keras输出预测值和真实值方式

from keras.callbacks import Callback
import tensorflow as tf
import numpy as np
class my_callback(Callback):
 def __init__(self,dataGen,showTestDetail=True):
  self.dataGen=dataGen
  self.showTestDetail=showTestDetail
  self.predhis = []
  self.targets = []
 def mape(self,y,predict):
  diff = np.abs(np.array(y) - np.array(predict))
  return np.mean(diff / y)
 def on_epoch_end(self, epoch, logs=None):
  x_test,y_test=next(self.dataGen)
  prediction = self.model.predict(x_test)
  self.predhis.append(prediction)
  #print("Prediction shape: {}".format(prediction.shape))
  #print("Targets shape: {}".format(y_test.shape))
  if self.showTestDetail:
   for index,item in enumerate(prediction):
    print(item,"=====",y_test[index],"====",y_test[index]-item)
  testLoss=self.mape(y_test,prediction)
  print("test loss is :{}".format(testLoss))

import numpy as np
def shuffleDatas(x,y):

 shuffleIndex=np.arange(len(x))
 np.random.shuffle(shuffleIndex)
 x=x[shuffleIndex]
 y=y[shuffleIndex]
 return x,y
def dataGen(x,y,batchsize=8,shuffle=True):
 assert len(x) == len(y)
 while True:
  if shuffle:
   x,y=shuffleDatas(x,y)
  index=0
  while index+batchsize<len(x):
   yield (x[index:index+batchsize],y[index:index+batchsize])
   index=index+batchsize