def main(_):
""" """
vocab_word2index, _ = create_or_load_vocabulary(FLAGS.data_path, \
FLAGS.mask_lm_source_file, FLAGS.vocab_size, \
test_mode=FLAGS.test_mode, tokenize_style=FLAGS.tokenize_style)
vocab_size = len(vocab_word2index)
logging.info("bert pretrain vocab size: %d" % vocab_size)
index2word = {v: k for k, v in vocab_word2index.items()}
train, valid, test = mask_language_model(FLAGS.mask_lm_source_file, \
FLAGS.data_path, index2word, max_allow_sentence_length= \
FLAGS.max_allow_sentence_length, test_mode=FLAGS.test_mode, \
process_num=FLAGS.process_num)
train_X, train_y, train_p = train
valid_X, valid_y, valid_p = valid
test_X, test_y, test_p = test
print("train_X:{}, train_y:{}, train_p:{}".format(train_X.shape, \
train_y.shape, train_p.shape))
#1.create session
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True
def main(_):
vocab_word2index, _ = create_or_load_vocabulary(
FLAGS.data_path,
FLAGS.mask_lm_source_file,
FLAGS.vocab_size,
test_mode=FLAGS.test_mode,
tokenize_style=FLAGS.tokenize_style)
vocab_size = len(vocab_word2index)
print("bert_pertrain_lm.vocab_size:", vocab_size)
index2word = {v: k for k, v in vocab_word2index.items()}
#train,valid,test=mask_language_model(FLAGS.mask_lm_source_file,FLAGS.data_path,index2word,max_allow_sentence_length=FLAGS.max_allow_sentence_length,test_mode=FLAGS.test_mode)
train, valid, test = mask_language_model(
FLAGS.mask_lm_source_file,
FLAGS.data_path,
index2word,
max_allow_sentence_length=FLAGS.max_allow_sentence_length,
test_mode=FLAGS.test_mode,
process_num=FLAGS.process_num)
train_X, train_y, train_p = train
valid_X, valid_y, valid_p = valid
test_X, test_y, test_p = test
print("length of training data:", train_X.shape, ";train_Y:",
train_y.shape, ";train_p:", train_p.shape, ";valid data:",
valid_X.shape, ";test data:", test_X.shape)
# 1.create session.
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True
with tf.Session(config=gpu_config) as sess:
#Instantiate Model
config = set_config(FLAGS, vocab_size, vocab_size)
model = BertModel(config)
#Initialize Save
saver = tf.train.Saver()
if os.path.exists(FLAGS.ckpt_dir + "checkpoint"):
print("Restoring Variables from Checkpoint.")
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.ckpt_dir))
for i in range(2): #decay learning rate if necessary.
print(i, "Going to decay learning rate by half.")
sess.run(model.learning_rate_decay_half_op)
else:
print('Initializing Variables')
sess.run(tf.global_variables_initializer())
if FLAGS.use_pretrained_embedding:
vocabulary_index2word = {
index: word
for word, index in vocab_word2index.items()
}
assign_pretrained_word_embedding(
sess, vocabulary_index2word, vocab_size,
FLAGS.word2vec_model_path, model.embedding,
config.d_model) # assign pretrained word embeddings
curr_epoch = sess.run(model.epoch_step)
# 2.feed data & training
number_of_training_data = len(train_X)
print("number_of_training_data:", number_of_training_data)
batch_size = FLAGS.batch_size
iteration = 0
score_best = -100
for epoch in range(curr_epoch, FLAGS.num_epochs):
loss_total_lm, counter = 0.0, 0
for start, end in zip(
range(0, number_of_training_data, batch_size),
range(batch_size, number_of_training_data, batch_size)):
iteration = iteration + 1
if epoch == 0 and counter == 0:
print("trainX[start:end]:", train_X[start:end],
"train_X.shape:", train_X.shape)
feed_dict = {
model.x_mask_lm: train_X[start:end],
model.y_mask_lm: train_y[start:end],
model.p_mask_lm: train_p[start:end],
model.dropout_keep_prob: FLAGS.dropout_keep_prob
}
current_loss_lm, lr, l2_loss, _ = sess.run([
model.loss_val_lm, model.learning_rate, model.l2_loss_lm,
model.train_op_lm
], feed_dict)
loss_total_lm, counter = loss_total_lm + current_loss_lm, counter + 1
if counter % 30 == 0:
print(
"%d\t%d\tLearning rate:%.5f\tLoss_lm:%.3f\tCurrent_loss_lm:%.3f\tL2_loss:%.3f\t"
% (epoch, counter, lr, float(loss_total_lm) /
float(counter), current_loss_lm, l2_loss))
if start != 0 and start % (800 *
FLAGS.batch_size) == 0: # epoch!=0
loss_valid, acc_valid = do_eval(sess, model, valid,
batch_size)
print(
"%d\tValid.Epoch %d ValidLoss:%.3f\tAcc_valid:%.3f\t" %
(counter, epoch, loss_valid, acc_valid * 100))
# save model to checkpoint
if acc_valid > score_best:
save_path = FLAGS.ckpt_dir + "model.ckpt"
print("going to save check point.")
saver.save(sess, save_path, global_step=epoch)
score_best = acc_valid
sess.run(model.epoch_increment)
