input sequence for lstm network

hi - i am sorry for asking many questions on LSTM but i find the implementation very confusing in matlab. I have a 32x305296 array of doubles which is generated by embedding musical note tokens in 32 dimensions. Effectively, we have 305296 notes of dimension 32 each in a time sequence. I need to create a LSTM network to train and predict the next note. (so it's a classification problem rather than regression as the token must be one of the permissible notes). I have set up the NN below. When i run the code I get this error

"Invalid training data. For image, sequence-to-label, and feature classification tasks, responses must be categorical."

Bearing in mind that this is a time sequence of embedded notes and not a group of labels, how do i change the time series of tokens to a permissible type?

layers = BachBotNN(32,32,0.3);

function [layers]=BachBotNN(inputSize,numHiddenUnits,dropoutProb)

layers = [ ...

sequenceInputLayer(inputSize)

lstmLayer(numHiddenUnits,'OutputMode','sequence')

batchNormalizationLayer

dropoutLayer(dropoutProb)

lstmLayer(numHiddenUnits,'OutputMode','sequence')

batchNormalizationLayer

dropoutLayer(dropoutProb)

lstmLayer(numHiddenUnits,'OutputMode','last')

batchNormalizationLayer

dropoutLayer(dropoutProb)

fullyConnectedLayer(1)

softmaxLayer

classificationLayer

]

[Philip Hua]

Don't worry about this. I have managed to figure it out using an example online. Thank you

Answer 1

[Philip Hua]

there is an example of how to do this in scripttunegenerator.mlx. The whole process is not intuitive so it does take a lot of time to figure out something trivial.