def train(self, x_train, y_train, x_val, y_val):
x_train = get_bert_param_lists(x_train)
x_val = get_bert_param_lists(x_val)
y_train = np.asarray(y_train)
y_val = np.asarray(y_val)
data_len = len(y_train)
step_sum = (int((data_len - 1) / config.batch_size) + 1) * config.epochs_num
best_acc_val = 0
cur_step = 0
last_improved_step = 0
adjust_num = 0
flag = True
saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(config.epochs_num):
for batch_x_train, batch_y_train in bert_bacth_iter(x_train, y_train, config.batch_size):
input_ids = batch_x_train[0]
input_masks = batch_x_train[1]
segment_ids = batch_x_train[2]
feed_dict = {
self.input_ids: input_ids,
self.input_masks: input_masks,
self.segment_ids: segment_ids,
self.labels: batch_y_train,
self.is_training: True,
}
cur_step += 1
fetches = [self.train_op, self.global_step]
sess.run(fetches, feed_dict=feed_dict)
if cur_step % config.print_per_batch == 0:
fetches = [self.loss, self.accuracy]
loss_train, acc_train = sess.run(fetches, feed_dict=feed_dict)
loss_val, acc_val = self.evaluate(sess, x_val, y_val)
if acc_val >= best_acc_val:
best_acc_val = acc_val
last_improved_step = cur_step
saver.save(sess, model_config.model_save_path)
improved_str = '*'
else:
improved_str = ''
cur_step_str = str(cur_step) + "/" + str(step_sum)
msg = 'the Current step: {0}, Train Loss: {1:>6.2}, Train Acc: {2:>7.2%},' \
+ ' Val Loss: {3:>6.2}, Val Acc: {4:>7.2%}, {5}'
print(msg.format(cur_step_str, loss_train, acc_train, loss_val, acc_val, improved_str))
if cur_step - last_improved_step >= config.improvement_step:
last_improved_step = cur_step
print("No optimization for a long time, auto adjust learning_rate...")
# learning_rate = learning_rate_decay(learning_rate)
adjust_num += 1
if adjust_num > 3:
print("No optimization for a long time, auto-stopping...")
flag = False
if not flag:
break
if not flag:
break
def evaluate(self, sess, x_val, y_val):
"""评估在某一数据上的准确率和损失"""
data_len = len(y_val)
total_loss = 0.0
total_acc = 0.0
for batch_x_val, batch_y_val in bert_bacth_iter(x_val, y_val, config.batch_size):
input_ids = batch_x_val[0]
input_masks = batch_x_val[1]
segment_ids = batch_x_val[2]
feed_dict = {
self.input_ids: input_ids,
self.input_masks: input_masks,
self.segment_ids: segment_ids,
self.labels: batch_y_val,
self.is_training: False,
}
batch_len = len(batch_y_val)
loss, acc = sess.run([self.loss, self.accuracy], feed_dict=feed_dict)
total_loss += loss * batch_len
total_acc += acc * batch_len
return total_loss / data_len, total_acc / data_len