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

2018 8 9
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更新时间：2018 8 19
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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

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Greg Heath
Greg Heath 2018-8-9
编辑：Greg Heath 2018-8-9

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[ 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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Venkat
Venkat 2018-8-9
Hi Greg,
Thanks for your time and input. I understood what you have said. My problem is I am using CNN. My inputs are images of size 16x512 and I have 30,000 image samples per class, totaling 60,000 representing my 2 classes.
In order to decide on the number of layers in CNN along with the number of filters in each convolutional layer, I am doing an iterative process, but that is going to take a long time. So I am trying to see if I can derive some generic numbers I can start with rather than iterating from 1 to N as far as the number of filters is concerned.
Can I apply the same rule? If yes, can you please explain a little bit more
Thanks

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Greg Heath
Greg Heath 2018-8-11

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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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