Weight decay parameter and Jacobian matrix of a neural network

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I want calculate prediction intervals so I have 2 direct questions:
  1. How can I get the weight decay parameter 'alpha' (mse+alpha*msw) used when using 'trainbr' as a training algorithm?
  2. How can I get the neural network jacobian matrix (derivatives following weights) calculated during training?

Accepted Answer

Greg Heath
Greg Heath on 19 Feb 2014
Edited: Greg Heath on 19 Feb 2014
The documentation for trainbr is pretty bad.
help trainbr
doc trainbr
Look at the source code
type trainbr
I am not familiar with it but will take a look when I get time.
Meanwhile, if you make a run, the training record tr, contains 2 parameters
gamk: [1x31 double]
ssX: [1x31 double]
that are involved.
Hope this helps.
Thank you for formally accepting my answer

More Answers (1)

Platon on 21 Feb 2014
Thank you. It helps to determine alpha but not to calculate the neural network jacobian matrix. Hope that future Matlab NN tool Box versions include specific tools to make prediction interval study.
  1 Comment
Greg Heath
Greg Heath on 21 Feb 2014
When using the obsolete msereg and mse with the regularization option, the weight parameters are alpha (specified error weight) and (1-alpha).
However when using trainbr, the weight parameters alpha and beta are calculated each epoch. Haven't decifered the logic yet. Might be faster to search the web.

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