def test():
model_path = os.path.join(MODEL_PATH, FLAGS.DEMO, 'checkpoints')
ckpt_file = tf.train.latest_checkpoint(model_path)
logging.info("load model from {}".format(ckpt_file))
textCNN = TextAttRNN(config=cfg(),
model_path=ckpt_file,
vocab=word2int,
tag2label=tag2label,
eopches=FLAGS.epoches)
saver = tf.compat.v1.train.Saver()
with tf.compat.v1.Session(config=cfg()) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
inps = ['卡布奇诺瑞纳冰已经胜过我爱的星爸爸',
'非常好，店长给我耐心的推荐不同口味，skr', '比app通知的时间晚太多做好',
'可以，还可以更好！', '巧克力有点腻', '太冰了没有加热', '商品未送到提前点击已送达，且无电话通知。']
results = textCNN.predict(sess, inps)
probs = textCNN.predict_prob(sess, inps)
for inp, r, prob in zip(inps, results, probs):
print("\n{}".format(inp))
for idx, p in enumerate(prob):
print("\t{} -> {}".format(int2tag[idx], p))
print("\tTag: {}".format(int2tag[r]))
def demo():
model_path = os.path.join(MODEL_PATH, FLAGS.DEMO, 'checkpoints')
ckpt_file = tf.train.latest_checkpoint(model_path)
logging.info("load model from {}".format(ckpt_file))
textCNN = TextAttRNN(config=cfg(),
model_path=ckpt_file,
vocab=word2int,
tag2label=tag2label,
eopches=FLAGS.epoches)
saver = tf.compat.v1.train.Saver()
with tf.compat.v1.Session(config=cfg()) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while True:
print('Please input your sentence:')
inp = input()
if inp == '' or inp.isspace():
print('See you next time!')
break
else:
inps = [inp.strip()]
pred = textCNN.predict(sess, inps)[0]
probs = textCNN.predict_prob(sess, inps)[0]
print("\n{}".format(inps))
for idx, prob in enumerate(probs):
print("\t{} -> {}".format(int2tag[idx], prob))
print("\tTag: {}".format(int2tag[pred]))
def train():
train, dev = read_corpus(filename='emergency_train.tsv')
textAttRNN = TextAttRNN(config=cfg(),
model_path=os.path.join(MODEL_PATH, FLAGS.DEMO),
vocab=word2int,
tag2label=tag2label,
batch_size=FLAGS.batch_size,
embed_size=FLAGS.embed_size,
sequence_length=FLAGS.sequence_length,
eopches=FLAGS.epoches)
with tf.compat.v1.Session(config=cfg()) as sess:
textAttRNN.train(sess, train, dev, shuffle=True)
def train():
iter = -1
iter_size = 20000
train, dev = read_corpus(random_state=1234, separator='\t', iter=iter, iter_size=iter_size)
textCNN = TextAttRNN(config=cfg(),
model_path=os.path.join(MODEL_PATH, FLAGS.DEMO),
vocab=word2int,
tag2label=tag2label,
batch_size=FLAGS.batch_size,
embed_size=FLAGS.embed_size,
eopches=FLAGS.epoches)
with tf.compat.v1.Session(config=cfg()) as sess:
textCNN.train(sess, train, dev, shuffle=True)
def re_train():
train, dev = read_corpus(filename='emergency_train.tsv', test_size=0.2)
model_path = os.path.join(MODEL_PATH, FLAGS.DEMO, 'checkpoints')
ckpt_file = tf.train.latest_checkpoint(model_path)
logging.info("load pre-train model from {}".format(ckpt_file))
textAttRNN = TextAttRNN(
config=cfg(),
model_path=ckpt_file,
vocab=word2int,
tag2label=tag2label,
batch_size=FLAGS.batch_size,
embed_size=FLAGS.embed_size,
sequence_length=FLAGS.sequence_length,
eopches=FLAGS.epoches,
)
saver = tf.compat.v1.train.Saver()
with tf.compat.v1.Session(config=cfg()) as sess:
saver.restore(sess, ckpt_file)
textAttRNN.set_model_path(
model_path=os.path.join(MODEL_PATH, FLAGS.DEMO))
textAttRNN.train(sess, train, dev, shuffle=True, re_train=True)