fig_loss = np.zeros([n_epoch])
fig_acc1 = np.zeros([n_epoch])
fig_acc2= np.zeros([n_epoch])
for epoch in range(n_epoch):
 start_time = time.time()

 #training
 train_loss, train_acc, n_batch = 0, 0, 0
 for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
  _,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a})
  train_loss += err; train_acc += ac; n_batch += 1

  summary_str = sess.run(merged_summary_op,feed_dict={x: x_train_a, y_: y_train_a})
  summary_writer.add_summary(summary_str, epoch)

 print(" train loss: %f" % (np.sum(train_loss)/ n_batch))
 print(" train acc: %f" % (np.sum(train_acc)/ n_batch))
 fig_loss[epoch] = np.sum(train_loss)/ n_batch
 fig_acc1[epoch] = np.sum(train_acc) / n_batch
 #validation
 val_loss, val_acc, n_batch = 0, 0, 0
 for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
  err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
  val_loss += err; val_acc += ac; n_batch += 1
 print(" validation loss: %f" % (np.sum(val_loss)/ n_batch))
 print(" validation acc: %f" % (np.sum(val_acc)/ n_batch))
 fig_acc2[epoch] = np.sum(val_acc) / n_batch
# 训练loss图
fig, ax1 = plt.subplots()
lns1 = ax1.plot(np.arange(n_epoch), fig_loss, label="Loss")
ax1.set_xlabel('iteration')
ax1.set_ylabel('training loss')

# 训练和验证两种准确率曲线图放在一张图中
fig2, ax2 = plt.subplots()
ax3 = ax2.twinx()#由ax2图生成ax3图
lns2 = ax2.plot(np.arange(n_epoch), fig_acc1, label="Loss")
lns3 = ax3.plot(np.arange(n_epoch), fig_acc2, label="Loss")

ax2.set_xlabel('iteration')
ax2.set_ylabel('training acc')
ax3.set_ylabel('val acc')
 # 合并图例
lns = lns3 + lns2
labels = ["train acc", "val acc"]
plt.legend(lns, labels, loc=7)

plt.show()

sgd = SGD(lr=float(model_value[3]), decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# validation_split：0~1之间的浮点数，用来指定训练集的一定比例数据作为验证集
history=model.fit(self.x_train, self.y_train, batch_size=self.batch_size, epochs=self.epoch_size, class_weight = 'auto', validation_split=0.1)
# 绘制训练 & 验证的准确率值
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
 
# 绘制训练 & 验证的损失值
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
print("savemodel---------------")
model.save(os.path.join(model_value[0],'model3_3.h5'))
#输出损失和精确度
score = model.evaluate(self.x_test, self.y_test, batch_size=self.batch_size)