基于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

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, 10)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = x.view(-1, 64 * 8 * 8)
        x = self.dropout(x)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# 定义训练过程
def train(model, device, train_loader, optimizer, criterion, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试过程
def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item() # 将一批的损失相加
            pred = output.argmax(dim=1, keepdim=True) # 找到概率最大的下标
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 设置参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
batch_size = 64
epochs = 10
learning_rate = 0.01
momentum = 0.5

# 加载数据集
train_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10(root='./data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.RandomHorizontalFlip(),
                       transforms.RandomCrop(32, padding=4),
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=batch_size, shuffle=True, num_workers=2)

test_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10(root='./data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])),
    batch_size=batch_size, shuffle=False, num_workers=2)

# 初始化模型、损失函数、优化器
model = CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)

# 训练和测试
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, criterion, epoch)
    test(model, device, test_loader, criterion)

这是一个简单的CIFAR-10分类任务，如果你想要尝试其他的任务或者网络结构，可以根据自己的需求进行修改。