写一个元学习的pytorch代码
时间: 2024-03-21 17:14:28 浏览: 21
以下是一个简单的元学习模型的 PyTorch 代码示例:
```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.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
class Learner(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Learner, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
class MetaLearningDataset(torch.utils.data.Dataset):
def __init__(self, num_tasks, num_samples_per_task):
self.num_tasks = num_tasks
self.num_samples_per_task = num_samples_per_task
self.tasks = []
for i in range(num_tasks):
task = []
for j in range(num_samples_per_task):
x = torch.randn(1, 1)
y = x**2
task.append((x, y))
self.tasks.append(task)
def __len__(self):
return self.num_tasks
def __getitem__(self, index):
return self.tasks[index]
meta_lr = 1e-3
inner_lr = 1e-2
num_tasks = 5
num_samples_per_task = 10
num_epochs = 10
meta_learner = MetaLearner(1, 10, 1)
meta_optimizer = optim.Adam(meta_learner.parameters(), lr=meta_lr)
meta_learning_dataset = MetaLearningDataset(num_tasks, num_samples_per_task)
meta_learning_dataloader = DataLoader(meta_learning_dataset, batch_size=1, shuffle=True)
for epoch in range(num_epochs):
for tasks in meta_learning_dataloader:
# Initialize the meta-gradients to zero
meta_optimizer.zero_grad()
# For each task, compute the loss on the train set and backpropagate
for task in tasks:
learner = Learner(1, 10, 1)
optimizer = optim.SGD(learner.parameters(), lr=inner_lr)
for x, y in task:
# Compute the loss on the train set
y_hat = learner(x)
loss = nn.MSELoss()(y_hat, y)
# Backpropagate and update the learner weights
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Compute the loss on the validation set
val_loss = 0
for x, y in task:
y_hat = learner(x)
val_loss += nn.MSELoss()(y_hat, y)
val_loss /= num_samples_per_task
# Compute the meta-gradients using the validation loss
val_loss.backward()
# Update the meta-learner weights using the meta-gradients
meta_optimizer.step()
```
上面的代码实现了一个简单的元学习框架,其中 MetaLearner 是元学习器,Learner 是元学习器使用的子学习器。MetaLearningDataset 是元学习数据集,其中每个任务都是一个包含 num_samples_per_task 个样本的数据集。在每个元学习迭代中,我们首先对每个任务进行 num_epochs 次训练和验证,然后使用验证集的损失来更新元学习器的权重。
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