mwptoolkit.model.Graph2Tree.multiencdec

class mwptoolkit.model.Graph2Tree.multiencdec.MultiEncDec(config, dataset)

Bases: Module

Reference:

Shen et al. “Solving Math Word Problems with Multi-Encoders and Multi-Decoders” in COLING 2020.

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

attn_decoder_forward(encoder_outputs, seq_mask, decoder_hidden, num_stack, target=None, output_all_layers=False)

calculate_loss(batch_data: dict) → float

Finish forward-propagating, calculating loss and back-propagation.

Parameters

batch_data – one batch data.

Returns

loss value.

batch_data should include keywords ‘input1’, ‘input2’, ‘output1’, ‘output2’, ‘input1 len’, ‘parse graph’, ‘num stack’, ‘output1 len’, ‘output2 len’, ‘num size’, ‘num pos’, ‘num order’

convert_idx2symbol1(output, num_list, num_stack)

batch_size=1

convert_idx2symbol2(output, num_list, num_stack)

decoder_forward(encoder_outputs, problem_output, attn_decoder_hidden, all_nums_encoder_outputs, seq_mask, num_mask, num_stack, target1, target2, output_all_layers)

encoder_forward(input1, input2, input_length, parse_graph, num_pos, num_pos_pad, num_order_pad, output_all_layers=False)

forward(input1, input2, input_length, num_size, num_pos, num_order, parse_graph, num_stack, target1=None, target2=None, output_all_layers=False)

Parameters

• input1 (torch.Tensor) –

• input2 (torch.Tensor) –

• input_length (torch.Tensor) –

• num_size (list) –

• num_pos (list) –

• num_order (list) –

• parse_graph (torch.Tensor) –

• num_stack (list) –

• target1 (torch.Tensor | None) –

• target2 (torch.Tensor | None) –

• output_all_layers (bool) –

Returns

generate_decoder_input(target, decoder_output, nums_stack_batch, num_start, unk)

generate_tree_input(target, decoder_output, nums_stack_batch, num_start, unk)

get_all_number_encoder_outputs(encoder_outputs, num_pos, batch_size, num_size, hidden_size)

model_test(batch_data: dict) → Tuple[str, list, list]

Model test.

Parameters

batch_data – one batch data.

Returns

result_type, predicted equation, target equation.

batch_data should include keywords ‘input1’, ‘input2’, ‘output1’, ‘output2’, ‘input1 len’, ‘parse graph’, ‘num stack’, ‘num pos’, ‘num order’, ‘num list’

predict(batch_data, output_all_layers=False)

training: bool

tree_decoder_forward(encoder_outputs, problem_output, all_nums_encoder_outputs, nums_stack, seq_mask, num_mask, target=None, output_all_layers=False)