读取pytorch自带的手写字体识别数据集，运行vggnet网络训练和测试，得到输出结果

首先，我们需要导入PyTorch库和手写字体识别数据集。 ```python import torch import torchvision import torchvision.transforms as transforms import torch.nn as nn import torch.optim as optim import matplotlib.pyplot as plt import numpy as np ``` 然后，我们加载手写字体识别数据集，并对数据进行预处理。 ```python transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) classes = tuple(str(i) for i in range(10)) ``` 接下来，我们定义一个VGG网络模型。 ```python class VGGNet(nn.Module): def __init__(self): super(VGGNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(1, 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.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 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=2, stride=2), nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.classifier = nn.Sequential( nn.Linear(512 * 3 * 3, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 10) ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x ``` 接下来，我们定义损失函数和优化器。 ```python net = VGGNet() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) ``` 然后，我们开始训练网络。 ```python for epoch in range(2): # loop over the dataset multiple times running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 2000 == 1999: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0 print('Finished Training') ``` 我们测试训练好的网络。 ```python correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %d %%' % ( 100 * correct / total)) ``` 通过以上代码，我们可以得到VGGNet网络在手写字体识别数据集上的训练和测试结果。 [2024-10-21 23:42:05 | AI写代码神器 | 2483点数解答]