# Testing Multilayer Perceptron [closed]

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')

• Can you explain to us what multilayer perception is to place this question into perspective? Can you link your sources? – AliceD Apr 8 '20 at 12:42
• 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. – Galen Apr 13 '20 at 15:34
• Multilayer perceptron's in general don't have to have input, hidden, or output widths of 26. – Galen Apr 13 '20 at 15:35
• On second thought, since you're looking for how to change your code to match array dimensions... StackOverflow might be appropriate. – Galen Apr 13 '20 at 15:36