虚拟环境pytorch的CNN代码

好的，以下是一个基于 PyTorch 的简单 CNN 代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader

# 定义网络结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据集
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
train_loader = DataLoader(train_set, batch_size=4, shuffle=True, num_workers=2)

# 初始化网络和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练网络
for epoch in range(2):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

这个例子中，我们定义了一个具有两个卷积层和三个全连接层的简单 CNN，在 CIFAR10 数据集上进行训练，并使用交叉熵损失和随机梯度下降优化器进行训练。