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)

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_sizeint, required

The dimension of the inputs to the LSTM.

hidden_sizeint, required

The dimension of the outputs of the LSTM.

num_layersint, 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_biasbool, 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.

Returns

output_accumulatorPackedSequence

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(self, inputs: torch.nn.utils.rnn.PackedSequence, initial_state: Union[Tuple[torch.Tensor, torch.Tensor], NoneType] = None) → Tuple[Union[torch.Tensor, torch.nn.utils.rnn.PackedSequence], Tuple[torch.Tensor, torch.Tensor]]

Parameters

inputsPackedSequence, required.

A batch first PackedSequence to run the stacked LSTM over.

initial_stateTuple[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).

Returns

output_sequencePackedSequence

The encoded sequence of shape (batch_size, sequence_length, hidden_size)

final_states: Tuple[torch.Tensor, torch.Tensor]

The per-layer final (state, memory) states of the LSTM, each with shape (num_layers, batch_size, hidden_size).