怎么把convlstm的输出和lenet网络结合起来pytorch

要将ConvLSTM的输出和LeNet网络结合起来，需要使用PyTorch中的nn.Sequential()函数将两个网络连接起来。以下是一个示例代码，其中ConvLSTM和LeNet都是自定义的网络模块：

import torch.nn as nn

class ConvLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size):
        super(ConvLSTM, self).__init__()
        # define ConvLSTM layers
        ...

class LeNet(nn.Module):
    def __init__(self, num_classes=10):
        super(LeNet, self).__init__()
        # define LeNet layers
        ...

# combine ConvLSTM and LeNet
class CombinedNet(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size, num_classes=10):
        super(CombinedNet, self).__init__()
        self.convlstm = ConvLSTM(input_size, hidden_size, kernel_size)
        self.lenet = LeNet(num_classes=num_classes)
        self.fc = nn.Linear(10, num_classes)

    def forward(self, x):
        # get ConvLSTM output
        convlstm_output = self.convlstm(x)
        # flatten output and pass through LeNet
        lenet_output = self.lenet(convlstm_output.view(-1, 1, 28, 28))
        # pass through fully connected layer
        output = self.fc(lenet_output)
        return output

在CombinedNet中，我们将ConvLSTM和LeNet连接在一起，并且添加了一个全连接层(fc)以生成最终的输出。在forward()函数中，我们首先将输入(x)传递给ConvLSTM，然后将输出展平并通过LeNet，最后将输出传递给全连接层(fc)。