在pytorch框架下，利用CNN实现手写数字识别的完整代码

好的，以下是一个基于PyTorch框架实现手写数字识别的完整代码：

import torch
import torch.nn.functional as F
from torchvision import datasets, transforms

# 定义超参数
batch_size = 64
learning_rate = 0.01
num_epochs = 10

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

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 定义CNN模型
class CNN(torch.nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
        self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
        self.fc1 = torch.nn.Linear(7*7*32, 256)
        self.fc2 = torch.nn.Linear(256, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 7*7*32)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

# 实例化模型和优化器
model = CNN()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images
        labels = labels
        outputs = model(images)
        loss = F.nll_loss(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

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

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

在这个代码中，我们首先加载MNIST数据集，然后定义了一个包含两个卷积层和两个全连接层的CNN模型。我们使用负对数似然损失函数和随机梯度下降优化器来训练模型，并在测试集上评估模型的准确率。