VGGNet实现cifar10数据集分类

import tensorflow as tf
import os
from matplotlib import pyplot as plt
import tensorflow.keras.datasets
from tensorflow.keras import  Model
import numpy as np
from tensorflow.keras.layers import Dense,Flatten,BatchNormalization,Dropout,Conv2D,Activation,MaxPool2D
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 VGGNet(Model):
    def __init__(self):
        super(VGGNet, self).__init__()
        self.c1=Conv2D(filters=64,kernel_size=(3,3),strides=1,padding='same')
        self.b1=BatchNormalization()
        self.a1=Activation('relu')
        self.c2 = Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same')
        self.b2 = BatchNormalization()
        self.a2 = Activation('relu')
        self.p2=MaxPool2D(pool_size=(2,2),strides=2,padding='same')
        self.d2=Dropout(0.2)

        self.c3 = Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same')
        self.b3 = BatchNormalization()
        self.a3 = Activation('relu')
        self.c4 = Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same')
        self.b4 = BatchNormalization()
        self.a4 = Activation('relu')
        self.p4 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d4 = Dropout(0.2)

        self.c5 = Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same')
        self.b5 = BatchNormalization()
        self.a5 = Activation('relu')

        self.c6 = Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same')
        self.b6 = BatchNormalization()
        self.a6 = Activation('relu')
        self.c7 = Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same')
        self.b7 = BatchNormalization()
        self.a7 = Activation('relu')
        self.p7 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d7 = Dropout(0.2)

        self.c8 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b8 = BatchNormalization()
        self.a8 = Activation('relu')

        self.c9 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b9 = BatchNormalization()
        self.a9 = Activation('relu')
        self.c10 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b10 = BatchNormalization()
        self.a10 = Activation('relu')
        self.p10 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d10 = Dropout(0.2)

        self.c11 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b11 = BatchNormalization()
        self.a11 = Activation('relu')

        self.c12 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b12 = BatchNormalization()
        self.a12 = Activation('relu')
        self.c13 = Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same')
        self.b13 = BatchNormalization()
        self.a13 = Activation('relu')
        self.p13 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d13 = Dropout(0.2)


        self.flatten=Flatten()
        self.f1 = Dense(512,activation='relu')
        self.d1 = Dropout(0.2)
        self.f2 = Dense(512, activation='relu')
        self.d2 = Dropout(0.2)
        self.f3 = Dense(10, activation='softmax')

    def call(self,x):

        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.c2(x)
        x = self.b2(x)
        x = self.a2(x)
        x = self.c2(x)
        x = self.d2(x)

        x = self.c3(x)
        x = self.b3(x)
        x = self.a3(x)
        x = self.c4(x)
        x = self.b4(x)
        x = self.a4(x)
        x = self.c4(x)
        x = self.d4(x)

        x = self.c5(x)
        x = self.b5(x)
        x = self.a5(x)

        x = self.c6(x)
        x = self.b6(x)
        x = self.a6(x)
        x = self.c7(x)
        x = self.b7(x)
        x = self.a7(x)
        x = self.c7(x)
        x = self.d7(x)

        x = self.c8(x)
        x = self.b8(x)
        x = self.a8(x)

        x = self.c9(x)
        x = self.b9(x)
        x = self.a9(x)
        x = self.c10(x)
        x = self.b10(x)
        x = self.a10(x)
        x = self.c10(x)
        x = self.d10(x)

        x = self.c11(x)
        x = self.b11(x)
        x = self.a11(x)

        x = self.c12(x)
        x = self.b12(x)
        x = self.a12(x)
        x = self.c13(x)
        x = self.b13(x)
        x = self.a13(x)
        x = self.c13(x)
        x = self.d13(x)


        x = self.flatten(x)

        x=self.f1(x)
        x=self.d1(x)
        x=self.f2(x)
        x=self.d2(x)
        y=self.f3(x)
        return y

model=VGGNet()

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

check_save_path='./checkpoint/VGGNet.ckpt'
if os.path.exists(check_save_path+'.index'):
    print('-------------lodel the model------------')
    model.load_weights(check_save_path)

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

history=model.fit(x_train,y_train,batch_size=128,epochs=5,validation_data=(x_test,y_test),
                  validation_freq=1,callbacks=[cp_callback])

model.summary()

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


############可视化图像###############
acc=history.history['sparse_categorical_accuracy']
val_acc=history.history['sparse_categorical_val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']

plt.subplot(1,2,1)
plt.plot(acc)
plt.plot(val_acc)
plt.legend()

plt.subplot(1,2,2)
plt.plot(loss)
plt.plot(val_loss)
plt.legend()

plt.show()

VGGNet共有13层卷积层，3层全连接层，共16层，单次遍历需要12小时