使用pytorch将cnn与lstm连接

可以使用PyTorch中的nn.Sequential()模块将一个CNN模型和一个LSTM模型连接起来，实现对序列数据的分类或者预测。具体步骤如下：

1. 定义CNN模型和LSTM模型，分别使用nn.Conv2d()和nn.LSTM()等模块搭建网络。

2. 根据CNN输出的特征图大小，将其压缩成一维向量，以便于LSTM进行处理。

3. 构建一个nn.Sequential()模块，将CNN和LSTM逐层连接起来。

4. 将输入数据传入nn.Sequential()模块进行分类或预测。

具体实现方式可以参考以下代码：

import torch
import torch.nn as nn

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 3, 1, 1)
        self.conv2 = nn.Conv2d(16, 32, 3, 1, 1)
        self.pool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        # x size: (batch_size, channel, height, width)
        # 压成一维向量
        x = x.view(x.size(0), -1)
        return x

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        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):
        # 设置初始隐状态和细胞状态
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        # 前向传播
        out, _ = self.lstm(x, (h0, c0))
        # 解码预测结果
        out = self.fc(out[:, -1, :])
        return out

# 使用nn.Sequential()连接CNN和LSTM
class CNN_LSTM(nn.Module):
    def __init__(self):
        super(CNN_LSTM, self).__init__()
        self.cnn = CNN()
        self.lstm = LSTM(32*7*7, 128, 1, 10)

    def forward(self, x):
        # 前向传播
        x = self.cnn(x)
        x = x.view(-1, 32, 7*7)
        x = self.lstm(x)
        return x

# 使用示例
model = CNN_LSTM()
inputs = torch.randn(4, 3, 32, 32)
outputs = model(inputs)  # outputs size: (4, 10)