neural net performance function 'msereg' disabled in 2013B

2013 12 2
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更新时间：2023 2 13
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I notice that 'msereg' neural net performance function disabled in 2013B. Any attempt to set net.performFcn='msereg' reverts back to 'mse'. For where in the code this happens, see ...\toolbox\nnet\nnet\@network\subsagn.m line 1901. Temporarily commenting out lines 1901-1903 allows to create a NN with net.performFcn='msereg' but training such a net results in an error.
Why was this functionality removed? Why does the documentation still mention is? Can this functionality be restored in any way?
Richard Hodges

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Greg Heath
Greg Heath 2013-12-3

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It was probably discontinued because mse now has a regularization option.
help mse
HTH
Thank you for formally accepting my answer
Greg

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Richard Hodges
Richard Hodges 2013-12-3
Thank you for the information. But there is very little documentation on the actual usage or tradeoffs.
Running some experiments, using trainbr on a multi-input multi-output time series net, I notice that when I set net.performParam.normalization to 'none', then TRAINBR gives a warning that net.performParam.regularization has been set to 0 (even though I set to .5)--does regularization REQUIRE a non-default normalization?
And when I set regularization to .5 and normalization to 'standard' the net training terminates with "maximum Mu reached" (Mu is 5e10), with a result in which the Error Histogram is several times broader (i.e. worse) than before, while the reported performance value is orders of magnitude smaller. The latter is expected, because my targets were in a range much larger than +-1. But what is the formula for what is minimized in the the regularized case? Is it such that the Sum Squared Param value is added to the performance value, or is it something else. Is Mu a factor in this term? In my test case, the SSP value is orders of magnitude larger than the final performance value.
Can you cite literature which this regularization is based on?
Thank you,
Richard
Greg Heath
Greg Heath 2013-12-4
It would help immensely if you ran your code on one of MATLAB's NN data sets so that we could compare details.
help nndatasets
Please post code, comments and error messages.
Greg Heath
Greg Heath 2014-2-20
For some reason
net.trainFn = 'trainbr';
net.performFcn = 'mse'
does result in regularization where the weights alpha and beta are AUTOMATICALLY determined.
To try to understand how the regularization is done,
type trainbr
find gamk
find ssX
I haven't figured the logic out yet.
Bottom line: Accept the default 'mse' and it will work fine. The time history of gamk and ssK can be obtained from the training history
gamk = tr.gamk
ssX = tr.ssX
Hope this helps.
Greg
P.S. Report this to MATLAB
Mohd Khan
Mohd Khan 2023-2-13
I would really like to know that is this tr.gamk. It is reported as 29.1771 in my case. I understant that 29 is the number of parameters used by the model in current stage but I have not idea what is 1771. Kindly, help me out.

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