MATLAB: Improve the performance of nprtool

Deep Learning Toolboxnprtoolpattern recognition

I used the neural network toolbox ( nprtool ) for classifying my objects. i used 75% of data for training and 15% for both validation and testing.also i considered 50 neurons for hidden layers. the progress stops because of validation checks (at 6). how can i improve the performance of this network? i couldn't find out how to change validation check or gradient ,….if you have any suggestion i will be very appreciate to hear that.

Best Answer

Insufficient information
 Which of the MATLAB classification example datasets are you using?
 help nndatasets
 doc nndatasets
 Number of classes  c =?
 Input vector dimensionality I = 1
 Number of examples  N = ?
 [ I N ] = size(input)
 [ O N ] = size(target)% O = c
 Default 70/15/15 data division? (75/15/15 doesn't add to 100)
Some problems require multiple(e.g., 10) designs for every value of hidden nodes that are tried.
For example, search the NEWSGROUP and ANSWERS using
 greg patternnet Ntrials
 Sorry I can't give you much advice on how to optimize the use of nprtool. However, consulting my command line code should be more than worthwhile.
Hope this helps.
Thank you for formally accepting my answer
Greg.
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