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
Risposta accettata
Greg Heath
il 9 Ago 2018
Modificato: Greg Heath
il 9 Ago 2018
[ 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
Più risposte (1)
Greg Heath
il 11 Ago 2018
Each case is different. However, things tend to be relatively straightforward if you have at least as many training equations as you have unknowns.
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