_size, input_size].

• R – The recurrence weight tensor with shape: [4*hidden_size, hidden_size].

• B – The bias tensor for gates with shape: [4*hidden_size].

• hidden_size – Specifies hidden state size.

• 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 LSTMCell. Node outputs count: 2.