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]]}')