基于MNIST 手写数字识别模型代码

以下是使用 PyTorch 框架基于 MNIST 手写数字识别模型的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义模型结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 定义训练函数
def train(model, device, train_loader, optimizer, 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 = nn.functional.nll_loss(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()))

# 定义测试函数
def test(model, device, test_loader):
    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 += nn.functional.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

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

# 设置训练参数
batch_size = 64
test_batch_size = 1000
epochs = 10
lr = 0.01
momentum = 0.5
seed = 1
log_interval = 100

# 检查是否支持 GPU
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# 加载数据集
torch.manual_seed(seed)
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transform),
    batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transform),
    batch_size=test_batch_size, shuffle=True)

# 初始化模型和优化器
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

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

这段代码会下载 MNIST 数据集并进行训练和测试，训练过程中会输出每个 batch 的 loss 值，测试过程会输出准确率。你可以根据需要调整训练参数，例如学习率、批量大小等，以优化模型性能。