使用PyTorch构建卷积神经网络（CNN）来训练图像分类器，识别手写数字

好的，以下是使用PyTorch构建卷积神经网络（CNN）来训练图像分类器，识别手写数字的步骤：

1. 导入必要的库和数据集

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 加载手写数字数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 定义批次大小
batch_size = 128

# 加载数据
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

2. 定义CNN模型

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 5, padding=2)
        self.conv2 = nn.Conv2d(32, 64, 5, padding=2)
        self.pool = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(64*7*7, 1024)
        self.fc2 = nn.Linear(1024, 10)
        self.dropout = nn.Dropout(p=0.5)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, 64*7*7)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# 实例化模型
model = CNN()

3. 定义损失函数和优化器

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

4. 训练模型

# 定义训练函数
def train(model, train_loader, criterion, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 训练模型
for epoch in range(1, 11):
    train(model, train_loader, criterion, optimizer, epoch)
    test(model, test_loader, criterion)

5. 测试模型

# 加载测试数据
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True)

# 预测一张手写数字图片
model.eval()
with torch.no_grad():
    for data, target in test_loader:
        output = model(data)
        print('Predicted Label:', output.argmax(dim=1).item())
        break

希望这个例子能对您有所帮助！