VGG网络

VGG

前言

AlexNet对于LeNet的提升得益于网络的更深更大，网络更深更大的方法：

• 更多的全连接层
• 更多的卷积层
• 将卷积层分成块（VGG）

和AlexNet的区别：
使用尺寸更小的3×3卷积核串联来替代大卷积核11×11,7×7这样的大尺寸卷积核，引入块设计思想，在相同的感受野的情况下，多个串联非线性能力更强，描述能力更强

VGG思路

VGG网络的思路是将卷积层分成多个块，每个块包含多个卷积层和一个池化层，最后串联接多个全连接层。

• VGG-16 13个卷积层与3个全连接层
• VGG-19 16个卷积层与3个全连接层

VGG实现

import torch
from torch import nn
from d2l import torch as d2l

# 定义VGG块
def vgg_block(num_convs, in_channels, out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))
        layers.append(nn.ReLU())
        in_channels = out_channels#更新输入通道数
    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    return nn.Sequential(*layers)

def vgg(conv_arch, num_classes=10):
    conv_blocks = []
    in_channels = 1  # 输入通道数
    for (num_convs, out_channels) in conv_arch:
        conv_blocks.append(vgg_block(num_convs, in_channels, out_channels))
        in_channels = out_channels
    return nn.Sequential(
        #卷积单元
        *conv_blocks,
        #全连接单元
        nn.Flatten(),
        nn.Linear(in_channels * 7 * 7, 4096),  # 假设输入图像大小为224x224
        nn.ReLU(),
        nn.Dropout(0.5),
        nn.Linear(4096, 4096),
        nn.ReLU(),
        nn.Dropout(0.5),
        nn.Linear(4096, num_classes)
    )

if __name__ == "__main__":
    conv_arch = [(1, 64), (1, 128), (2, 256), (2, 512), (2, 512)]  # VGG架构
    net = vgg(conv_arch)
    
    # 测试网络结构
    # X = torch.randn(1, 1, 224, 224)  # 输入图像的形状
    # for layer in net:
    #     X = layer(X)  # 前向传播
    #     print(layer.__class__.__name__, 'output shape:\t', X.shape)

    batch_size = 128
    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
    lr, num_epochs = 0.01, 10
    d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())