读取pytorch自带的手写字体识别数据集，分别写出vggnet网络模型，训练和测试代码

```python import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import torchvision.datasets as datasets import torch.optim as optim import torch.nn.functional as F from torch.utils.data import DataLoader # Load MNIST dataset 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=True) train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True) test_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False) # Define VGGNet model class VGGNet(nn.Module): def __init__(self): super(VGGNet, self).__init__() self.conv_layers = nn.Sequential( nn.Conv2d(1, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) self.fc_layers = nn.Sequential( nn.Linear(128*7*7, 512), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(512, 512), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(512, 10) ) def forward(self, x): x = self.conv_layers(x) x = x.view(x.size(0), -1) x = self.fc_layers(x) return x # Initialize the model model = VGGNet() # Define loss function and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Training num_epochs = 10 for epoch in range(num_epochs): model.train() for images, labels in train_loader: optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}') # Testing model.eval() 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() accuracy = correct / total print(f'Test Accuracy: {accuracy}') ``` 这段代码实现了使用VGGNet模型对MNIST手写数字进行识别的训练和测试过程。在训练过程中，使用了交叉熵损失函数和随机梯度下降优化器进行模型优化，共进行了10个epoch的训练。在测试过程中，通过测试集计算了模型的准确率。 [2024-10-17 18:34:13 | AI写代码神器 | 1873点数解答]