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) + ' ') file.write(str(v.shape) + ' ') file.write(str(v.np()) + ' ') 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