MATLAB: RNG neural Network and outputs

Deep Learning Toolboxoutputspatternnetrng

Hello everyone.!
I am new in nn so that might be a silly question, but as I change the rng of my nn, the quality of the solution changes too.
For example, for a specific rng , the best setup is using softmax in the second layer, with 11 neurons in the first one. However with a different rng, the best setup is logsig in the second layer with 11 neurons in the first one. What is going on with that?Is there an optimal rng? Also, although I have formulated my output in a 1-c form, the output I get is not binary. Why? I use patternnet, with 10 input categories with 180 responses and 5 output classes.
Thank you all.!

Best Answer

GEH1 = 'Size of input and target matrices and Hub?'
GEH2 = ' Are target columns {0,1} unit vectors?'
GEH3 = 'I find it better to only look at ~ 100 designs at a time : Ntrials ~ numel(Hmin:dH:Hmax) ~ 10. I don"t recall ever having to make more than ~ 200 designs'
GEH4 = ' Too much space is wasted on statements that assign defaults. Accept as many as you can and delete the corresponding statements. goal and min_grad are the only ones I specify'
GEH5 = ' Use dH > 1 and do not use h as a matrix index'
GEH6 = 'defaults TRAINSCG and CROSSENTROPY are usually preferred for classification'
GEH7 = ' Error rates are what what you are trying to minimize. Check out some of my patternnet posts that yield error rates.'
ope tis helps.
Thank you for formally accepting my answer
Greg
Related Question