MATLAB: Relation between input data points and hyper parameters that needs to be tuned

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Hi All,
Can anyone please let me know the relationship between the number of input data points and the hyperparameters/number of layers that needs to be present in any machine learning model?
Thanks for your time and help

Best Answer

 [ I N]  = size(input)
 [ O N ] = size(target)
 % (MATLAB DEFAULT)
   Ntst    = round(0.15*N)
   Nval    = Ntst
   Ntrn    = N-(Ntst+Nval)%  ~ 0.7*N
 % Design parameters 
   Ndes = Ntrn*O           % No. of design equations ~ 0.7*N*O
   H                       % No. of hidden nodes for I-H-O net
   Nw   = (I+1)*H+(H+1)*O  % No. of unknown weights
 Require  Ndes >= Nw   ==> H <= Hub = (Ntrn*O-O)/(I+O+1)
 Desire   Ndes >> Nw   ==> H << Hub
My typical goal: Minimize H subject to the requirement
         MSE < = 0.01*var(target',1)   % Rsquare >= 0.99
My approach:
 1. Apply the requirement to the training data
 2. Loop over H to find the minimum H to satisfy the 
    requirement.
I have hundreds of examples in the NEWSGROUP comp.soft-sys.matlab as well as ANSWERS.
Hope this helps
Thank you for formally accepting my answer
Greg
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