# 2-Class Problem with patternnet

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afef on 14 Jun 2017
Edited: afef on 14 Jun 2017
Hi, I have a problem using the NN toolbox a neural network shall be trained to recognize a two class problem. I used the default settings ( dividerand , 10 hidden neurons, divide radio 0.7, 0.15, 0.15) and my input is a 9xn matrix and my target is a 2xn matrix ([1; 0]for class one and [0; 1] for class two for each sample), where n=1012. the ratio of the classes are about 50:50 .this is the confusion matrix
This is the code that i used :
rng('default');
x = patientInputs;
t = patientTargets ;
inputs=mapminmax(x);
targets=t;
size(inputs);
trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.
% Create a Pattern Recognition Network
hiddenLayerSize =10;
net = patternnet(hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.performFcn = 'mse'; % Cross-Entropy
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotconfusion', 'plotroc'};
net.trainParam.max_fail =55;
net.trainParam.show=10;
net.trainParam.lr=0.01;
net.trainParam.epochs=90;
net.trainParam.goal=0.001;
% Train the Network
[net,tr] = train(net,inputs,targets);
y = net(inputs);
e = gsubtract(targets,y);
performance = perform(net,targets,y)
tind = vec2ind(targets);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
% Recalculate Training, Validation and Test Performance
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)
% View the Network
view(net)
Can anyone tell me how to solve this problem and please go easy on me because newbie in matla and neural network .
Thanks

Greg Heath on 14 Jun 2017
In order to insure stability with respect to changes in operating conditions I recommend
MINIMIZING THE NUMBER OF HIDDEN NODES
subject to the normalized mean-square-error constraint
NMSE = mse(error)/mean(var(target',1) < 0.01 % i.e., Rsquare >= 0.99
Although I derived this for regression, it works extremely well for classificaton.
I have zillions of posted examples in both the NEWSGROUP and ANSWERS. Try searching using
greg patternnet
Hope this helps.
Thank you for formally accepting my answer
Greg
afef on 14 Jun 2017
Edited: afef on 14 Jun 2017
when i tried to minimize the number of hiddrn nodes less than 10 i got bad performance. Conerning the use of NMSE sorry i don't understand why should i use it ? when i was looking for your examples in NEWSGROUP i tried to modify my coe to be like this
x = patientInputs;
t = patientTargets;
N=1012
I=9
O=2
[ I N ] = size(x)
[ O N ] = size(t)
Ntrn = N-2*round(0.15*N) % Ntrn=708
Ntrneq = Ntrn*O % Ntrneq=1416
%For a robust design desire Ntrneq >> Nw or
H=10
Hub = -1+ceil( (Ntrneq-O) / (I+O+1)) % Hub =117
Nw = (I+1)*H+(H+1)*O % Number of unknown weights =122
%H << Hub = -1+ceil( (Ntrneq-O) / (I+O+1))
Ntrials = 10
rng(0)
j=0
for h =round([Hub/10, Hub/2, Hub])
j = j+1
h = h %12
Nw = (I+1)*h+(h+1)*O % Nw=146
Ndof = Ntrneq-Nw % Ndof=1270
net = patternnet(h);
net.divideFcn = 'dividerand'; % 'dividetrain'
for i = 1:Ntrials
net = configure(net,x,t);
[ net tr outputs regerrors ] = train(net,x,t);
assignedclasses = vec2ind(outputs);
classerr = assignedclasses~=trueclasses;
Nerr(i,j) = sum(classerr);
% FrErr = Fraction of Errors (Nerr/N)
[FrErr(i,j),CM,IND,ROC] = confusion(t,outputs);
FN(i,j) = mean(ROC(:,1)); % Fraction of False Negatives
TN(i,j) = mean(ROC(:,2)) ; % Fraction of True Negatives
TP(i,j) = mean(ROC(:,3)); % Fraction of True Positives
end
end
PctErr=100*Nerr/N
But i got this error message Undefined function or variable 'trueclasses'. and the confusion matrix show a bad accuracy so please tell me what should i do?

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