def train(train_path=None,
save_model_path=None,
save_input_token_path=None,
save_target_token_path=None,
batch_size=64,
epochs=10,
rnn_hidden_dim=200):
print('Training model...')
data_reader = CGEDReader(train_path)
input_texts, target_texts = data_reader.build_dataset(train_path)
print('input_texts:', input_texts[0])
print('target_texts:', target_texts[0])
input_characters = data_reader.read_vocab(input_texts)
target_characters = data_reader.read_vocab(target_texts)
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_len = max([len(text) for text in input_texts])
max_decoder_seq_len = max([len(text) for text in target_texts])
print('num of samples:', len(input_texts))
print('num of unique input tokens:', num_encoder_tokens)
print('num of unique output tokens:', num_decoder_tokens)
print('max sequence length for inputs:', max_encoder_seq_len)
print('max sequence length for outputs:', max_decoder_seq_len)
input_token_index = dict([(char, i)
for i, char in enumerate(input_characters)])
target_token_index = dict([(char, i)
for i, char in enumerate(target_characters)])
# save word dict
save_word_dict(input_token_index, save_input_token_path)
save_word_dict(target_token_index, save_target_token_path)
encoder_input_data = np.zeros(
(len(input_texts), max_encoder_seq_len, num_encoder_tokens),
dtype='float32')
decoder_input_data = np.zeros(
(len(input_texts), max_decoder_seq_len, num_decoder_tokens),
dtype='float32')
decoder_target_data = np.zeros(
(len(input_texts), max_decoder_seq_len, num_decoder_tokens),
dtype='float32')
# one hot representation
for i, (input_text,
target_text) in enumerate(zip(input_texts, target_texts)):
for t, char in enumerate(input_text):
encoder_input_data[i, t, input_token_index[char]] = 1.0
for t, char in enumerate(target_text):
# decoder_target_data is a head of decoder_input_data by one timestep
decoder_input_data[i, t, target_token_index[char]] = 1.0
if t > 0:
decoder_target_data[i, t - 1, target_token_index[char]] = 1.0
logger.info("Data loaded.")
# model
logger.info("Training seq2seq model...")
model = create_model(num_encoder_tokens, num_decoder_tokens,
rnn_hidden_dim)
# save
callbacks_list = callback(save_model_path, logger)
model.fit([encoder_input_data, decoder_input_data],
decoder_target_data,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks_list)
logger.info("Model save to " + save_model_path)
logger.info("Training has finished.")
eval(num_encoder_tokens, num_decoder_tokens, rnn_hidden_dim,
input_token_index, target_token_index, max_decoder_seq_len,
encoder_input_data, input_texts)
def train(train_path=None,
save_model_path=None,
encoder_model_path=None,
decoder_model_path=None,
save_input_token_path=None,
save_target_token_path=None,
batch_size=64,
epochs=10,
rnn_hidden_dim=200):
print('Training model...')
data_reader = CGEDReader(train_path)
input_texts, target_texts = data_reader.build_dataset(train_path)
print('input_texts:', input_texts[0])
print('target_texts:', target_texts[0])
max_input_texts_len = max([len(text) for text in input_texts])
max_target_texts_len = max([len(text) for text in target_texts])
print('num of samples:', len(input_texts))
print('max sequence length for inputs:', max_input_texts_len)
print('max sequence length for outputs:', max_target_texts_len)
# load or save word dict
if os.path.exists(save_input_token_path) and os.path.exists(
save_target_token_path):
input_token_index = load_word_dict(save_input_token_path)
target_token_index = load_word_dict(save_target_token_path)
else:
input_characters = data_reader.read_vocab(input_texts)
target_characters = data_reader.read_vocab(target_texts)
input_token_index = dict([(char, i)
for i, char in enumerate(input_characters)])
target_token_index = dict([
(char, i) for i, char in enumerate(target_characters)
])
save_word_dict(input_token_index, save_input_token_path)
save_word_dict(target_token_index, save_target_token_path)
encoder_input_data = np.zeros(
(len(input_texts), max_input_texts_len, len(input_token_index)),
dtype='float32')
decoder_input_data = np.zeros(
(len(input_texts), max_target_texts_len, len(target_token_index)),
dtype='float32')
decoder_target_data = np.zeros(
(len(input_texts), max_target_texts_len, len(target_token_index)),
dtype='float32')
# one hot representation
for i, (input_text,
target_text) in enumerate(zip(input_texts, target_texts)):
for t, char in enumerate(input_text):
if char in input_token_index:
encoder_input_data[i, t, input_token_index[char]] = 1.0
for t, char in enumerate(target_text):
# decoder_target_data is a head of decoder_input_data by one timestep
if char in target_token_index:
decoder_input_data[i, t, target_token_index[char]] = 1.0
if t > 0:
decoder_target_data[i, t - 1,
target_token_index[char]] = 1.0
logger.info("Data loaded.")
# model
logger.info("Training seq2seq model...")
if os.path.exists(save_model_path) and os.path.exists(encoder_model_path):
model = load_model(save_model_path)
encoder_model = load_model(encoder_model_path)
decoder_model = load_model(decoder_model_path)
else:
model, encoder_model, decoder_model = create_model(
len(input_token_index), len(target_token_index), rnn_hidden_dim)
# Run training
callbacks_list = callback(save_model_path, logger)
model.fit(x=[encoder_input_data, decoder_input_data],
y=decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.1,
callbacks=callbacks_list)
encoder_model.save(encoder_model_path)
decoder_model.save(decoder_model_path)
logger.info("Model save to " + save_model_path)
logger.info("Training has finished.")
evaluate(encoder_model, decoder_model, len(input_token_index),
len(target_token_index), rnn_hidden_dim, target_token_index,
max_target_texts_len, encoder_input_data, input_texts)