cnn-lstm 代码matlab

以下是一个简单的CNN-LSTM网络的MATLAB代码示例：

%加载数据
load('imdb.mat')

%定义CNN层
layers = [
    sequenceInputLayer(inputSize) %输入层
    convolution2dLayer([3 3],64,'Padding','same') %卷积层1
    batchNormalizationLayer %BatchNorm层1
    reluLayer %激活函数层1
    maxPooling2dLayer([2 2],'Stride',2) %池化层1
    convolution2dLayer([3 3],128,'Padding','same') %卷积层2
    batchNormalizationLayer %BatchNorm层2
    reluLayer %激活函数层2
    maxPooling2dLayer([2 2],'Stride',2) %池化层2
    ];

%定义LSTM层
numHiddenUnits = 128; %LSTM单元数
lstmLayer = lstmLayer(numHiddenUnits,'OutputMode','last');

%连接CNN和LSTM层
layers = [
    layers %CNN层
    sequenceFoldingLayer('Name','fold') %序列折叠层
    lstmLayer %LSTM层
    sequenceUnfoldingLayer('Name','unfold') %序列展开层
    fullyConnectedLayer(2) %全连接层
    softmaxLayer %Softmax层
    classificationLayer %分类层
    ];

%定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs',5, ...
    'MiniBatchSize',64, ...
    'SequenceLength','longest', ...
    'Shuffle','every-epoch', ...
    'Verbose',false, ...
    'Plots','training-progress');

%训练网络
net = trainNetwork(trainData,layers,options);

其中，`inputSize`是输入图像的大小，`numHiddenUnits`是LSTM单元数，`trainData`是训练数据。这个示例只是一个简单的CNN-LSTM网络，你可以根据你的具体应用需求对网络进行调整。