RNN Decoder

class textbox.module.Decoder.rnn_decoder.AttentionalRNNDecoder(embedding_size, hidden_size, context_size, num_dec_layers, rnn_type, dropout_ratio=0.0, attention_type='LuongAttention', alignment_method='concat')

Bases: Module

Attention-based Recurrent Neural Network (RNN) decoder.

forward(input_embeddings, hidden_states=None, encoder_outputs=None, encoder_masks=None, previous_probs=None)

Implement the attention-based decoding process.

Parameters

• input_embeddings (Torch.Tensor) – source sequence embedding, shape: [batch_size, sequence_length, embedding_size].

• hidden_states (Torch.Tensor) – initial hidden states, default: None.

• encoder_outputs (Torch.Tensor) – encoder output features, shape: [batch_size, sequence_length, hidden_size], default: None.

• encoder_masks (Torch.Tensor) – encoder state masks, shape: [batch_size, sequence_length], default: None.

Returns

• Torch.Tensor: output features, shape: [batch_size, sequence_length, num_directions * hidden_size].

• Torch.Tensor: hidden states, shape: [batch_size, num_layers * num_directions, hidden_size].

Return type

tuple

init_hidden(input_embeddings)

Initialize initial hidden states of RNN.

Parameters

input_embeddings (Torch.Tensor) – input sequence embedding, shape: [batch_size, sequence_length, embedding_size].

Returns

the initial hidden states.

Return type

Torch.Tensor

training: bool

class textbox.module.Decoder.rnn_decoder.BasicRNNDecoder(embedding_size, hidden_size, num_dec_layers, rnn_type, dropout_ratio=0.0)

Bases: Module

Basic Recurrent Neural Network (RNN) decoder.

forward(input_embeddings, hidden_states=None)

Implement the decoding process.

Parameters

• input_embeddings (Torch.Tensor) – target sequence embedding, shape: [batch_size, sequence_length, embedding_size].

• hidden_states (Torch.Tensor) – initial hidden states, default: None.

Returns

• Torch.Tensor: output features, shape: [batch_size, sequence_length, num_directions * hidden_size].

• Torch.Tensor: hidden states, shape: [num_layers * num_directions, batch_size, hidden_size].

Return type

tuple

init_hidden(input_embeddings)

Initialize initial hidden states of RNN.

Parameters

input_embeddings (Torch.Tensor) – input sequence embedding, shape: [batch_size, sequence_length, embedding_size].

Returns

the initial hidden states.

Return type

Torch.Tensor

training: bool

class textbox.module.Decoder.rnn_decoder.PointerRNNDecoder(vocab_size, embedding_size, hidden_size, context_size, num_dec_layers, rnn_type, dropout_ratio=0.0, is_attention=False, is_pgen=False, is_coverage=False)

Bases: Module

forward(input_embeddings, decoder_hidden_states, kwargs=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool