基于pytorch,写LSTM深度学习基于时间序列A预测序列B的代码
时间: 2023-03-01 08:00:09 浏览: 135
下面是一份简单的代码,它基于PyTorch实现了LSTM来预测时间序列A的序列B:
```python
import torch
import torch.nn as nn
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(LSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x, hidden):
out, hidden = self.lstm(x, hidden)
out = out[:, -1, :]
out = self.fc(out)
return out, hidden
def init_hidden(self, batch_size):
return (torch.zeros(self.num_layers, batch_size, self.hidden_size),
torch.zeros(self.num_layers, batch_size, self.hidden_size))
model = LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, output_size=output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
hidden = model.init_hidden(batch_size)
for i, (inputs, targets) in enumerate(train_loader):
inputs = inputs.reshape(-1, sequence_length, input_size)
outputs, hidden = model(inputs, hidden)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
这份代码定义了一个LSTM模型,并在训练数据上进行训练。其中,`input_size`表示输入的特征数量,`hidden_size`表示隐藏层的特征数量,`num_layers`表示LSTM层数,`output_size`表示输出的特征数量,`sequence_length`表示序列长度,`num_epochs`表示训练的轮数,`batch_size`表示每次训练的数