手写数字识别（MNIST数据集）的项目的代码（用pytorch写）

手写数字识别项目通常使用深度学习框架PyTorch来实现，特别是卷积神经网络（Convolutional Neural Networks，CNN）。这里是一个简单的使用PyTorch和MNIST数据集训练LeNet模型的示例：

import torch
import torchvision
from torchvision import transforms
from torch import nn, optim

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

train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transform)

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

# 定义LeNet模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)  # 输出层有10个节点对应0-9的分类

    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

model = LeNet()
criterion = nn.CrossEntropyLoss()  # 使用交叉熵损失函数
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练过程
num_epochs = 10
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        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}%")

这个代码首先导入必要的库，然后加载和预处理MNIST数据，接着定义LeNet网络结构并初始化，之后设置损失函数和优化器。在训练循环中更新权重，并在每个epoch结束后打印损失。最后，测试模型并计算准确率。