Rsq from NMSE in NN

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vanrapa on 31 Mar 2019
Edited: Greg Heath on 1 Apr 2019
I am trying to find out the best number of hidden neurons for a network. I am training the network in a loop with different number of hidden neurons and storing the value of NMSE and Rsq for each iteration.
My ip database size is 20 x 714 and op database is 3 x 714. I am dividing the dataset as 70% training and 15% for validation and testing each.
I have computed NMSE and Rsq in the usual manner,
MSE00 = mean(var(trnopdb',1))
NMSE = mse(trnopdb'-net(trnipdb'))/MSE00
Rsq = 1 - NMSE
Now I have the following queries,
  1. The value of Rsq is negative. It does not lie between 0 and 1. What am I doing wrong?
  2. Also, matlab nnfit displays Rtraining, Rvalidation, Rtesting and Rall as positive values. So what is the relation between Rsq and the R values?
  3. Is it possible to extract the R values from the network info?
  4. Should I consider Rtesting as the network performance criteria or the other R values?
I am sure there might be lots of info about these trivial questions. Nonetheless I seem to have these doubts. So any help would be great. Thanks in advance.

Accepted Answer

Greg Heath
Greg Heath on 1 Apr 2019
Edited: Greg Heath on 1 Apr 2019
  1. NMSE = mse(trnopdb-net(trnipdb))/MSE00
2. Rsq = R^2
3. Yes. Use separate calculations for the training, validation and training subsets.
4. I typically make 10 or more designs differing by random number initializations. Then I use the summary stats of NMSE.
Hope this helps
Thank you for formally accepting my answer

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