帮我写一个CNN卷积神经网络的代码，来识别手写数字的图片，以pytorch为环境

当然可以。首先，我们需要导入必要的库并设置一些基本配置。在这个例子中，我们将使用PyTorch构建一个简单的卷积神经网络（Convolutional Neural Network，简称CNN），用于MNIST数据集的手写数字识别。这是一个简化的版本，实际项目可能需要更多的优化。

# 导入所需库
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

# 定义CNN模型
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        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()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(32 * 7 * 7, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)  # 输出层，10表示MNIST的10个类别

    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 * 7 * 7)  # 扁平化
        x = self.fc1(x)
        x = self.relu3(x)
        out = self.fc2(x)
        return out

model = SimpleCNN().to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练过程
num_epochs = 10
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images, labels = images.to(device), labels.to(device)

        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print(f'Test Accuracy of the model on the 10000 test images: {100 * correct / total}%')

这个代码示例创建了一个基础的卷积神经网络，包括两个卷积层、池化层以及全连接层。在训练过程中，它会打印出每轮的损失，并在测试阶段计算准确率。