with open('vocab.json', 'w') as fp:
json.dump(vocab, fp)
with open('config.txt', 'w') as f:
f.write(str(vocab_size) + '\n')
f.write(str(max_length))
# import model
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model = CNN(sess=sess,
vocab_size=vocab_size,
sequence_length=max_length,
trainable=True)
model.embedding_assign(embedding)
batches = batch_iter(list(zip(x_input, y_input)),
batch_size=64,
num_epochs=5)
saver = tf.train.Saver(max_to_keep=3, keep_checkpoint_every_n_hours=0.5)
# train model
print('모델 훈련을 시작합니다.')
avgLoss = []
for step, batch in enumerate(batches):
x_train, y_train = zip(*batch)
x_train = sentence_to_index_morphs(x_train, vocab, max_length)
l, _ = model.train(x_train, y_train)
avgLoss.append(l)
if step % 500 == 0:
print('batch:', '%04d' % step, 'loss:', '%05f' % np.mean(avgLoss))
# save configuration
with open('config.txt', 'w') as f:
f.write(str(vocab_size) + '\n')
f.write(str(max_length))
# open session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# make model instance
model = CNN(sess=sess, vocab_size=vocab_size, sequence_length=max_length, trainable=True)
# assign pretrained embedding vectors
model.embedding_assign(embedding)
# make train batches
batches = batch_iter(list(zip(x_input, y_input)), batch_size=64, num_epochs=5)
# model saver
saver = tf.train.Saver(max_to_keep=3, keep_checkpoint_every_n_hours=0.5)
# train model
print('모델 훈련을 시작합니다.')
avgLoss = []
for step, batch in enumerate(batches):
x_train, y_train = zip(*batch)
x_train = sentence_to_index_morphs(x_train, vocab, max_length)
l, _ = model.train(x_train, y_train)
avgLoss.append(l)
