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()