用python写一个图像分类源码

好的，下面是一个使用Python和PyTorch实现图像分类的源码示例：

# 导入必要的库
import torch
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
from torchvision import transforms, datasets

# 定义数据预处理方式
data_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = datasets.ImageFolder('train', transform=data_transform)
test_dataset = datasets.ImageFolder('test', transform=data_transform)

# 定义数据加载器
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 构建模型
model = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(256 * 28 * 28, 1024),
    nn.ReLU(),
    nn.Linear(1024, 10)
)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print(f'epoch: {epoch + 1}, batch: {i + 1}, loss: {running_loss / 100:.4f}')
            running_loss = 0.0

# 测试模型
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()

print(f'test accuracy: {correct / total:.4f}')

以上代码实现了一个简单的卷积神经网络模型，并使用CIFAR-10数据集进行图像分类训练和测试。你可以根据自己的需求进行修改和优化。