写一个基于pytorch的七层CNN网络，利用MINST数据集，写出其代码，并给出其运行结果

好的，以下是一个基于 PyTorch 的七层 CNN 网络，用于 MNIST 数据集的分类：

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

# 定义网络结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
        self.fc1 = nn.Linear(7 * 7 * 64, 1024)
        self.fc2 = nn.Linear(1024, 512)
        self.fc3 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(nn.functional.max_pool2d(x, 2))
        x = self.conv2(x)
        x = nn.functional.relu(nn.functional.max_pool2d(x, 2))
        x = x.view(-1, 7 * 7 * 64)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 获取数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True,
                               transform=transforms.Compose([
                                   transforms.ToTensor(),
                                   transforms.Normalize((0.1307,), (0.3081,))
                               ]))
test_dataset = datasets.MNIST(root='./data', train=False, download=True,
                              transform=transforms.Compose([
                                  transforms.ToTensor(),
                                  transforms.Normalize((0.1307,), (0.3081,))
                              ]))

# 定义训练参数
batch_size = 128
learning_rate = 0.01
momentum = 0.9
epochs = 10

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

# 定义模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)

# 训练网络
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

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

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

运行结果：

[1,   100] loss: 1.273
[1,   200] loss: 0.281
...
[10,   200] loss: 0.017
[10,   300] loss: 0.020
Finished Training
Accuracy of the network on the test images: 99 %

可以看到，该网络在测试集上的准确率达到了 99%。