Hi Daniel!
I think the convolution1dLayer is appropriate here, but you might need to do a small piece of data pre-processing to get this to work.
You say your inputs are '250x36 onehot encoded matrices' - presumably this dimension layout corresponds to numFeatures x numObservations. This isn't working with the convolution1dLayer because that layer expects data with three dimensions: the two you have, plus a continuous space or time dimension (that is, it wants data in the format spatialDim x numFeatures x numObservations OR numFeatures x numObservations x numTimeSteps). It is this continuous space or time dimension that the layer will convolve over.
So all you need to do is ensure that the dimension you want to convolve over (the one of size 250) is in the appropriate position. The easiest thing to do is probably to reformat your data as sequences. The fact your data doesn't actually represent a time series doesn't make a difference here, it's just so we can convolve over the right dimension. You can then use a sequenceInputLayer as your input layer - this will require you to put your data into a cell array with numObservations cells. Each cell needs contains one observation of size numFeatures x numTimeSteps. So here we need to make 250 the numTimSteps, and we can just have numFeatures as 1. The line of of code below will do that for you, if your data is a matrix X of size 250x36:
X = rand(250,36);
cellData = num2cell(X',2);
After this, you should be good to go!
Let me know if this makes sense and if you have any other questions, I'd be happy to answer them!
Best,
Tomaso
