def train(self, session):
"""
模型训练函数
:param session:
:return:
"""
training_set = SequenceData(filename=self.conf.training_data, max_seq_len=self.seq_max_len)
for step in range(1, self.conf.training_steps + 1):
batch_x, batch_y, batch_seqlen = training_set.next(self.conf.batch_size)
# Run optimization op (backprop)
session.run(self.optimizer_op,
feed_dict={self.X: batch_x, self.Y: batch_y,
self.seqlen: batch_seqlen,
self.dropout_keep_prob: self.conf.dropout_keep_prob})
if step % self.conf.display_steps == 0 or step == 1:
# Calculate batch accuracy & loss
acc, loss = session.run([self.accuracy_op, self.loss_op],
feed_dict={self.X: batch_x, self.Y: batch_y,
self.seqlen: batch_seqlen,
self.dropout_keep_prob: self.conf.dropout_keep_prob})
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
print("Optimization Finished!")
print('Start to save model.')
saver = tf.train.Saver()
saver.save(session, self.conf.save_model_path)
def train(self):
"""
train
:return:
"""
print 'Start training model.'
training_set = SequenceData(filename=self.config.training_file, max_seq_len=self.max_seq_len)
for i in range(self.config.training_steps):
batch_x, batch_y, batch_seqlen, batch_words = training_set.next(self.config.batch_size)
batch_x = batch_x.to(device)
_, batch_y = torch.max(batch_y, 1) # 元组第一个维度为最大值，第二个维度为最大值的索引
batch_y = batch_y.to(device)
batch_seqlen = batch_seqlen.to(device)
# Forward pass
outputs, all_outputs = self.model(batch_x, batch_seqlen)
# loss = self.cross_entropy_loss(outputs, batch_y)
loss = self.cross_entropy_loss(outputs, batch_y) + \
self.regularized_loss(all_outputs, batch_seqlen, batch_words)
# loss = self.criterion(outputs, batch_y)
# Backward and optimize
self.optimizer.zero_grad() # 清空梯度缓存
loss.backward() # 反向传播，计算梯度
self.optimizer.step() # 利用梯度更新模型参数
if (i + 1) % 100 == 0:
print 'Step [{}/{}], Loss: {:.4f}'\
.format(i + 1, self.config.training_steps, loss.item())
# Save the model checkpoint
print 'Start saving model to "%s".' % self.config.save_model_path
torch.save(self.model.state_dict(), self.config.save_model_path)