- Try not to have have more unknown weights than training equations (OVERFITTING)because there is no guarantee that you will get good performance on nontraining data.
- Rank multiple designs by the performance on validation data
- Obtain unbiased performance estiamtes using the performance on test data.
- It is preferrable to have more training equations than unknown weights. Therefore it is desirable to use as few hidden nodes as possible.
- There is a default 0.7/0.15/0.15 RANDOM Ntrn/Nval/Ntst data division
- The default initial weights are RANDOM
- Given a sufficient number of hidden nodes, you may have to train 10 or 20 nets to obtain one that yields a good set of initial weights.
[ I N ] = size(input) % [ 4800 90 ]
[ O N ] = size(target) % [ 15 90 ]
Ntst = round(0.15*N) % 14
Nval = Ntst % 14
Ntrn = N - Nval-Ntst % 62
Ntrneq = Ntrn*O % 930 training equations
H = 10 % default # of training equations
Nw = (I+1)*H+(H+1)*O = 48175 % Unknown weights
You have more than 50 times as many weights than you have equations (SEVERE OVERFITTING). You should try to use image feature extraction to drastically reduce the number of inputs.
Also, use the training record tr to separate training, validation and testing performance.
Hope this helps.
Thank you for formally accepting my answer
Greg
PS: I have zillions of patternnet posts in the NEWSGROUP and ANSWERS. For initial searches try
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