def test(): _, _, _, sentence_size, vocab_size = build_corpus() v2i, _ = build_vocab() _, i2l = build_label() origin_questions = ['今天 天气 不错', '介绍 贵金属 产品'] questions = [q.split() for q in origin_questions] questions = [[v2i[vocab] for vocab in ques if vocab in v2i] for ques in questions] config = tf.ConfigProto() with tf.Session(config=config) as sess: model = Model(sentence_size, vocab_size, FLAGS.embed_size, FLAGS.class_num, FLAGS.learning_rate, FLAGS.decay_step, FLAGS.decay_rate, FLAGS.layer_size, FLAGS.multi_channel_size) saver = tf.train.Saver() saver.restore(sess, tf.train.latest_checkpoint(FLAGS.check_point)) questions = pad_sequences(questions, maxlen=sentence_size, value=0) feed_dict = { model.encoder_input: questions, model.batch_size: FLAGS.batch_size } p = sess.run([model.predict], feed_dict=feed_dict) p = p[0].tolist() for index in range(len(questions)): print(f'{origin_questions[index]} is_business: {i2l[p[index]]}')
def test(): v2i, _ = build_vocab() _, i2l = build_label() origin_questions = ['今天 天气 不错', '介绍 贵金属 产品'] questions = [q.split() for q in origin_questions] questions = [[v2i[vocab] for vocab in ques if vocab in v2i] for ques in questions] with tf.Session() as sess: saver = tf.train.import_meta_graph(checkpoint_path + model_name) saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path)) model = tf.get_default_graph() x = model.get_tensor_by_name("x:0") predict = model.get_tensor_by_name("predictions:0") questions = pad_sequences(questions, maxlen=x.shape[1], value=0) feed_dict = {x: questions} p = sess.run([predict], feed_dict=feed_dict) p = p[0].tolist() for index in range(len(questions)): print(f'{origin_questions[index]} is_business: {i2l[p[index]]}')