LeNet网络实现cifar10数据集分类

import  tensorflow as tf
import numpy as np
import os
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Dense,Flatten,Activation,Conv2D,MaxPool2D
from tensorflow.keras import Model


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 LeNet5(Model):
    def __init__(self):
        super(LeNet5,self).__init__()
        self.c1=Conv2D(filters=6,kernel_size=(5,5),strides=1,padding='valid')
        self.a1=Activation('sigmoid')
        self.p1=MaxPool2D(pool_size=(2,2),strides=2,padding='valid')

        self.c2=Conv2D(filters=16,kernel_size=(5,5),strides=1,padding='valid')
        self.a2=Activation('sigmoid')
        self.p2=MaxPool2D(pool_size=(2,2),strides=2,padding='valid')

        self.flatten=Flatten()
        self.f1=Dense(120,activation='sigmoid')
        self.f2=Dense(84, activation='sigmoid')
        self.f3=Dense(10, activation='softmax')

    def call(self,x):
        x = self.c1(x)
        x = self.a1(x)
        x = self.p1(x)

        x = self.c2(x)
        x = self.a2(x)
        x = self.p2(x)

        x = self.flatten(x)
        x = self.f1(x)
        x = self.f2(x)
        y= self.f3(x)
        return y

model=LeNet5()

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

checkpoint_save_path='./checkpoint/LeNet.ckpt'

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

cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                               save_best_only=True,
                                               save_weights_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('./LeNet_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()

#############可视化图像#############
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(loss,label='loss')
plt.plot(val_loss,label='val_loss')
plt.title('Training and Validation Loss')
plt.legend()

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

plt.show()