用了PyTorch框架来实现AlexNet网络模型，并使用minist数据集进行训练和测试

很好，这是一个不错的练习。下面是一个简单的PyTorch实现AlexNet的代码，以及使用mnist数据集进行训练和测试的代码：

import torch
import torch.nn as nn

class AlexNet(nn.Module):
    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# define the device for the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# define the model
model = AlexNet(num_classes=10).to(device)

# define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# load the mnist dataset
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)

# train the model
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        # move the images and labels to the device
        images = images.to(device)
        labels = labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # backward pass and optimize
        loss.backward()
        optimizer.step()

        # print statistics
        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{10}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')

# evaluate the model
model.eval()
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        # move the images and labels to the device
        images = images.to(device)
        labels = labels.to(device)

        # forward pass
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)

        # calculate accuracy
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy: {100 * correct / total}%')

在这个示例中，我们使用了PyTorch实现了AlexNet模型，并使用mnist数据集进行训练和测试。我们首先定义了AlexNet模型，然后定义了训练所需的损失函数和优化器。接下来，我们使用torchvision加载mnist数据集，并创建了数据加载器。在训练循环中，我们将每个批次的图像和标签移动到设备上，并执行正向传递、反向传递和优化步骤。在测试循环中，我们将模型设置为评估模式，并计算测试集上的精度。