能不能用matlab帮我编写lstm和注意力机制结合的代码

作为AI语言模型，我可以提供一些lstm和注意力机制的代码示例，但是这需要您提供更具体的任务和数据集信息，以便我能够更好地为您提供帮助。以下是一个简单的示例代码：

% Load data
trainData = load('trainData.mat');
trainLabels = load('trainLabels.mat');
testData = load('testData.mat');
testLabels = load('testLabels.mat');

% Define LSTM network architecture
numFeatures = size(trainData,2);
numClasses = numel(unique(trainLabels));
numHiddenUnits = 100;
layers = [
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
];

% Train LSTM network
options = trainingOptions('adam', ...
    'MaxEpochs',30, ...
    'MiniBatchSize',64, ...
    'InitialLearnRate',0.001, ...
    'GradientThreshold',1, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress');
net = trainNetwork(trainData,trainLabels,layers,options);

% Define attention mechanism
attentionLayer = attentionLayer(numHiddenUnits, numFeatures);

% Combine LSTM network and attention mechanism
combinedLayers = [
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    attentionLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
];

% Train combined network
options = trainingOptions('adam', ...
    'MaxEpochs',30, ...
    'MiniBatchSize',64, ...
    'InitialLearnRate',0.001, ...
    'GradientThreshold',1, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress');
net_combined = trainNetwork(trainData,trainLabels,combinedLayers,options);

% Evaluate trained network on test data
predictedLabels = classify(net,testData);
accuracy = sum(predictedLabels == testLabels)/numel(testLabels);
disp(['Test accuracy: ', num2str(accuracy)]);