帮我用python写一个用LSTNet模型预测多输入单输出的代码
时间: 2024-03-22 20:41:22 浏览: 17
好的,以下是一个使用LSTNet模型预测多输入单输出的Python代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LSTNet(nn.Module):
def __init__(self, input_size, output_size, hidden_size, skip_size, ar_window, cnn_kernel):
super(LSTNet, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.skip_size = skip_size
self.ar_window = ar_window
self.cnn_kernel = cnn_kernel
# CNN layer
self.cnn_layer = nn.Conv2d(1, skip_size, kernel_size=(cnn_kernel, input_size))
# LSTM layer
self.lstm_layer = nn.LSTM(skip_size * (ar_window + 1), hidden_size)
# Fully connected output layer
self.output_layer = nn.Linear(hidden_size, output_size)
def forward(self, x):
# CNN layer
cnn_out = self.cnn_layer(x.unsqueeze(1).unsqueeze(1))
# Reshape CNN output
cnn_out = cnn_out.view(cnn_out.size(0), cnn_out.size(1), -1)
# LSTM layer
lstm_out, _ = self.lstm_layer(cnn_out)
# Fully connected output layer
output = self.output_layer(lstm_out[:, -1, :])
return output
# Define model hyperparameters
input_size = 5
output_size = 1
hidden_size = 64
skip_size = 16
ar_window = 24
cnn_kernel = 3
# Initialize model
model = LSTNet(input_size, output_size, hidden_size, skip_size, ar_window, cnn_kernel)
# Define input tensor
x = torch.randn(32, ar_window + 1, input_size)
# Make prediction
prediction = model(x)
print(prediction)
```
这个模型接受一个大小为 (batch_size, ar_window + 1, input_size) 的输入张量 x,并返回一个大小为 (batch_size, output_size) 的输出张量 prediction。其中,ar_window 表示自回归窗口大小,cnn_kernel 表示 CNN 层卷积核大小。在模型中,我们使用了一个 CNN 层和一个 LSTM 层来学习输入序列的特征,最后通过一个全连接输出层得到单一的预测值。