MATLAB: Newff vs patternet in the program

Deep Learning Toolboxemotion recognitionnewffoverfittingpatternnet

I have written a code , the purpose of the code is classification of EEG datasets related to emotion, I have used both newff and patternet , I have always thought there is no difference between them except for the training function they use , but the thing is there are tremendous changes between the results; for example some times when I use same number of neurons for my network hidden layer, using newff leads to some results while using patternet may lead to no result , it seems net is not working or the result will lead to a very big error. I wonder why is that for. more over I assume in new version of net codes I mean patternet for example there are some ways that prevent overfitting happens even we set net.divideParam.valRatio=0 , it seems there are some hidden strategies that keep net from overfitting. I would really appreciate it if some one helps me to figure these differences out.

Best Answer

You are very confused. The current MLP functions are
 -FITNET for regression and curve-fitting
 -PATTERNNET for classification and pattern-recognition
 -FEEDFORWARDNET a generic net called by FITNET and  
  PATTERNNET
They replace the obsolete but still available functions
 -NEWFIT for regression and curve-fitting
 -NEWPR  for classification and pattern-recognition
 -NEWFF a generic net called by NEWFIT and NEWPR
PATTERNNET and NEWFF are not even remotely similar.
To obtain the documentation for PATTERNNET try
 help patternnet
 doc  patternnet
 type patternnet
I have posts which explain the differences between the three current functions as well as for the three obsolete functions.
The coding for current and obsolete functions is not similar. Don't waste your time trying to compare them.
Hope this helps.
Thank you for formally accepting my answer
Greg
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