使用AlexNet训练mnist(面向对象)

from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.optimizers import SGD, Adam
from keras.layers import Dense, Flatten, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.models import load_model
import keras
import numpy as np
from keras.applications.imagenet_utils import preprocess_input
from keras import backend as K
from keras.datasets import cifar10
from tensorflow.examples.tutorials.mnist import input_data
K.clear_session()
mnist = input_data.read_data_sets("MNIST_DATA", one_hot=True)
class AlexModel:
    #初始化参数
    def __init__(self, epochs, batch_size):
        """
        :param epochs: 训练集迭代的轮数
        :param batch_size: 每次训练的样本的个数
        """
        self.epochs = epochs
        self.batch_size = batch_size
        # 存储训练过程中的精度和误差
        self.train_accuracy_and_loss = None
    # 创建模型
    def build_model(self):
        """
        创建模型, 基于alexnet
        :return: 
        """

        model = Sequenti