使用块的网络VGG

1.AlexNet比LeNet更深更大来得到精度，能不能更深和更大?

选项：

①更多的全连接层（太贵）

②更多的卷积层

③将卷积层组合成块

2.VGG块的选择

①深或者宽?

5*5卷积

3*3卷积

选择深且窄的效果更好

②VGG块

3*3卷积（填充1）：学习到更多的特征且参数更少（n层，m通道）

2*2最大池化层（步幅2）

3.VGG架构

①多个VGG块后连接成全连接层

②不同次数的重复块得到的不同架构VGG-16,VGG-19

4.VGG进度

①LeNet(1995)

2卷积+池化层

2全连接层

②AlexNet

更大更深

ReLu,DropOut数据增强

③VGG

更大更深的AlexNet(重复的VGG块)

【总结】

①VGG使用可重复使用的卷积块来构建深度卷积神经网络

②不同的卷积块个数和超参数可以得到不同复杂度的变种。

【代码实现】

import torch
from torch import nn
from d2l import torch as d2l# 函数由三个参数 卷积层数量num_convs,输入通道in_channels,输出通道out_channels
# 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  # 上一次的输出是下一次的输入layers.append(nn.MaxPool2d(kernel_size=2, stride=2))return nn.Sequential(*layers)# VGG网络  超参数变量conv_arch：每个VGG块里卷积层个数和输出通道数
conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))# 定义VGG-11
def vgg(conv_arch):conv_blks = []in_channels = 1# 卷积层部分for (num_convs, out_channels) in conv_arch:conv_blks.append(vgg_block(num_convs, in_channels, out_channels))in_channels = out_channelsreturn nn.Sequential(*conv_blks, nn.Flatten(),# 全连接层部分nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),nn.Linear(4096, 10))net = vgg(conv_arch)# 模型训练
ratio = 4
small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]
net = vgg(small_conv_arch)
lr, num_epochs, batch_size = 0.05, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
d2l.plt.show()