Finding best neural network structure using optimization algorithms and cross-validation

 Hi.

 
I'm using optimization algorithm to find best structure+inputs of a 'patternnet' neural network in MATLAB R2014a using 5-fold cross validation. Where should i initialize weights of my neural network?
 
 
 *Position_1(for weight initialization)*

 for i=1:num_of_loops
 *Position_2(for weight initialization)* 

 - repeating cross validation
 for i=1:num_of_kfolds
 *Position_3(for weight initialization)*
 - Cross validation loop

 end
  end
I'm repeating 5-fold cross validation (because random selection of cross validation) to have more reliable outputs (average of neural network outputs). Which part is better for weight initialization (Position_1,Position_2 or Position_3) and why?


ANSWER



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To help understanding, I will assume Nval = Ntst = 0. Search for the nonzero examples in the NEWSGROUP and ANSWERS.
 
To design a typical I-H-O net with Ntrn training examples, try to not let the number of unknown weights
 
 Nw = (I+1)*H+(H+1)*O

exceed the number of training equations

 Ntrneq = Ntrn*O

This will occur as long as H <= Hub where Hub is the upperbound

 Hub = -1+ceil( (Ntrneq-O) / (I+O+1) )

Based on Ntrneq and Hub I decide on a set of numH candidate values for H

 0 <= Hmin:dH:Hmax <= Hmax

 numH = numel(Hmin:dH:Hmax)

and the number of weight initializations for each value of H, e.g.,


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