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VGG16训练和测试Fashion和CIFAR10

news 2025/8/10 7:25:42

CIFAR10 epoch=50

Test Accuracy: 91.77%

VGG网络结构

VGG16(D)网络结构 5个block+5个Maxpool+3个FC+soft-max

VGG网络参数（标准 ImageNet 版 VGG16）

第一个VGG Block层（两个卷积层，两个ReLu，一个池化）

self.block1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2)
)

第二个VGG Block层（两个卷积层，两个ReLu，一个池化）

self.block2 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2))

第三个VGG Block层（三个卷积层，三个ReLu，一个池化）

        self.block3 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2))

第四个VGG Block层（三个卷积层，三个ReLu，一个池化）

        self.block4 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2))

第五个VGG Block层（三个卷积层，三个ReLu，一个池化）

        self.block5 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2))

线性分类器层（flatten,linear,Relu,Dropout)

        self.classifier = nn.Sequential(nn.Flatten(),nn.Linear(in_features=7*7*512, out_features=4096),nn.ReLU(inplace=True),nn.Dropout(),nn.Linear(in_features=4096, out_features=4096),nn.ReLU(inplace=True),nn.Dropout(),nn.Linear(in_features=4096, out_features=10),)

前向传播代码

    def forward(self, x):x = self.block1(x)x = self.block2(x)x = self.block3(x)x = self.block4(x)x = self.block5(x)x = self.classifier(x)return x

kaiming初始化权重函数

# ===== Kaiming 初始化函数 =====
def init_weights(m):if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')nn.init.constant_(m.bias, 0)

主函数代码

if __name__ == '__main__':device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")model = VGG16().to(device)print(summary(model, (1, 224, 224)))

完整代码

import torch
import torch.nn as nn
from torchsummary import summaryclass VGG16(nn.Module):def __init__(self):super().__init__()self.block1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))self.block2 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))self.block3 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))self.block4 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))self.block5 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))self.classifier = nn.Sequential(nn.Flatten(),nn.Linear(7*7*512, 4096),nn.ReLU(inplace=True),nn.Dropout(),nn.Linear(4096, 4096),nn.ReLU(inplace=True),nn.Dropout(),nn.Linear(4096, 10),)def forward(self, x):x = self.block1(x)x = self.block2(x)x = self.block3(x)x = self.block4(x)x = self.block5(x)x = self.classifier(x)return x# ===== Kaiming 初始化函数 =====
def init_weights(m):if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')nn.init.constant_(m.bias, 0)if __name__ == '__main__':device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")model = VGG16().to(device)# 应用 Kaiming 初始化model.apply(init_weights)# 打印模型结构summary(model, (1, 224, 224))