Class ov::op::util::RNNCellBase#

class RNNCellBase : public ov::op::Op#

Base class for all recurrent network cells.

Note

It holds all common attributes.

Subclassed by ov::op::internal::AUGRUCell, ov::op::internal::AUGRUSequence, ov::op::v0::LSTMCell, ov::op::v0::LSTMSequence, ov::op::v0::RNNCell, ov::op::v3::GRUCell, ov::op::v4::LSTMCell, ov::op::v5::GRUSequence, ov::op::v5::LSTMSequence, ov::op::v5::RNNSequence

Public Functions

RNNCellBase(const OutputVector &args, std::size_t hidden_size, float clip, const std::vector<std::string> &activations, const std::vector<float> &activations_alpha, const std::vector<float> &activations_beta)#

Constructs a RNNCellBase class.

Parameters:

  • hidden_size – [in] The number of hidden units for recurrent cell.

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

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

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

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

void validate_input_rank_dimension(const std::vector<PartialShape> &input)#

Validates static rank and dimension for provided input parameters. Additionally input_size dimension is checked for X and W inputs.

Parameters:

input – [in] Vector with RNN-Cell op inputs in following order: X, initial_hidden_state, W, R and B.