给出一段代码,用pytorch实现cnn lstm attention模型股票价格预测
时间: 2023-04-08 14:05:08 浏览: 397
我可以回答这个问题。以下是一个使用PyTorch实现CNN-LSTM-Attention模型进行股票价格预测的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
class CNN_LSTM_Attention(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, kernel_size, dropout):
super(CNN_LSTM_Attention, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.conv1 = nn.Conv1d(input_size, input_size, kernel_size, padding=kernel_size//2)
self.conv2 = nn.Conv1d(input_size, input_size, kernel_size, padding=kernel_size//2)
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout)
self.fc = nn.Linear(hidden_size, output_size)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = x.permute(0, 2, 1)
x = self.conv1(x)
x = self.conv2(x)
x = x.permute(0, 2, 1)
output, (hidden, cell) = self.lstm(x)
attn_weights = self.softmax(self.fc(hidden[-1]))
context = torch.bmm(output.transpose(1, 2), attn_weights.unsqueeze(2)).squeeze(2)
output = self.fc(context)
return output
# 数据准备
data = np.random.rand(100, 10, 1)
target = np.random.rand(100, 1)
# 模型训练
model = CNN_LSTM_Attention(input_size=1, hidden_size=64, output_size=1, num_layers=2, kernel_size=3, dropout=0.2)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(100):
optimizer.zero_grad()
output = model(torch.Tensor(data))
loss = criterion(output, torch.Tensor(target))
loss.backward()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 100, loss.item()))
# 模型预测
test_data = np.random.rand(1, 10, 1)
with torch.no_grad():
output = model(torch.Tensor(test_data))
print('Predicted price:', output.item())
```
这个模型使用了CNN对输入数据进行特征提取,然后使用LSTM进行序列建模,最后使用Attention机制对LSTM的输出进行加权平均,得到最终的预测结果。
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