Neural Network generated code gives me different result then my own code
Hi ! Neural Network script generated by Matlab gets some set X and then we set paremters traing set ratio, validation set ratio and test set ratio. In my case this ratio is 0.55/0.15/0.3. However I would like to write script which predict the same number of elements but gets only training set and number h - horizon (how many values neural network should predict).Because I give Neural Network only training set (instead of the whole set) by proportion I should split training set into ratio 0.78/0.22/0.0 (there is no test set so test ratio is 0). function y_predykcja = matlabNeuralNetworkScript(training_set, horizon) %T = simplenarTargets; T= tonndata(training_set,true,false); trainFcn = 'trainlm'; % Levenberg-Marquardt feedbackDelays = 1:2; hiddenLayerSize = 10; net = narnet(feedbackDelays,hiddenLayerSize,'open',trainFcn); net.input.processFcns = {'removeconstantrows','mapminmax'}; [x,xi,ai,t] = preparets(net,{},{},T); net.divideParam.trainRatio = 0.78; net.divideParam.valRatio = 0.22; net.divideParam.testRatio = 0; net.trainParam.showWindow = false; net.performFcn = 'mse'; % Mean squared error % Train the Network [net tr Ys Es Xf Af ] = train(net,x,t,xi,ai,'useParallel','no'); y = net(x,xi,ai); y_predykcja = zeros(1,horizon); for i=1:horizon Xnew = net(x,Xf,Af); Xf = [Xf Xnew]; Xf = Xf(1,2:3); y_predykcja(1,i) = cell2mat(Xf(1,2)); end end My solution is working... but not as good as normal generated by Matlab script. For example I use series load ice_dataset. If I use ntstool where the whole series is divied into ratio 0.55/0.15/0.3 I get MSE 0.02. When I split this data to training_set (0.7 of whole set) and then use my script I get MSE 2. If I use sinus seris MSE of Matlab script is 1.4-e10 in my case is 1.4-e8. Could anybody explain me why ? How to fix my script to get expected accuracy ?
John Michell answered .
2025-11-20
I don't quite follow your logic. You seem to be trying to mimic the effect of using a closed loop configuration. I'm not sure of the validity. However, it seems that the last loop should be something like
Xnew = Ys; Xinew = Xf, Ainew = Af; Ypred = {[]}
for i=1:horizon
[Ynew Xfnew Afnew] = net(Xnew,Xinew,Ainew)
Ypred = [Ypred Ynew ];
Xnew = Ynew; Xinew = Xfnew; Ainew = Afnew;
end