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for evaluating NN performance for a given number of trail or retrain which approach is right and why?????
for trail=1:100 net=newff(....); [net,tr,Y,E,Pf,Af] = train(...); ......; end
for trail=1:100 [net,tr,Y,E,Pf,Af] = train(...); ........; end
Note: i am getting decent result for both approach; but the later giving me best result.
The first example is the correct one because it containss 100 random weight initializations. Therefore each net is a valid independent result.
The 2nd example just keeps training the same net more and more.
What, exactly, do you mean by decent results?
Is this regression or classification?
Are you using validation stopping?
How many acceptable solutions out of 100?
If regression, what are the means and standard deviations of the training, validation and testing NORMALIZED (with average target variance) mean-square-error?
I usually shoot for (but don't always get) NMSEtrn <= 0.01
For an I-H-O net
Ntrneq = prod(size(ttrn)) % Ntrn*O = No. of training equations
Nw = (I+1)*H +(H+1)*O % No. of unknown weights
NMSEtrn = sse(trn-ytrn)/(Ntrneq-Nw)/mean(var(ttrn',0))
NMSEi = mse(yi-ti)/mean(var(ti',1)) for i = val and test
I have posted many example in NEWSGROUP and ANSWERS. Try searching on
heath newff Ntrials
Hope this helps.
Thank you for formally accepting my answer.