使用 Python 实现一个简单的图像分类模型（基于 PyTorch）

在深度学习领域，图像分类是一个基础但非常重要的任务。本文将介绍如何使用 Python 和 PyTorch 框架构建一个简单的卷积神经网络（CNN）来对 CIFAR-10 数据集进行分类。我们将从数据加载、模型构建、训练到评估的整个流程进行讲解，并提供完整的代码示例。

环境准备

首先，确保你的环境中安装了以下库：

pip install torch torchvision matplotlib

我们主要使用 torch 进行张量计算和构建神经网络，torchvision 提供常用的数据集和预训练模型，matplotlib 用于可视化结果。

导入必要的库

import torchimport torch.nn as nnimport torch.optim as optimimport torchvisionimport torchvision.transforms as transformsfrom torch.utils.data import DataLoaderimport matplotlib.pyplot as plt

加载和预处理数据

我们将使用 CIFAR-10 数据集，它包含 60,000 张 32x32 的彩色图片，分为 10 类。

3.1 数据预处理

我们使用 transforms 对图像进行标准化处理：

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

3.2 加载训练集和测试集

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,                                             download=True, transform=transform)test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,                                            download=True, transform=transform)train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)classes = ('plane', 'car', 'bird', 'cat', 'deer',           'dog', 'frog', 'horse', 'ship', 'truck')

我们可以查看一些样本图像：

def imshow(img):    img = img / 2 + 0.5  # unnormalize    plt.imshow(img.permute(1, 2, 0).numpy())    plt.show()dataiter = iter(train_loader)images, labels = next(dataiter)imshow(torchvision.utils.make_grid(images[:4]))print('Labels:', ' '.join(f'{classes[labels[j]]}' for j in range(4)))

构建 CNN 模型

我们将构建一个简单的 CNN，包括两个卷积层和三个全连接层。

class SimpleCNN(nn.Module):    def __init__(self):        super(SimpleCNN, self).__init__()        self.features = nn.Sequential(            nn.Conv2d(3, 6, kernel_size=5),            nn.ReLU(),            nn.MaxPool2d(kernel_size=2),            nn.Conv2d(6, 16, kernel_size=5),            nn.ReLU(),            nn.MaxPool2d(kernel_size=2)        )        self.classifier = nn.Sequential(            nn.Linear(16 * 5 * 5, 120),            nn.ReLU(),            nn.Linear(120, 84),            nn.ReLU(),            nn.Linear(84, 10)        )    def forward(self, x):        x = self.features(x)        x = x.view(-1, 16 * 5 * 5)        x = self.classifier(x)        return xmodel = SimpleCNN()print(model)

定义损失函数和优化器

我们使用交叉熵损失函数和随机梯度下降（SGD）优化器：

criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

训练模型

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device)num_epochs = 5for epoch in range(num_epochs):    running_loss = 0.0    for i, data in enumerate(train_loader, 0):        inputs, labels = data        inputs, labels = inputs.to(device), labels.to(device)        optimizer.zero_grad()        outputs = model(inputs)        loss = criterion(outputs, labels)        loss.backward()        optimizer.step()        running_loss += loss.item()        if i % 200 == 199:            print(f'[Epoch {epoch+1}, Batch {i+1}] Loss: {running_loss / 200:.3f}')            running_loss = 0.0print('Finished Training')

保存模型

训练完成后，我们可以将模型保存下来：

PATH = './simple_cnn.pth'torch.save(model.state_dict(), PATH)

测试模型性能

我们使用测试集评估模型准确率：

correct = 0total = 0with torch.no_grad():    for data in test_loader:        images, labels = data        images, labels = images.to(device), labels.to(device)        outputs = model(images)        _, predicted = torch.max(outputs.data, 1)        total += labels.size(0)        correct += (predicted == labels).sum().item()print(f'Accuracy of the network on the 10000 test images: {100 * correct / total:.2f}%')

