pytorch实现cnn—lstm代码
时间: 2023-10-26 14:21:45 浏览: 45
以下是一个简单的 PyTorch CNN-LSTM 实现代码示例:
```python
import torch
import torch.nn as nn
class CNNLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(CNNLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.lstm = nn.LSTM(128 * 7 * 7, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
# CNN layers
x = self.conv1(x)
x = nn.ReLU()(x)
x = self.pool(x)
x = self.conv2(x)
x = nn.ReLU()(x)
x = self.pool(x)
# Flatten
x = x.view(-1, 128 * 7 * 7)
# LSTM layers
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
# Fully connected layer
out = self.fc(out[:, -1, :])
return out
```
在这个实现中,我们首先定义了 CNN 层,然后将输出展平为一维张量。接下来,我们将输入传递到 LSTM 层中,并使用最后一个时间步的输出进行分类。最后,我们返回分类输出。
请注意,此代码仅供参考,实际实现可能因数据集和任务而异。