本项目将引导你使用 PyTorch 框架来实现一个手写数字识别系统。我们将从数据预处理开始，逐步构建模型，并进行训练和测试。

项目目标

  • 理解 PyTorch 的基本使用方法
  • 学习如何构建神经网络模型
  • 掌握模型训练和评估的基本流程

数据集

我们使用的是 MNIST 数据集，这是一个包含 60000 个训练样本和 10000 个测试样本的手写数字数据集。

步骤

  1. 导入库和加载数据集

    import torch
from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

  2. 定义模型

    import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.fc1 = nn.Linear(9216, 128)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

model = Net()

  3. 训练模型

    import torch.optim as optim

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

for epoch in range(10):  # loop over the dataset multiple times
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = F.nll_loss(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:    # print every 100 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

  4. 评估模型

    correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(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))

扩展阅读

更多关于 PyTorch 的信息，请访问我们的官方文档：PyTorch 官方文档。

图片展示

MNIST Sample Digits