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)
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)