如何使用PyTorch库构建一个基础的MNIST手写数字分类器？

要使用PyTorch库构建一个基础的MNIST手写数字分类器，你可以按照以下几个步骤操作：

1. **导入所需库**:

import torch
import torchvision
from torchvision import datasets, transforms

2. **数据预处理**:

transform = transforms.Compose([
    transforms.ToTensor(),      # 将图像转换为张量
    transforms.Normalize((0.5,), (0.5,))  # 归一化到0-1之间
])

train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

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=False)

3. **定义模型**:

model = torch.nn.Sequential(
    torch.nn.Linear(784, 128),   # 输入层到隐藏层
    torch.nn.ReLU(),             # 非线性激活函数
    torch.nn.Linear(128, 10),    # 隐藏层到输出层
    torch.nn.LogSoftmax(dim=1)    # 输出层应用softmax用于概率预测
)

4. **损失函数和优化器**:

criterion = torch.nn.NLLLoss()  # 交叉熵损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)  # 使用随机梯度下降优化器

5. **训练模型**:

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()  # 梯度清零
        outputs = model(images.view(-1, 784))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}")

6. **评估模型**:

with torch.no_grad():
    total_correct = 0
    for images, labels in test_loader:
        outputs = model(images.view(-1, 784))
        _, predicted = torch.max(outputs.data, 1)
        total_correct += (predicted == labels).sum().item()

accuracy = total_correct / len(test_dataset)
print(f"Test Accuracy: {accuracy * 100:.2f}%")