VGGNet16网络在python下TensorFlow2的实现
参考资料： 北京大学， 软微学院， 曹健老师， 《人工智能实践：TensorFlow2.0笔记》
运行环境：
python3.7
tensorflow 2.1.0
numpy 1.17.4
matplotlib 3.2.1
# vgg16
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_test = x_train / 255.0, x_test / 255.0
class VGG16(Model):
def __init__(self):
super(VGG16, self).__init__()
# cbapd
self.c1 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')
self.b1 = BatchNormalization()
self.a1 = Activation('relu')
self.c2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')
self.b2 = BatchNormalization()
self.a2 = Activation('relu')
self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d1 = Dropout(0.2)
self.c3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
self.b3 = BatchNormalization()
self.a3 = Activation('relu')
self.c4 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
self.b4 = BatchNormalization()
self.a4 = Activation('relu')
self.p2 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d2 = Dropout(0.2)
self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
self.b5 = BatchNormalization()
self.a5 = Activation('relu')
self.c6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
self.b6 = BatchNormalization()
self.a6 = Activation('relu')
self.c7 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
self.b7 = BatchNormalization()
self.a7 = Activation('relu')
self.p3 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d3 = Dropout(0.2)
self.c8 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b8 = BatchNormalization()
self.a8 = Activation('relu')
self.c9 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b9 = BatchNormalization()
self.a9 = Activation('relu')
self.c10 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b10 = BatchNormalization()
self.a10 = Activation('relu')
self.p4 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d4 = Dropout(0.2)
self.c11 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b11 = BatchNormalization()
self.a11 = Activation('relu')
self.c12 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b12 = BatchNormalization()
self.a12 = Activation('relu')
self.c13 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b13 = BatchNormalization()
self.a13 = Activation('relu')
self.p5 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d5 = Dropout(0.2)
self.flatten = Flatten()
self.f1 = Dense(512, activation='relu')
self.d6 = Dropout(0.2)
self.f2 = Dense(512, activation='relu')
self.d7 = 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.p1(x)
x = self.d1(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.p2(x)
x = self.d2(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.p3(x)
x = self.d3(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.p4(x)
x = self.d4(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.p5(x)
x = self.d5(x)
x = self.flatten(x)
x = self.f1(x)
x = self.d6(x)
x = self.f2(x)
x = self.d7(x)
y = self.f3(x)
return y
model = VGG16()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy']
)
checkpoint_save_path = './checkpoint/VGG16.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()
# print(model.trainable_variables)
file = open('./weights_vgg16.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 ###############################################
# 显示训练集和验证集的acc和loss曲线
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()