写一个时序预测的MAML算法
时间: 2023-11-18 15:23:16 浏览: 40
MAML(Model-Agnostic Meta-Learning)是一种元学习算法,可以在少量样本的情况下快速适应新任务。以下是一个使用MAML进行时序预测的示例代码:
```python
import torch
import torch.nn as nn
import numpy as np
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size):
super(LSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size)
def forward(self, x):
output, _ = self.lstm(x)
return output[:, -1, :]
class MAML:
def __init__(self, input_size, hidden_size, lr):
self.model = LSTM(input_size, hidden_size)
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=lr)
def predict(self, x):
return self.model(x)
def fast_adapt(self, x, y, lr):
# 在x上进行一步梯度下降
loss = nn.MSELoss()
y_hat = self.model(x)
inner_loss = loss(y_hat, y)
self.optimizer.zero_grad()
inner_loss.backward()
# 更新模型参数
for param in self.model.parameters():
param.data -= lr * param.grad.data
def train(self, tasks, iterations, shots, lr_inner, lr_outer):
# 训练MAML模型
loss = nn.MSELoss()
for i in range(iterations):
total_loss = 0
# 针对每个任务进行训练
for task in tasks:
x_train, y_train, x_test, y_test = task
# 复制模型参数
model_copy = LSTM(self.model.input_size, self.model.hidden_size)
model_copy.load_state_dict(self.model.state_dict())
# 在少量样本上进行快速适应
for j in range(shots):
self.fast_adapt(x_train[j:j+1], y_train[j:j+1], lr_inner)
# 在测试集上进行评估
y_hat = model_copy(x_test)
task_loss = loss(y_hat, y_test)
total_loss += task_loss
# 计算梯度并更新模型参数
task_loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
print('Iteration %d: loss=%.4f' % (i+1, total_loss/len(tasks)))
# 生成示例数据
def generate_data(num_tasks, num_samples, input_dim, output_dim):
tasks = []
for i in range(num_tasks):
# 生成随机的训练集和测试集
x_train = np.random.randn(num_samples, input_dim)
y_train = np.random.randn(num_samples, output_dim)
x_test = np.random.randn(num_samples, input_dim)
y_test = np.random.randn(num_samples, output_dim)
tasks.append((x_train, y_train, x_test, y_test))
return tasks
# 训练MAML模型
input_dim = 4
output_dim = 2
num_tasks = 10
num_samples = 10
hidden_size = 10
lr = 0.1
lr_inner = 0.01
lr_outer = 0.001
iterations = 100
shots = 5
tasks = generate_data(num_tasks, num_samples, input_dim, output_dim)
maml = MAML(input_dim, hidden_size, lr)
maml.train(tasks, iterations, shots, lr_inner, lr_outer)
```
在这个示例中,我们使用LSTM模型进行时序预测,并使用MAML算法进行快速适应。我们首先生成随机的训练集和测试集,然后在这些任务上训练MAML模型。在每次迭代中,我们使用少量样本进行快速适应,然后在测试集上进行评估,并计算损失。最后,我们在所有任务上更新模型参数。
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