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The following script is from Trappenberg's Fundamentals of Computational Neuroscience and is used to test a perceptron's robustness against noise.

However, how would one alter it to test the output of a multilayer perceptron?

In particular, wOut and rIn are non-comformable because the wOut of a multilayer perceptron with 2 hidden layers is 26*2.

So how could I alter this to test the trained multilayer perceptron?

%% Testing generalization perfroamnce of trained perceptron
 perceptron_network_sim; 
 letterMatrix=rIn; 
 for nflip = 1:80;    
    dist1=[]; dist2=[]; 
    for trial=1:10; 
       rIn=abs(letterMatrix-randomFlipMatrix(nflip)); 
       % Threshold output function
       rOut1=(wOut*rIn)>0;
       nerror=0; 
       for j=1:26; nerror=nerror+(sum(rDes(:,j)~=rOut1(:,j))>0); end 
       dist1=[dist1,nerror];
       % Max output function
       [v,i]=max(wOut*rIn);
       rOut2=zeros(26); for j=1:26; rOut2(i(j),j)=1; end
       dist2=[dist2,0.5*sum(sum(rDes~=rOut2))];
    end
    meanDist1(nflip)=mean(dist1); stdDist1(nflip)=std(dist1);
    meanDist2(nflip)=mean(dist2); stdDist2(nflip)=std(dist2);
 end
 figure; hold on;
 errorbar((1:80)/156,meanDist1,stdDist1,':')
 errorbar((1:80)/156,meanDist2,stdDist2,'r')
 xlabel('Fraction of flipped bits')
 ylabel('Average number of wrong letters')
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  • $\begingroup$ Can you explain to us what multilayer perception is to place this question into perspective? Can you link your sources? $\endgroup$ – AliceD Apr 8 at 12:42
  • $\begingroup$ This question belongs on the SE Cross Validated. Multilayer perceptron is a standard term within statistical machine learning which is a deep artificial neural network; a statistical model. $\endgroup$ – Galen Apr 13 at 15:34
  • $\begingroup$ Multilayer perceptron's in general don't have to have input, hidden, or output widths of 26. $\endgroup$ – Galen Apr 13 at 15:35
  • $\begingroup$ On second thought, since you're looking for how to change your code to match array dimensions... StackOverflow might be appropriate. $\endgroup$ – Galen Apr 13 at 15:36