基于参数的元学习的代码
时间: 2023-06-11 08:07:14 浏览: 52
以下是一个使用PyTorch实现的基于参数的元学习的示例代码:
```
import torch
import torch.nn as nn
import torch.optim as optim
class MetaLearner(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(MetaLearner, 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
class Learner(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Learner, 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
class MetaLearningModel(nn.Module):
def __init__(self, meta_lr, learner_lr, input_size, hidden_size, output_size):
super(MetaLearningModel, self).__init__()
self.meta_lr = meta_lr
self.learner_lr = learner_lr
self.meta_learner = MetaLearner(input_size, hidden_size, output_size)
self.learner = Learner(input_size, hidden_size, output_size)
self.meta_optimizer = optim.Adam(self.meta_learner.parameters(), lr=meta_lr)
def forward(self, x):
# initialize learner weights
self.learner_weights = self.meta_learner(x)
# train learner on task
learner_optimizer = optim.Adam(self.learner.parameters(), lr=self.learner_lr)
for i in range(num_iterations):
y_pred = self.learner(x, self.learner_weights)
loss = loss_function(y_pred, y_true)
learner_optimizer.zero_grad()
loss.backward()
learner_optimizer.step()
# compute meta-gradients
y_pred = self.learner(x, self.learner_weights)
loss = loss_function(y_pred, y_true)
meta_gradients = torch.autograd.grad(loss, self.learner_weights)
# update meta-learner with meta-gradients
for param, grad in zip(self.meta_learner.parameters(), meta_gradients):
param.data -= self.meta_lr * grad.data
# return final learner predictions
return self.learner(x, self.meta_learner(x))
```
这个模型包含了三个子模型:`MetaLearner`、`Learner` 和 `MetaLearningModel`。`MetaLearner` 用于根据输入的任务数据(即输入输出对)生成一个初始化的学习器权重,`Learner` 用于训练该权重,并返回训练后的预测结果,而 `MetaLearningModel` 则将这两个子模型结合在一起,实现基于参数的元学习。
在 `MetaLearningModel` 的前向传播过程中,首先使用 `MetaLearner` 生成一个初始化的学习器权重,然后使用 `Learner` 在该权重下对输入的任务数据进行训练。在训练完成后,使用训练后的学习器权重对输入数据进行预测,并计算损失。接着,通过反向传播计算出该损失对学习器权重的梯度(即元梯度),并使用该梯度更新 `MetaLearner` 的参数,从而实现对学习器权重的元学习。
需要注意的是,这只是一个简单的示例代码,实际上基于参数的元学习有很多变体和改进,可以根据具体应用场景进行相应的调整和优化。
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