create a lstm text classification model in matlab

main.m
documents = preprocessTextData(textdata); % Preprocess the textdata
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main.m
[trainInd,valInd,testInd] = dividerand(numDocs,0.6,0.2,0.2); % Divide the data randomly
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main.m
ds = textDatastore(textdata, 'IncludeSubfolders',true,'LabelSource','foldernames');
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main.m
numHiddenUnits = 100;
numClasses = numel(categories(ds.Labels));
layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
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main.m
options = trainingOptions('adam', ...
    'MaxEpochs',20, ...
    'MiniBatchSize',64, ...
    'Plots','training-progress');
net = trainNetwork(trainSeq,layers,options);
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main.m
YTest = classify(net,testSeq);
accuracy = sum(YTest == testTargets)/numel(testTargets);
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