Algorithm Pseudo Code for Estimating an Optimal Configuration for n hidden layers

arrsizes =emptyList([])

niter = n

for i ← [1,2,..niter] do

for numNeurons = 5;numNeurons+= 5;numNeurons≤ 1000 do

for kfold in 10 fold Cross Validation do

models←MLPClassifier(hiddenLayerSizes=arrsizes.append(numNeurons), activation =‘ReLu’, solver=‘Adam’, maxIterations=1000)

{Now train on k-1 folds and test on kth fold}

model.fit([1fold, 2fold, …k – 1fold], trainLabels)

{Add all confusion matrices for k-folds}

confusionMatrix←confusionMatrix+confMat(testLabels, model.predict(kth fold))

end for

{Store the number of neurons for which we have the maximum f1 score}

if confusionMatrix.f1 > max f1 then

max f1 ←confusionMatrix.f1

bestNumNeurons← numNeurons

end if

end for

arrsizes[i]←bestNumNeurons

end for