maml元学习pytorch代码

以下是使用PyTorch实现的MAML元学习的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

class MAML(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MAML, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x
    
    def clone(self, device=None):
        clone = MAML(self.input_size, self.hidden_size, self.output_size)
        if device is not None:
            clone.to(device)
        clone.load_state_dict(self.state_dict())
        return clone

class MetaLearner(nn.Module):
    def __init__(self, model, lr):
        super(MetaLearner, self).__init__()
        self.model = model
        self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
        
    def forward(self, x):
        return self.model(x)
    
    def meta_update(self, task_gradients):
        for param, gradient in zip(self.model.parameters(), task_gradients):
            param.grad = gradient
        self.optimizer.step()
        self.optimizer.zero_grad()

def train_task(model, data_loader, lr_inner, num_updates_inner):
    model.train()
    task_loss = 0.0
    for i, (input, target) in enumerate(data_loader):
        input = input.to(device)
        target = target.to(device)
        clone = model.clone(device)
        meta_optimizer = MetaLearner(clone, lr_inner)
        for j in range(num_updates_inner):
            output = clone(input)
            loss = nn.functional.mse_loss(output, target)
            grad = torch.autograd.grad(loss, clone.parameters(), create_graph=True)
            fast_weights = [param - lr_inner * g for param, g in zip(clone.parameters(), grad)]
            clone.load_state_dict({name: param for name, param in zip(clone.state_dict(), fast_weights)})
        output = clone(input)
        loss = nn.functional.mse_loss(output, target)
        task_loss += loss.item()
        grad = torch.autograd.grad(loss, model.parameters())
        task_gradients = [-lr_inner * g for g in grad]
        meta_optimizer.meta_update(task_gradients)
    return task_loss / len(data_loader)

# Example usage
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

input_size = 1
hidden_size = 20
output_size = 1

model = MAML(input_size, hidden_size, output_size)
model.to(device)

data_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(torch.randn(100, input_size), torch.randn(100, output_size)), batch_size=10, shuffle=True)

meta_optimizer = MetaLearner(model, lr=0.001)

for i in range(100):
    task_loss = train_task(model, data_loader, lr_inner=0.01, num_updates_inner=5)
    print('Task loss:', task_loss)
    meta_optimizer.zero_grad()
    task_gradients = torch.autograd.grad(task_loss, model.parameters())
    meta_optimizer.meta_update(task_gradients)

在这个示例中，我们定义了两个类，MAML和MetaLearner。MAML是一个普通的神经网络，而MetaLearner包含了用于更新MAML的元优化器。在每个任务上，我们使用MAML的副本进行内部更新，然后使用元优化器来更新MAML的权重。在元学习的过程中，我们首先通过调用train_task函数来训练一个任务，然后通过调用meta_update函数来更新MAML的权重。