def __init__(self, params, input_size, output_embedder, model,
token_predictor):
self.lstms = du.create_multilayer_lstm_params(
params.decoder_num_layers, input_size, params.decoder_state_size,
model, "LSTM-d")
self.token_predictor = token_predictor
self.output_embedder = output_embedder
self.start_token_embedding = du.add_params(
model, (params.output_embedding_size, ), "y-0")
def __init__(
self,
params,
input_vocabulary,
output_vocabulary,
anonymizer):
self.params = params
self._pc = dy.ParameterCollection()
if params.new_version:
self.controller = Controller(output_vocabulary)
else:
self.controller = None
# Create the input embeddings
self.input_embedder = Embedder(self._pc,
params.input_embedding_size,
name="input-embedding",
vocabulary=input_vocabulary,
anonymizer=anonymizer)
# Create the output embeddings
self.output_embedder = Embedder(self._pc,
params.output_embedding_size,
name="output-embedding",
vocabulary=output_vocabulary,
anonymizer=anonymizer)
# Create the encoder
encoder_input_size = params.input_embedding_size
if params.discourse_level_lstm:
encoder_input_size += params.encoder_state_size / 2
self.utterance_encoder = Encoder(params.encoder_num_layers,
encoder_input_size,
params.encoder_state_size,
self._pc)
# Positional embedder for utterances
attention_key_size = params.encoder_state_size
if params.state_positional_embeddings:
attention_key_size += params.positional_embedding_size
self.positional_embedder = Embedder(
self._pc,
params.positional_embedding_size,
name="positional-embedding",
num_tokens=params.maximum_utterances)
# Create the discourse-level LSTM parameters
if params.discourse_level_lstm:
self.discourse_lstms = du.create_multilayer_lstm_params(
1, params.encoder_state_size, params.encoder_state_size / 2, self._pc, "LSTM-t")
self.initial_discourse_state = du.add_params(self._pc, tuple(
[params.encoder_state_size / 2]), "V-turn-state-0")
# Snippet encoder
final_snippet_size = 0
if params.use_snippets and not params.previous_decoder_snippet_encoding:
snippet_encoding_size = int(params.encoder_state_size / 2)
final_snippet_size = params.encoder_state_size
if params.snippet_age_embedding:
snippet_encoding_size -= int(
params.snippet_age_embedding_size / 4)
self.snippet_age_embedder = Embedder(
self._pc,
params.snippet_age_embedding_size,
name="snippet-age-embedding",
num_tokens=params.max_snippet_age_embedding)
final_snippet_size = params.encoder_state_size \
+ params.snippet_age_embedding_size / 2
self.snippet_encoder = Encoder(params.snippet_num_layers,
params.output_embedding_size,
snippet_encoding_size,
self._pc)
token_predictor = construct_token_predictor(self._pc,
params,
output_vocabulary,
attention_key_size,
final_snippet_size,
anonymizer)
# 注意：此处在input增加了decoder_state_size维度
self.decoder = SequencePredictor(
params,
params.output_embedding_size +
attention_key_size + params.decoder_state_size,
self.output_embedder,
self._pc,
token_predictor)
self.trainer = dy.AdamTrainer(
self._pc, alpha=params.initial_learning_rate)
self.dropout = 0.
def __init__(self, num_layers, input_size, state_size, model):
self.num_layers = num_layers
self.forward_lstms = create_multilayer_lstm_params( \
self.num_layers, input_size, state_size / 2, model, "LSTM-ef")
self.backward_lstms = create_multilayer_lstm_params( \
self.num_layers, input_size, state_size / 2, model, "LSTM-eb")