### 基于PyTorch框架的测试代码示例 对于希望了解如何使用PyTorch编写简单神经网络模型并对其进行训练和评估的需求，下面提供了一个基础的例子。此例子展示了定义一个简单的全连接层神经网络，并执行基本的数据加载、前向传播以及反向传播过程。 ```python import torch from torch import nn, optim from torchvision import datasets, transforms transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) # 加载MNIST数据集作为示范 trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) model = nn.Sequential(nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 10), nn.LogSoftmax(dim=1)) criterion = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=0.003) epochs = 5 for e in range(epochs): running_loss = 0 for images, labels in trainloader: # Flatten MNIST images into a 784 long vector images = images.view(images.shape[0], -1) optimizer.zero_grad() output = model(images) loss = criterion(output, labels) loss.backward() optimizer.step() running_loss += loss.item() else: print(f"Training loss: {running_loss/len(trainloader)}") dataiter = iter(trainloader) images, labels = dataiter.next() img = images[0].view(1, 784) with torch.no_grad(): logps = model(img) ps = torch.exp(logps) probab = list(ps.numpy()[0]) print(f'Predicted Digit: {probab.index(max(probab))}') ``` 上述代码片段创建了一个小型多层感知器来处理手写数字识别任务(MNIST)，并通过几个epoch的学习调整权重参数以最小化损失函数[^1]。