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Neural-Net​work-Perfo​rmance Paradox (WITH PICTURES!)

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Detlef Preis
Detlef Preis 2015-7-15
关闭： MATLAB Answer Bot 2021-8-20
Hello,
I observed a strange behaviour of my neural network.
Training:
-> As you see, the performance(MSE) is pretty good in training, validation AND testing. But look what happens, when I test the network with more data, that I cut out from the dataset before training.
Testing:
-> The performance is terrible! You would assume, that the performance is more or less the same as in the training-testset, because both testsets are from the same dataset and doesn't influence the training, but they are not.
Has anybody an explanation?
Kind regards, Detlef

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回答（1 个）

Greg Heath
Greg Heath 2015-7-16
编辑：Walter Roberson 2015-7-18
Rnew looks good but MSEnew looks about 60 times too large. Something is wrong.
MSEnew has the symptoms of overtraining an overfit net. Are there more unknown weights Nw than training equations Ntrneq?
[ I N ] = size(input), [ O N ] = size(target)
Ntst = round(0.15*N), Nval = Ntst,
Ntrn = N - 2*Ntst, Ntrneq = Ntrn*O
For an I-H-O node topology
Nw = ( I + 1 ) * H + ( H + 1 )*O
Ntrneq >= Nw when
H <= (Ntrneq - O ) / ( I + O + 1) % 19442
I assume that you don't have anywhere near that many hidden nodes.
How may do you have?
How much training time?
What was the stopping criterion tr.stop?
Try repeating with other randomizations of the data.
However, with a data set that large, I would try
Nnew = Ntst = Nval = Ntrn
Hope this helps.
Greg
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Detlef Preis
Detlef Preis 2015-7-17
Yes ok, I understand.
But I still do not get it why the integrated tool-test performs way better than the extra-test afterwards...
Greg Heath
Greg Heath 2015-7-18
I think you should be more concerned that the original data has
R ~ 0.996 with MSE ~ 45
whereas the new data has
R ~ 0.956 with MSE ~ 2705
It doesn't make sense.
What is the mean variance of both targets?
Remember
R ~ sqrt( 1 - MSE/mean(var(target',1))
ope this helps.
Greg

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