写出将结构写在一个sequtial中的代码结构与此相同 class CNN(nn.Module): # def __init__(self): # super(CNN, self).__init__() # self.conv1 = nn.Conv2d(3, 32, 3) # self.conv2 = nn.Conv2d(32, 64, 3) # self.conv3 = nn.Conv2d(64, 128, 3) # self.conv4 = nn.Conv2d(128, 256, 3) # self.fc1 = nn.Linear(6400, 1024) # self.fc2 = nn.Linear(1024, 512) # self.fc3 = nn.Linear(512, 128) # self.fc4 = nn.Linear(128, 10) # def forward(self, x): # # 3 x 32 x 32 # x = torch.relu(self.conv1(x)) # 32 x 30 x 30 # x = nn.BatchNorm2d(32).to("cuda")(x) # 32 x 30 x 30 # x = torch.relu(self.conv2(x)) # 64 x 28 x 28 # x = torch.max_pool2d(x, 2) # 64 x 14 x 14 # x = torch.relu(self.conv3(x)) # 128 x 12 x 12 # x = nn.BatchNorm2d(128).to("cuda")(x) # 128 x 12 x 12 # x = torch.relu(self.conv4(x)) # 256 x 10 x 10 # x = torch.max_pool2d(x, 2) # 256 x 5 x 5 # x = x.view(-1, 6400) # x = torch.relu(self.fc1(x)) # x = torch.tanh(self.fc2(x)) # x = torch.relu(self.fc3(x)) # x = self.fc4(x) # return x

以下是将结构写在一个Sequential中的代码：

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 3),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.Conv2d(32, 64, 3),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, 3),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.Conv2d(128, 256, 3),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(6400, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.Tanh(),
            nn.Linear(512, 128),
            nn.ReLU(),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.model(x)
        return x

model = CNN()

在这个代码中，我们使用了`nn.Sequential`来将所有的层组合在一起。每个层都按照从输入到输出的顺序添加到了`nn.Sequential`中。同时，我们也将`nn.BatchNorm2d`和`nn.MaxPool2d`层直接添加到了`nn.Sequential`中。最后，我们在`forward`方法中调用了整个模型。