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While training a simple network using Matlab trainbr (maximum parameters 22, effective parameters 6), I noticed that the weights and biases, 22 in all, have finite values after initialization AND after convergence. I'd have expected only "effective" 6 converged weights (and biases), with the rest being zero or NaN.
The trainbr source code shows how the effective number of parameters (gamk) is calculated, but offers no clues as to why the full suite of parameters is still populated (22 in my case) even after the code declares convergence. If only some of the parameters are ultimately effective (6 in my case), why aren't the rest of the parameters zero or undefined?
Thanks in advance for any insights
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If you use 22 parameters and trainbr says the number of effective parameters is six
1. I don't think that means you can discard 16 of them without more training.
2. It may not even mean that if you started with six, you could obtain an acceptable design.
This is just conjuncture. However, if oucan find an example, please post.
Hope this helps.
Thank you for formally accepting my answer