cnn

下面是一个使用Python和PyTorch框架实现的CNN图像识别和分类的简单示例。这个例子中，我们将使用CIFAR-10数据集，它包含了10个类别的60000张32x32的彩色图像。

### 步骤1：导入必要的库

```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
```

### 步骤2：定义CNN模型

```python
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)  # 输入通道3，输出通道6，卷积核5x5
        self.pool = nn.MaxPool2d(2, 2)   # 最大池化2x2
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 全连接层
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)  # 输出层，10个类别

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)  # 展平
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x
```

### 步骤3：数据预处理和加载

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

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=4,
                         shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = DataLoader(testset, batch_size=4,
                        shuffle=False, num_workers=2)
```

### 步骤4：初始化模型、损失函数和优化器

```python
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
```

### 步骤5：训练模型

```python
for epoch in range(2):  # 这里只训练2个epoch作为示例
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # 每2000个小批量打印一次
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')
```

### 步骤6：测试模型

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

这个示例展示了如何使用PyTorch构建和训练一个简单的CNN模型来识别CIFAR-10数据集中的图像。在实际应用中，你可能需要调整模型结构、超参数以及训练更多的epoch来提高模型的性能。