I understand that you have categorical data that you want to input into an LSTM network with 32 dimensions. To achieve this, you can incorporate a word embedding layer before the LSTM layer to convert the words into a 32-dimensional representation. Here is an example code to demonstrate this:
% Assuming you have already prepared your data and defined cX and cY
embeddingDimension = 32; % Dimensionality of the word embedding space
% Create the word embedding layer
embeddingLayer = wordEmbeddingLayer(embeddingDimension, vocabularySize);
% Define the LSTM network architecture
layers = [
sequenceInputLayer(inputSize) % inputSize depends on your data preprocessing
embeddingLayer
lstmLayer(numHiddenUnits, 'OutputMode', 'sequence')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer
];
% Train the LSTM network
options = trainingOptions('adam', 'MaxEpochs', 10);
net = trainNetwork(cX, cY, layers, options);
Please find links to below documentation which I believe will help you for further reference.\
- wordEmbeddingLayer: https://www.mathworks.com/help/textanalytics/ref/nnet.cnn.layer.wordembeddinglayer.html
- sequenceInputLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html
- lstmLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.lstmlayer.html
- fullyConnectedLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.fullyconnectedlayer.html
- trainingOptions: https://www.mathworks.com/help/deeplearning/ref/trainingoptions.html
- trainNetwork: https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html
