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Windows Crashing during NN training

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mp1994 on 8 Dec 2017
Commented: mp1994 on 3 Jul 2018
Hello folks,
I am writing here because I have a problem with the Neural Network toolbox: during training I have had several crashes of Windows (BSOD). It also just not crash, it freezes on the "sad-smile blue screen" and I have to force the restart manually.
Apparently, the problem regards the for loop that I am doing to test the performance of the network depending on its hidden layer. I am doing something like this...
for h = H
for k = 1:K
% K-Fold Cross-Validation (K=10)
% ANN definition
net = fitnet(h,'trainbr');
net = configure(net,inputs',targets');
net.layers{1}.transferFcn = 'tansig';
net.divideParam.trainRatio =1;
net.divideParam.valRatio = 0;
net.divideParam.testRatio = 0;
net.performFcn = 'mse';
disp(' Training...')
% ANN training
[net,tr] = train(net,inputs',targets');
% Performance evaluation, MSE.
% ...
What's wrong? TIA


Greg Heath
Greg Heath on 8 Dec 2017
Size of inputs, targets, H and K?
Does the code work on the MATLAB EXAMPLE datasets?
help nndatasets
doc nndatasets
mp1994 on 8 Dec 2017
H = 5:12
K = 10
I have checked the code and it works... I have also tried to reduce H (going up to 11), but it crashed anyways... It works quite well instead if I remove the for loop, but I haven't tested for all the values of H... I want the code to do that, as it is supposed to do... The input and target datasets are randomly extracted from a vector of 38420 rows. I am working on a desktop PC, Win10, i7-2600k (at 4.5 GHz), 16 GB RAM (not even at 30%)... CPU temperature always below 60°C...

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Answers (1)

Petorr on 3 Jul 2018
Try closing the figure. I am getting a matlab crash (win7, r2017b) on the next train() call if I don't close the previous training progress figure. For some reason the figure is hidden (see here) so it doesn't turn up with gcf(), but you can get the handle with:
- Peter

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mp1994 on 3 Jul 2018
Thank you for your answer, but I fixed the issue back in the days improving the stability of my overclock... apparently, the problem was that (low Vcore, if I remember correctly).

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