Good Afternoon,
Looking around ANSWER and exploring GOOGLE GROUPS i found this method by Dr. Greg Heath to define a valid training goal for the MSE performance function:
[I,N]=size(x); [O,N]=size(t); MSE00a=mean(var(t,0,2)); Ntrn=floor(0.7*N); Hub=floor((Ntrn-O)/(I+1+O)); MSEgoal=0.01*(Ndof/Ntrneq)*MSE00a;
And i was wondering if there is a similar method to set a Crossentropy reference goal for neural net performance, since i want to experiment different type of loss functions in order to get the best results.
ANSWER
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These equations are not necessarily precise.For example:data = design + testdesign = training + validationIn particular:Test subset data should not be used to estimate design parameters.However, since we typically let the training function randomly perform the trn/val/tst division, the separate train/val/tst subsets are not available before training.That is why I typically design 10 nets for every trial value for the number of hidden nodes.
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These equations are not necessarily precise.
For example:
data = design + test
design = training + validation
In particular:
Test subset data should not be used to estimate design parameters.
However, since we typically let the training function randomly perform the trn/val/tst division, the separate train/val/tst subsets are not available before training.
That is why I typically design 10 nets for every trial value for the number of hidden nodes.
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