手写数字识别综合实践

### 手写数字识别实践项目教程

#### 使用MNIST数据集进行手写数字识别的完整流程

对于手写数字识别的任务，通常采用经典的MNIST数据集作为训练和测试的基础。该数据集由28×28像素的手写数字图像组成，共包含60,000张训练图片以及10,000张测试图片[^1]。

为了确保模型能够有效学习并具备良好的泛化能力，在构建模型之前应当合理划分数据集。当有足够的数据量时，建议按照大约60%、20%、20%的比例分配给训练集、验证集和测试集；然而如果数据有限，则可以考虑使用交叉验证方法来充分利用现有资源。

下面是一个基于PyTorch框架实现简单卷积神经网络(CNN)对手写数字分类的例子：

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt


def load_data(batch_size=64):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    
    train_loader = DataLoader(
        datasets.MNIST('./data', train=True, download=True,
                       transform=transform),
        batch_size=batch_size, shuffle=True)

    test_loader = DataLoader(
        datasets.MNIST('./data', train=False,
                       transform=transform),
        batch_size=batch_size, shuffle=False)
        
    return train_loader, test_loader


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = torch.nn.Linear(320, 50)
        self.fc2 = torch.nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(torch.max_pool2d(self.conv1(x), 2))
        x = torch.relu(torch.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return torch.log_softmax(x, dim=1)


train_loader, test_loader = load_data()
model = Net()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.CrossEntropyLoss()


for epoch in range(10):  
    model.train()
    running_loss = 0.0
    
    for images, labels in train_loader:
        optimizer.zero_grad()
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        
print(f'Finished Training')

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f'Test Accuracy of the network on {total} test images: {accuracy:.2f}%')

这段代码展示了如何加载MNIST数据集，并定义了一个简单的CNN架构用于处理这些图像。通过调整超参数如批次大小(`batch_size`)、迭代次数(`epoch`)等可进一步提升性能表现[^2]。