卷积网络实现cifar数据集分类

import tensorflow as tf
import os
import numpy as np
from matplotlib import  pyplot as plt
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dropout,Flatten,Dense
from tensorflow.keras import Model
np.set_printoptions(threshold=np.inf)

cifar10=tf.keras.datasets.cifar10
(x_train,y_train),(x_test,y_test)=cifar10.load_data()
x_train=x_train/255.
x_test=x_test/255.

class Baseline(Model):
    def __init__(self):
        super(Baseline,self).__init__()
        self.c1=Conv2D(filters=6,kernel_size=(5,5),padding='same')   #6个5*5卷积核
        self.b1=BatchNormalization()                                 #批标准化
        self.a1=Activation('relu')
        self.p1=MaxPool2D(pool_size=(2,2),strides=2,padding='same')  #最大值池化
        self.d1=Dropout(0.2)                                         #舍弃

        self.flatter=Flatten()                                      #数据拉直
        self.f1=Dense(128,activation='relu')
        self.d2=Dropout(0.2)
        self.f2=Dense(10,activation='softmax')

    def call(self,x):
        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.p1(x)
        x = self.d1(x)
        x = self.flatter(x)
        x = self.d2(x)
        y = self.f2(x)
        return y

model=Baseline()
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

checkpoint_save_path='./checkpoint/Baseline.ckpt'

if os.path.exists(checkpoint_save_path+'.index'):
    print('-------load the model-------')
    model.load_weights(checkpoint_save_path)

cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                               save_weights_only=True,
                                               save_best_only=True)

history=model.fit(x_train,y_train,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,
                  callbacks=[cp_callback])

model.summary()

file=open('./weights.txt','w')
for v in model.trainable_variables:
    file.write(str(v.name)+'\n')
    file.write(str(v.shape) + '\n')
    file.write(str(v.numpy()) + '\n')

file.close()

##########show###########
acc=history.history['sparse_categorical_accuracy']
val_acc=history.history['val_sparse_categorical_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']

plt.subplot(1,2,1)
plt.plot(acc,label='Training Accuracy')
plt.plot(val_acc,label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1,2,2)
plt.plot(loss,label='Training Loss')
plt.plot(val_loss,label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()