Why is my neural network overfitting?
Hello! I’m trying to make a forecast program using neural networks. The training function I’m using is the Bayesian Regularization.
Results are pretty good, but when I see the performance, I notice that the training error has decreased but the Test values didn’t.
In fact, when I test the network with additional new values, the results are pretty awful. I believe that this was because the network became overfitted.
My question is,how can I prevent the ‘trainbr’ function from overfitting? Every time I train the network the error of the values assigned for testing does not decrease.
inputs = tonndata(x,false,false);
targets = tonndata(t,false,false);net = feedforwardnet([15,13],'trainbr');net.trainParam.lr = 0.05;
net.trainParam.mc = 0.9; net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};net.divideFcn = 'dividerand'; net.divideMode = 'time';
net.divideParam.trainRatio = 85/100;
net.divideParam.valRatio = 0/100;
net.divideParam.testRatio = 15/100;
net.layers{1}.transferFcn = 'logsig';
net.layers{2}.transferFcn = 'logsig';net.performFcn = 'mse';net = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
ANSWER
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The best approach for regression is to start with FITNET using as many defaults as possible. The default I-H-O node topology contains Nw = (I+1)*H+(H+1)*O unknown weights. Ntrn training examples yields Ntrneq = Ntrn*O training equations with Ntrndof = Ntrneq-Nw training degrees of freedom. The average variance in the training target examples is MSEtrn00 = mean(var(target’)). Obtaining a mean-square-error lower than MSEtrngoal = 0.01*Ntrndof*MSEtrn00a/Ntrneq for Ntrndof > 0 results in a normalized DOF adjusted MSE of NMSEtrna <= 0.01 and the corresponding adjusted training Rsquared R2trna = 1-NMSEtrna >= 0.99. That is interpreted as the successful modeling of at least 99% of the variation in the target.
The training objective is to try and minimize H with the constraint R2trna >=0.99. This is usually achieved by trial and error over a double for loop with the outer loop of hidden node candidate values h = Hmin:dH:Hmax and an inner loop of i = 1:Ntrials random weight initializations. I have posted many, many examples. Search NEWSGROUP and ANSWERS using
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