print("Data Path: ", NEG_DATA_PATH, POS_DATA_PATH)
print("Word2Vec Path: ", WORD2VEC_PATH)
print("Save Path: ", SAVE_PATH)
data_helper = DataHelper(config)
#### load the data ####
input_texts, target_texts, target_texts_inputs, classes = data_helper.read_txt_sentiment(
NEG_DATA_PATH, POS_DATA_PATH)
#### tokenize the inputs, outputs ####
input_sequences, word2idx_inputs, max_len_input = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### load word2vec pretrained model ####
word2vec = data_helper.load_word2vec(WORD2VEC_PATH)
#### create embedding matrix ####
embedding_matrix = data_helper.create_embedding_matrix(
word2vec, word2idx_inputs, WORD2VEC_PATH)
#### set data of model ####
model = ConvModel(config)
model.set_data(input_sequences, classes, max_len_input, word2idx_inputs)
model.set_embedding_matrix(embedding_matrix)
#### build model ####
model.build_model()
#### train model ####
model.train_model()
#### save model ####
model.save_model(SAVE_PATH)
#### load the data ####
input_texts, target_texts, target_texts_inputs = data_helper.read_txt_translation(
DATA_PATH)
#### tokenize the inputs, outputs ####
encoder_inputs, decoder_inputs, decoder_targets, \
word2idx_inputs, word2idx_outputs, \
max_len_input, max_len_target, num_words_output = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### load word2vec pretrained model ####
word2vec = data_helper.load_word2vec(WORD2VEC_PATH)
#### create embedding matrix ####
embedding_matrix = data_helper.create_embedding_matrix(
word2vec, word2idx_inputs)
#### set data of model ####
model = Seq2SeqAttnModel(config)
model.set_data(encoder_inputs, decoder_inputs, decoder_targets,
max_len_input, max_len_target, num_words_output,
word2idx_inputs, word2idx_outputs)
model.set_embedding_matrix(embedding_matrix)
#### build model ####
model.build_model()
#### train model ####
model.train_model()
#### save model ####
model.save_model(SAVE_PATH)