Embedding category data for LSTM network

hi - could i ask how one would embed category data for a LSTM network? Suppose I have 108 unique tokens by m columns (each column is a time step). Currently I am passing this into a LSTM network by coverting the tokens to numerical values using double(108xm) and then oneshot as follows

cX = cell(1,1);

cY = cell(1,1);

corpusTokens = split(corpus,'@');

corpusValues = categorical(corpusTokens); % sequence of tokens (by columns)

dTokens = double(corpusValues); % numeric tokens

uniqueTokens = categories(corpusValues); % unique tokens

vocabularySize = numel(uniqueTokens); % number of unique tokens

cX{1}= dummyvar(dTokens(1:end-1))'; % oneshot coding

cY{1}= categorical(corpusTokens(2:end)'); % sequence of tokens (by rows)

cX and cY are then passed into the network. This works fine but I want to embed the input to 32 dimensions. How do you pass the embedded data into the network?

Thank you in advance

Philip

Answer 1

[Yash Sharma]

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