pytorch实现CNN

下面是一个使用PyTorch实现CNN的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义一个简单的CNN模型
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 8 * 8, 1024)
        self.relu3 = nn.ReLU(inplace=True)
        self.dropout1 = nn.Dropout(0.5)
        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.dropout1(x)
        x = self.fc2(x)
        return x

# 定义数据集和数据加载器
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, 
                                             download=True, transform=transforms.ToTensor())
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, 
                                            download=True, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

# 定义训练函数和测试函数
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()))

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() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

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

# 定义训练参数和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleCNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# 训练模型
for epoch in range(1, 11):
    train(model, device, train_loader, optimizer, criterion, epoch)
    test(model, device, test_loader, criterion)

在上述代码中，我们定义了一个简单的CNN模型 `SimpleCNN`，它包含两个卷积层、两个池化层和两个全连接层。我们使用 CIFAR-10 数据集来训练和测试模型。训练过程中，我们通过调用 `train` 函数来迭代地训练模型，每迭代一次就计算一次损失并更新模型参数。测试过程中，我们通过调用 `test` 函数来评估模型的性能，包括平均损失和准确率。最后，我们使用 SGD 优化器来优化模型参数，学习率为 0.01，动量为 0.9。