VGG使用块的网络
一.手写VGG架构
import torch
from torch import nn
import d2l
#1.VGG块
def VGG_block(nums_conv,in_chanels,out_chanels):#卷积层数，输入通道数，输出通道数layers=[]#一个VGG里面的层for i in range(nums_conv):layers.append(nn.Conv2d(in_chanels,out_chanels,3,padding=1))layers.append(nn.ReLU())in_chanels=out_chanels#下一层输入等于本层输出layers.append(nn.MaxPool2d(kernel_size=2,stride=2))return nn.Sequential(*layers)
#VGG网络由多个VGG块组成
def VGG_net(conv_info):#传入有关每一个块的卷积情况conv_blocks=[]in_chanels=1#默认输入通道初始是1for (nums_conv,out_chanels) in conv_info:conv_blocks.append(VGG_block(nums_conv,in_chanels,out_chanels))in_chanels=out_chanels#下一层输入等于本层输出return nn.Sequential(*conv_blocks,nn.Flatten(),nn.Linear(7*7*out_chanels,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,10))
conv_info=((1,64),(1,128),(2,256),(2,512),(2,512))
net=VGG_net(conv_info)
X=torch.randn((1,1,224,224))
for layer in net:X=layer(X)print(layer.__class__.__name__,X.shape)分析代码：
VGG网络架构，由多个VGG块以及3个全连接层组成
每个VGG块可以不同，VGG块由多个3*3padding=1的卷积层和一个2*2stride=2的最大池化层组成
（1）VGG块
def VGG_block(nums_conv,in_chanels,out_chanels):#卷积层数，输入通道数，输出通道数layers=[]#一个VGG里面的层for i in range(nums_conv):layers.append(nn.Conv2d(in_chanels,out_chanels,3,padding=1))layers.append(nn.ReLU())in_chanels=out_chanels#下一层输入等于本层输出layers.append(nn.MaxPool2d(kernel_size=2,stride=2))return nn.Sequential(*layers)（2）VGG网络
def VGG_net(conv_info):#传入有关每一个块的卷积情况conv_blocks=[]in_chanels=1#默认输入通道初始是1for (nums_conv,out_chanels) in conv_info:conv_blocks.append(VGG_block(nums_conv,in_chanels,out_chanels))in_chanels=out_chanels#下一层输入等于本层输出return nn.Sequential(#这一部分和AlexNet一样，三层全连接层*conv_blocks,nn.Flatten(),nn.Linear(7*7*out_chanels,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,10))二.训练VGG网络
import torch
from torch import nn
from d2l import torch as d2l
#1.VGG块
def VGG_block(nums_conv,in_chanels,out_chanels):#卷积层数，输入通道数，输出通道数layers=[]#一个VGG里面的层for i in range(nums_conv):layers.append(nn.Conv2d(in_chanels,out_chanels,3,padding=1))layers.append(nn.ReLU())in_chanels=out_chanels#下一层输入等于本层输出layers.append(nn.MaxPool2d(kernel_size=2,stride=2))return nn.Sequential(*layers)#VGG网络由多个VGG块组成
def VGG_net(conv_info):#传入有关每一个块的卷积情况conv_blocks=[]in_chanels=1#默认输入通道初始是1for (nums_conv,out_chanels) in conv_info:conv_blocks.append(VGG_block(nums_conv,in_chanels,out_chanels))in_chanels=out_chanels#下一层输入等于本层输出nn.Flatten()return nn.Sequential(*conv_blocks,nn.Flatten(),nn.Linear(7*7*out_chanels,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(p=0.5),nn.Linear(4096,10))
conv_info=((1,64),(1,128),(2,256),(2,512),(2,512))
#因为这个VGG网络层数太多运行太慢，所以把输出通道除4
ratio=4
smallconv_info=[(pair[0],pair[1]//ratio) for pair in conv_info]
net=VGG_net(smallconv_info)
batch_size=128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size,resize=224)#数据集
lr=0.05
nums_epochs=10
d2l.train_ch6(net,train_iter,test_iter,nums_epochs,lr,d2l.try_gpu())