写一段元学习的回归代码吧
时间: 2023-03-23 19:01:03 浏览: 56
当然,我很乐意为您提供元学习的回归代码。以下是一段基于PyTorch的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class MetaLearner(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(MetaLearner, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.relu2 = nn.ReLU()
self.fc3 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.fc1(x)
x = self.relu1(x)
x = self.fc2(x)
x = self.relu2(x)
x = self.fc3(x)
return x
class MetaRegression(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(MetaRegression, self).__init__()
self.meta_learner = MetaLearner(input_size, hidden_size, output_size)
def forward(self, x, alpha):
out = self.meta_learner(x)
theta = {}
for name, param in self.meta_learner.named_parameters():
theta[name] = param - alpha * param.grad
for name, param in self.named_parameters():
param.data = theta[name]
out = self.meta_learner(x)
return out
def train(model, train_loader, meta_optimizer, task_lr):
criterion = nn.MSELoss()
for x, y in train_loader:
x = x.float()
y = y.float()
for i in range(x.shape[0]):
output = model(x[i], task_lr)
loss = criterion(output, y[i])
grad = torch.autograd.grad(loss, model.parameters(), create_graph=True)
model.meta_learner.zero_grad()
for j, param in enumerate(model.meta_learner.parameters()):
param.grad = grad[j]
meta_optimizer.step()
def test(model, test_loader):
criterion = nn.MSELoss()
total_loss = 0.0
for x, y in test_loader:
x = x.float()
y = y.float()
output = model(x, 0.0)
loss = criterion(output, y)
total_loss += loss.item()
return total_loss / len(test_loader)
if __name__ == '__main__':
input_size = 1
hidden_size = 64
output_size = 1
meta_lr = 1e-3
task_lr = 1e-2
meta_batch_size = 16
task_batch_size = 10
num_epochs = 10
num_tasks = 5
meta_model = MetaRegression(input_size, hidden_size, output_size)
meta_optimizer = optim.Adam(meta_model.parameters(), lr=meta_lr)
for epoch in range(num_epochs):
total_loss = 0.0
for task in range(num_tasks):
train_loader = DataLoader(torch.randn(task_batch_size, input_size), torch.randn(task_batch_size, output_size), batch_size=task_batch_size)
test_loader = DataLoader(torch.randn(task_batch_size, input_size), torch.randn(task_batch_size, output_size), batch_size=task_batch_size)
train(meta_model, train_loader, meta_optimizer, task_lr)
loss = test(meta_model, test_loader)
total_loss += loss
print(f'Epoch {epoch}: