元学习meta-learning进行图像分类 完整代码

元学习（meta-learning）是一种机器学习范式，允许模型在处理新任务时快速学习和适应。下面是一个使用 Python 和 PyTorch 实现的简单元学习图像分类的示例，其中使用了简单的模型和数据集。

首先，确保你已经安装了必要的库，比如 PyTorch 和 torchvision。

pip install torch torchvision

接下来，这是一个元学习图像分类的基本框架代码，其中使用了 Omniglot 数据集，这是一个常用于元学习任务的小规模数据集。你也可以根据自己的需求更改数据集和模型。

import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from tqdm import tqdm

# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 定义数据转换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载 Omniglot 数据集
train_dataset = datasets.Omniglot(
    root='./data',
    download=True,
    transform=transform
)

# 划分训练集和测试集
train_set, test_set = torch.utils.data.random_split(train_dataset, [3200, 656])

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

# 定义基本模型
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.flatten = nn.Flatten()
        self.fc1 = nn.Linear(1 * 28 * 28, 64)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(64, 5)  # 这里假设有5个类别

    def forward(self, x):
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# 定义元学习算法
def meta_learning(model, optimizer, criterion, epochs, train_loader, test_loader):
    model.to(device)
    for epoch in range(epochs):
        model.train()
        for images, labels in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"):
            images, labels = images.to(device), labels.to(device)
            
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

        model.eval()
        total_correct = 0
        total_images = 0
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            total_correct += (predicted == labels).sum().item()
            total_images += labels.size(0)

        accuracy = total_correct / total_images
        print(f"Epoch {epoch + 1}/{epochs}, Test Accuracy: {accuracy:.4f}")

# 创建模型、优化器和损失函数
model = SimpleModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 运行元学习
meta_learning(model, optimizer, criterion, epochs=5, train_loader=train_loader, test_loader=test_loader)