CNN-LSTM的matlab代码

以下是一个使用Matlab实现CNN-LSTM的示例代码：

% Load the data
load('trainData.mat');
load('testData.mat');

% Set the sequence length
sequenceLength = 50;

% Define the CNN layers
inputLayer = imageInputLayer([28 28 1]);
convLayer1 = convolution2dLayer(5,20);
reluLayer1 = reluLayer();
poolLayer1 = maxPooling2dLayer(2,'Stride',2);
convLayer2 = convolution2dLayer(5,50);
reluLayer2 = reluLayer();
poolLayer2 = maxPooling2dLayer(2,'Stride',2);
cnnLayers = [
    inputLayer
    convLayer1
    reluLayer1
    poolLayer1
    convLayer2
    reluLayer2
    poolLayer2
    ];

% Define the LSTM layers
numHiddenUnits = 100;
numClasses = 10;
lstmLayers = [
    sequenceInputLayer([28 28 1])
    sequenceFoldingLayer('Method','last')
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
    ];

% Combine the CNN and LSTM layers
layers = [
    cnnLayers
    sequenceInputLayer([28 28 1 sequenceLength])
    lstmLayers
    ];

% Set the training options
options = trainingOptions('sgdm', ...
    'MaxEpochs',10, ...
    'MiniBatchSize',128, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress');

% Train the network
net = trainNetwork(trainData,trainLabels,layers,options);

% Test the network
predictedLabels = classify(net,testData);
accuracy = sum(predictedLabels == testLabels)/numel(testLabels);

在这个示例中，我们首先加载了训练和测试数据，然后定义了CNN和LSTM层。接下来，我们将这些层组合在一起，并使用训练数据对模型进行训练。最后，我们使用测试数据对模型进行测试，并计算模型的准确率。

请注意，这只是一个简单的CNN-LSTM模型示例。你可以根据你的需求和数据集来定义更复杂的CNN-LSTM架构和训练选项。