def train(self):
batch_manager = BatchManager(self.encoder_vec, self.decoder_vec, self.batch_size)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
loss_track = []
total_time = 0
nums_batch = len(batch_manager.batch_data)
for epoch in range(self.max_epoch):
print "[->] epoch {}".format(epoch)
batch_index = 0
for batch in batch_manager.batch():
batch_index += 1
# 获取fd [time_steps, batch_size]
fd = self.get_fd(batch, self.model)
_, loss, logits, labels = self.sess.run([self.model.train_op,
self.model.loss,
self.model.logits,
self.model.decoder_labels], fd)
loss_track.append(loss)
if batch_index % self.show_batch == 0:
print "\tstep: {}/{}".format(batch_index, nums_batch)
print '\tloss: {}'.format(loss)
print "\t"+"-"*50
checkpoint_path = self.model_path+"chatbot_seq2seq.ckpt"
# 保存模型
self.model.saver.save(self.sess, checkpoint_path, global_step=self.model.global_step)
def train(self):
print("++++++++train+++++++")
batch_manager = BatchManager(self.encoder_vec, self.decoder_vec,
self.batch_size)
#用来配置tf的sess，使用gpu还是cpu
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#存放交叉熵结果
loss_track = []
total_time = 0
#算第几轮用的参数
nums_batch = len(batch_manager.batch_data)
for epoch in range(self.max_epoch):
print("[->] epoch {}".format(epoch))
batch_index = 0
for batch in batch_manager.batch():
batch_index += 1
# 获取fd [time_steps, batch_size]
fd = self.get_fd(batch, self.model)
#sess.run计算model的张量tensor，这里利用优化器做优化
_, loss, logits, labels = self.sess.run([
self.model.train_op, self.model.loss, self.model.logits,
self.model.decoder_labels
], fd)
loss_track.append(loss)
if batch_index % self.show_batch == 0:
print("\tstep: {}/{}".format(batch_index, nums_batch))
print('\tloss: {}'.format(loss))
print("\t" + "-" * 50)
checkpoint_path = self.model_path + "chatbot_seq2seq.ckpt"
# 保存模型
self.model.saver.save(self.sess,
checkpoint_path,
global_step=self.model.global_step)