可视化预测结果

我们还可以可视化一些预测结果：

dataiter = iter(test_loader)images, labels = next(dataiter)images, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs, 1)imshow(torchvision.utils.make_grid(images.cpu()[:4]))print('Predicted:', ' '.join(f'{classes[predicted[j]]}' for j in range(4)))print('Actual:   ', ' '.join(f'{classes[labels[j]]}' for j in range(4)))

十、总结与展望

本文演示了如何使用 PyTorch 构建一个简单的 CNN 图像分类模型，并完成了从数据加载、模型训练到评估的全过程。虽然我们的模型结构较为简单，但在 CIFAR-10 上已经能够达到约 60% 左右的准确率。当然，这只是一个入门级的例子。

为了进一步提升性能，你可以尝试以下改进：

随着你对深度学习理解的加深，图像分类任务将成为你探索计算机视觉领域的第一步。希望这篇文章对你有所帮助！

完整代码汇总如下：

import torchimport torch.nn as nnimport torch.optim as optimimport torchvisionimport torchvision.transforms as transformsfrom torch.utils.data import DataLoaderimport matplotlib.pyplot as plt# 数据预处理transform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])# 加载数据集train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,                                             download=True, transform=transform)test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,                                            download=True, transform=transform)train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)classes = ('plane', 'car', 'bird', 'cat', 'deer',           'dog', 'frog', 'horse', 'ship', 'truck')# 显示图像函数def imshow(img):    img = img / 2 + 0.5  # unnormalize    plt.imshow(img.permute(1, 2, 0).numpy())    plt.show()# 查看部分图像dataiter = iter(train_loader)images, labels = next(dataiter)imshow(torchvision.utils.make_grid(images[:4]))print('Labels:', ' '.join(f'{classes[labels[j]]}' for j in range(4)))# 定义模型class SimpleCNN(nn.Module):    def __init__(self):        super(SimpleCNN, self).__init__()        self.features = nn.Sequential(            nn.Conv2d(3, 6, kernel_size=5),            nn.ReLU(),            nn.MaxPool2d(kernel_size=2),            nn.Conv2d(6, 16, kernel_size=5),            nn.ReLU(),            nn.MaxPool2d(kernel_size=2)        )        self.classifier = nn.Sequential(            nn.Linear(16 * 5 * 5, 120),            nn.ReLU(),            nn.Linear(120, 84),            nn.ReLU(),            nn.Linear(84, 10)        )    def forward(self, x):        x = self.features(x)        x = x.view(-1, 16 * 5 * 5)        x = self.classifier(x)        return xmodel = SimpleCNN()# 损失函数和优化器criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)# 训练模型device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device)num_epochs = 5for epoch in range(num_epochs):    running_loss = 0.0    for i, data in enumerate(train_loader, 0):        inputs, labels = data        inputs, labels = inputs.to(device), labels.to(device)        optimizer.zero_grad()        outputs = model(inputs)        loss = criterion(outputs, labels)        loss.backward()        optimizer.step()        running_loss += loss.item()        if i % 200 == 199:            print(f'[Epoch {epoch+1}, Batch {i+1}] Loss: {running_loss / 200:.3f}')            running_loss = 0.0print('Finished Training')# 保存模型PATH = './simple_cnn.pth'torch.save(model.state_dict(), PATH)# 测试模型correct = 0total = 0with torch.no_grad():    for data in test_loader:        images, labels = data        images, labels = images.to(device), labels.to(device)        outputs = model(images)        _, predicted = torch.max(outputs.data, 1)        total += labels.size(0)        correct += (predicted == labels).sum().item()print(f'Accuracy of the network on the 10000 test images: {100 * correct / total:.2f}%')# 可视化预测结果dataiter = iter(test_loader)images, labels = next(dataiter)images, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs, 1)imshow(torchvision.utils.make_grid(images.cpu()[:4]))print('Predicted:', ' '.join(f'{classes[predicted[j]]}' for j in range(4)))print('Actual:   ', ' '.join(f'{classes[labels[j]]}' for j in range(4)))

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