openvino.runtime.opset9.rnn_cell#

openvino.runtime.opset9.rnn_cell(X: Node | int | float | ndarray, initial_hidden_state: Node | int | float | ndarray, W: Node | int | float | ndarray, R: Node | int | float | ndarray, B: Node | int | float | ndarray, hidden_size: int, activations: List[str], activations_alpha: List[float], activations_beta: List[float], clip: float = 0.0, name: str | None = None) → Node#

Perform RNNCell operation on tensor from input node.

It follows notation and equations defined as in ONNX standard: onnx/onnx

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.