假设MINST数据集在E:\MNIST\train-images.idx3-ubyte中，怎么使用pytorch搭建一个七层CNN网络并使用这个MNIST数据集进行训练和测试，写出代码

好的，下面是使用PyTorch搭建一个七层CNN网络并使用MNIST数据集进行训练和测试的代码。注释中会对每个步骤进行说明：

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

# 定义超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),  # 转换为Tensor类型
    transforms.Normalize((0.1307,), (0.3081,))  # 标准化，使得均值为0，标准差为1
])

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

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(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)
        out = self.pool(out)
        out = out.view(-1, 64 * 7 * 7)
        out = self.fc1(out)
        out = self.relu3(out)
        out = self.fc2(out)
        return out

# 实例化模型并定义损失函数和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 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()

        # 每100个batch打印一次训练信息
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
model.eval()  # 进入测试模式，关闭Dropout和BatchNormalization层
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('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

这样，就可以使用PyTorch搭建一个七层CNN网络并使用MNIST数据集进行训练和测试了。