VGGNet
VGG的实质:AlexNet增强版
VGG网络用了更小的3x3的卷积核，两个连续的3x3的卷积核相当于5x5的感受野。
三个3x3的连续的卷积核也就相当于7x7的感受野。
VGG池化层特征池化核2x2；AlexNet网络中池化核3x3；
从而层数变多，训练参数减少。
层数多，提取特征效果好，但是计算量就大了。
特点：
小尺寸卷积核
减少参数
提升准确率
16层网络 f分别是
CBA CBAPT 35
CBA CBAPT 35
CBA CBA CBAPT 335
CBA CBA CBAPT 335
flatten 拉直层 全连接（512个神经元）全连接（512个神经元）全连接（10个神经元） 111
卷积核个数变化
64-128-256-512
核数量 逐渐增加
越靠后，图像特征越少，增加卷积核的个数—》增加特征图的深度
保持信息承载能力。
VGG16 网络模型 的 代码
导包
import os
import tensorflow as tf
from tensorflow.keras import layers, optimizers, datasets, Sequential
import matplotlib.pyplot as plt
设置屏蔽不喜欢的提示
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
小tips：
log信息共有四个等级，按重要性递增为：
INFO（通知）<WARNING（警告）<ERROR（错误）<FATAL（致命的）
| base_log | 屏蔽信息 | 输出信息 |
|---|---|---|
| “0” INFO | 无 | INFO + WARNING + ERROR + FATAL |
| “1” | WARNING | INFO WARNING + ERROR + FATAL |
| “2” | ERROR | INFO + WARNING ERROR + FATAL |
| “3” | FATAL | INFO + WARNING + ERROR FATAL |
加载数据
(x_train, y_train), (x_test, y_test) = datasets.cifar100.load_data()
tf.random.set_seed(2345)
class VGG16(Model):
def __init__(self):
super(VGG16, self).__init__()
self.c1 = Conv2D(filters=64, kernel_size=(3, 3), padding='same') # 卷积层1
self.b1 = BatchNormalization() # BN层1
self.a1 = Activation('relu') # 激活层1
self.c2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same', )
self.b2 = BatchNormalization() # BN层1
self.a2 = Activation('relu') # 激活层1
self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d1 = Dropout(0.2) # dropout层
self.c3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
self.b3 = BatchNormalization() # BN层1
self.a3 = Activation('relu') # 激活层1
self.c4 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
self.b4 = BatchNormalization() # BN层1
self.a4 = Activation('relu') # 激活层1
self.p2 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d2 = Dropout(0.2) # dropout层
self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
self.b5 = BatchNormalization() # BN层1
self.a5 = Activation('relu') # 激活层1
self.c6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
self.b6 = BatchNormalization() # BN层1
self.a6 = Activation('relu') # 激活层1
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() # BN层1
self.a8 = Activation('relu') # 激活层1
self.c9 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b9 = BatchNormalization() # BN层1
self.a9 = Activation('relu') # 激活层1
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() # BN层1
self.a11 = Activation('relu') # 激活层1
self.c12 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
self.b12 = BatchNormalization() # BN层1
self.a12 = Activation('relu') # 激活层1
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