def __init__(self):
self.config = TCNNConfig()
self.categories, self.cat_to_id = read_category()
self.words, self.word_to_id = read_vocab(vocab_dir)
self.config.vocab_size = len(self.words)
self.model = TextCNN(self.config)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=self.session, save_path=save_path) # 读取保存的模型
def __init__(self):
self.config = BILSTMConfig()
self.categories, self.cat_to_id = read_category()
self.words = np.load('./datas/dict.npy')
self.word_to_id = np.load('./datas/dict.npy').tolist()
self.config.vocab_size = len(self.word_to_id.keys())
self.model = BILSTMModel(self.config)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=self.session, save_path=save_path) # 读取保存的模型
def __init__(self):
self.config = TCNNConfig()
self.categories, self.cat_to_id = read_category()
self.words = np.load('./datas/all_phrase.npy')
self.word_to_id = np.load('./datas/phrase_to_id.npy').tolist()
self.config.vocab_size = len(self.words)
self.model = TextCNN(self.config)
self.session = tf.Session()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=self.session, save_path=save_path) # 读取保存的模型
logger.info(
metrics.classification_report(y_test_cls,
y_pred_cls,
target_names=categories))
# 混淆矩阵
logger.info("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
logger.info(cm)
time_dif = get_time_dif(start_time)
logger.info("Time usage:", time_dif)
if __name__ == '__main__':
config = TCNNConfig()
#if not os.path.exists(vocab_dir): # 如果不存在词汇表，重建
# build_vocab(train_dir, vocab_dir, config.vocab_size)
categories, cat_to_id = read_category()
#words, word_to_id = read_vocab(vocab_dir)
words = np.load('./datas/dict_token.npy')
word_to_id = np.load('./datas/token_to_id.npy').tolist()
config.vocab_size = len(words)
model = TextCNN(config)
option = 'train'
if option == 'train':
train()
else:
test()
