使用pytorch框架搭建一个CNN-LSTM网络的代码

以下是使用 PyTorch 框架搭建 CNN-LSTM 网络的代码示例：

import torch
import torch.nn as nn

class CNN_LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(CNN_LSTM, self).__init__()
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.maxpool1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.maxpool2(out)
        out = out.reshape(out.shape[0], -1, out.shape[2])
        h0 = torch.zeros(self.num_layers, out.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, out.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(out, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

其中，`input_size` 表示输入数据的特征数，`hidden_size` 表示 LSTM 隐藏层的大小，`num_layers` 表示 LSTM 层数，`num_classes` 表示分类的类别数。在 `forward` 函数中，首先进行卷积和池化操作，然后将输出的特征图展开成二维张量，再输入到 LSTM 中，最后通过全连接层输出分类结果。