# allennlp.modules.stacked_alternating_lstm¶

A stacked LSTM with LSTM layers which alternate between going forwards over the sequence and going backwards.

class allennlp.modules.stacked_alternating_lstm.StackedAlternatingLstm(input_size: int, hidden_size: int, num_layers: int, recurrent_dropout_probability: float = 0.0, use_highway: bool = True, use_input_projection_bias: bool = True) → None[source]

Bases: torch.nn.modules.module.Module

A stacked LSTM with LSTM layers which alternate between going forwards over the sequence and going backwards. This implementation is based on the description in Deep Semantic Role Labelling - What works and what’s next .

Parameters: input_size : int, required The dimension of the inputs to the LSTM. hidden_size : int, required The dimension of the outputs of the LSTM. num_layers : int, required The number of stacked LSTMs to use. recurrent_dropout_probability: float, optional (default = 0.0) The dropout probability to be used in a dropout scheme as stated in A Theoretically Grounded Application of Dropout in Recurrent Neural Networks . use_input_projection_bias : bool, optional (default = True) Whether or not to use a bias on the input projection layer. This is mainly here for backwards compatibility reasons and will be removed (and set to False) in future releases. output_accumulator : PackedSequence The outputs of the interleaved LSTMs per timestep. A tensor of shape (batch_size, max_timesteps, hidden_size) where for a given batch element, all outputs past the sequence length for that batch are zero tensors.
forward(inputs: torch.nn.utils.rnn.PackedSequence, initial_state: typing.Union[typing.Tuple[torch.FloatTensor, torch.FloatTensor], NoneType] = None)[source]
Parameters: inputs : PackedSequence, required. A batch first PackedSequence to run the stacked LSTM over. initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None) A tuple (state, memory) representing the initial hidden state and memory of the LSTM. Each tensor has shape (1, batch_size, output_dimension). output_sequence : PackedSequence The encoded sequence of shape (batch_size, sequence_length, hidden_size) final_states: torch.Tensor The per-layer final (state, memory) states of the LSTM, each with shape (num_layers, batch_size, hidden_size).