train_files = '../data/cnews.train.txt'
val_files = '../data/cnews.val.txt'
test_files = '../data/cnews.test.txt'
save_file = 'cnews.vocab_label.pkl'
# 数据处理
converter = TextConverter(train_files,
save_file,
max_vocab=Config.vocab_size,
seq_length=Config.seq_length)
print('vocab size:', converter.vocab_size)
print('labels:', converter.label)
train_texts, train_labels = converter.load_data(train_files)
train_x, train_x_len, train_y = converter.texts_to_arr(
train_texts, train_labels)
val_texts, val_labels = converter.load_data(val_files)
val_x, val_x_len, val_y = converter.texts_to_arr(val_texts, val_labels)
# 产生训练样本
train_g = converter.batch_generator(train_x, train_x_len, train_y,
Config.batch_size)
val_g = converter.val_samples_generator(val_x, val_x_len, val_y,
Config.batch_size)
model = Model(Config)
# 加载上一次保存的模型
checkpoint_path = tf.train.latest_checkpoint(model_path)
if checkpoint_path:
train_files = '../data/cnews.train.txt'
val_files = '../data/cnews.val.txt'
test_files = '../data/cnews.test.txt'
save_file = 'cnews.vocab_label.pkl'
# 数据处理
converter = TextConverter(train_files,
save_file,
max_vocab=Config.vocab_size,
seq_length=Config.seq_length)
print('vocab size:', converter.vocab_size)
print('labels:', converter.label)
test_texts, test_labels = converter.load_data(test_files)
test_x, test_x_len, test_y = converter.texts_to_arr(
test_texts, test_labels)
test_g = converter.val_samples_generator(test_x, test_x_len, test_y,
Config.batch_size)
model = Model(Config)
# 加载上一次保存的模型
checkpoint_path = tf.train.latest_checkpoint(model_path)
if checkpoint_path:
model.load(checkpoint_path)
print('start to testing...')
model.test(test_g)
