mwptoolkit.module.Layer.layers

class mwptoolkit.module.Layer.layers.GenVar(dim_encoder_state, dim_context, dim_attn_hidden=256, dropout_rate=0.5)

Bases: Module

Module to generate variable embedding.

Parameters

• dim_encoder_state (int) – Dimension of the last cell state of encoder RNN (output of Encoder module).

• dim_context (int) – Dimension of RNN in GenVar module.

• dim_attn_hidden (int) – Dimension of hidden layer in attention.

• dim_mlp_hiddens (int) – Dimension of hidden layers in the MLP that transform encoder state to query of attention.

• dropout_rate (int) – Dropout rate for attention and MLP.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(encoder_state, context, context_lens)

Generate embedding for an unknown variable.

Parameters

• encoder_state (torch.FloatTensor) – Last cell state of the encoder (output of Encoder module).

• context (torch.FloatTensor) – Encoded context, with size [batch_size, text_len, dim_hidden].

Returns

Embedding of an unknown variable, with size [batch_size, dim_context]

Return type

torch.FloatTensor

training: bool

class mwptoolkit.module.Layer.layers.Transformer(dim_hidden)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(top2)

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

class mwptoolkit.module.Layer.layers.TreeAttnDecoderRNN(hidden_size, embedding_size, input_size, output_size, n_layers=2, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input_seq, last_hidden, encoder_outputs, seq_mask)

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