openvino.runtime.opset9.rnn_cell

openvino.runtime.opset9.rnn_cell(X: Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], initial_hidden_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: List[str], activations_alpha: List[float], activations_beta: List[float], clip: float = 0.0, name: Optional[str] = None) → openvino._pyopenvino.Node

Perform RNNCell operation on tensor from input node.

It follows notation and equations defined as in ONNX standard: https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN

Note this class represents only single cell and not whole RNN layer.

Parameters

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

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

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

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

• B – The sum of biases (weight and recurrence) with shape: [hidden_size].

• hidden_size – The number of hidden units for recurrent cell. Specifies hidden state size.

• activations – The vector of activation functions used inside recurrent cell.

• activation_alpha – The vector of alpha parameters for activation functions in order respective to activation list.

• activation_beta – The vector of beta parameters for activation functions in order respective to activation list.

• clip – The value defining clipping range [-clip, clip] on input of activation functions.

• name – Optional output node name.

Returns

The new node performing a RNNCell operation on tensor from input node.