create a lstm text classification model in matlab

To create a LSTM text classification model in Matlab, you can follow these basic steps:

  1. Load and preprocess the text data: You should load the text data and preprocess it by removing stopwords, stemming, tokenizing, and converting words to numeric data.
main.m
documents = preprocessTextData(textdata); % Preprocess the textdata
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  1. Split the data into training, validation, and test sets: You should split the data into training, validation, and test sets to evaluate the model's accuracy.
main.m
[trainInd,valInd,testInd] = dividerand(numDocs,0.6,0.2,0.2); % Divide the data randomly
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  1. Create a text data source: You should create a text data source to interact with text data and to manage text processing.
main.m
ds = textDatastore(textdata, 'IncludeSubfolders',true,'LabelSource','foldernames');
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  1. Define the LSTM layers: You should define the LSTM layers for the text classification model.
main.m
numHiddenUnits = 100;
numClasses = numel(categories(ds.Labels));
layers = [ ...
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
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  1. Train the model: You should train the model with the train data.
main.m
options = trainingOptions('adam', ...
    'MaxEpochs',20, ...
    'MiniBatchSize',64, ...
    'Plots','training-progress');
net = trainNetwork(trainSeq,layers,options);
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  1. Test the model: You should test the model with the test data.
main.m
YTest = classify(net,testSeq);
accuracy = sum(YTest == testTargets)/numel(testTargets);
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By following these steps, you can create a LSTM text classification model in Matlab.

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