基于pytorch的cnn-lstm模型一维数据代码
时间: 2024-01-21 12:03:12 浏览: 34
下面是一个基于PyTorch的CNN-LSTM模型的一维数据代码示例:
```python
import torch
import torch.nn as nn
class CNNLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, kernel_size, dropout):
super(CNNLSTM, self).__init__()
# CNN layer
self.conv1d = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=kernel_size, stride=1, padding=1)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool1d(kernel_size=2)
# LSTM layer
self.lstm = nn.LSTM(input_size=16, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout)
# Fully connected layer
self.fc = nn.Linear(hidden_size, 1)
def forward(self, x):
# CNN layer
x = self.conv1d(x)
x = self.relu(x)
x = self.maxpool(x)
# Reshape the output of CNN layer
batch_size, channels, seq_length = x.size()
x = x.view(batch_size, seq_length, channels)
# LSTM layer
h0 = torch.zeros(num_layers, x.size(0), hidden_size).to(device)
c0 = torch.zeros(num_layers, x.size(0), hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
# Fully connected layer
out = self.fc(out[:, -1, :])
return out
```
在这个模型中,首先使用了一个CNN层来提取输入数据的特征。然后,将CNN层的输出重塑为LSTM层的输入形状。最后,将LSTM层的输出传递给一个全连接层,以获得最终的预测结果。