MATLAB: How to read result of sim using Patternnet

Deep Learning Toolboxneural networkpatternnetsim

I created neural network for binary classification , i have 2 classes and 9 features using an input matrix with a size of [9 981] and target matrix [1 981] . This is my code :
 rng(0);  
inputs = patientInputs;
targets = patientTargets;
[x,ps] = mapminmax(inputs);
t=targets; 
trainFcn = 'trainbr';  
% Create a Pattern Recognition Network
hiddenLayerSize =8;
net = patternnet(hiddenLayerSize,trainFcn);
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';  
net.trainParam.max_fail=6;
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotconfusion', 'plotroc'};
 % Train the Network
net= configure(net,x,t);
[net,tr] = train(net,x,t);
 y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
 % Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)
 % View the Network
view(net)
and when i tried to test the neural network with new data using
    ptst2 = mapminmax('apply',tst2,ps);
    bnewn = sim(net,ptst2);
I don't get the same values like the target i mean 0 or 1 however if i put test data with target 0 i have as a result of bnewn= 0.1835 and with data test having target 1 i got cnewn= 0.816. How can i read this results ? as i understand if it is >0.5 so target=1 else target=0

Best Answer

For two class classifiers with scalar 0/1 targets
     1. Use LOGSIG as the output function
     2. The output class index is given by
     outputclassindex = round(output)
     or
     outputclassindex = round(net(input)) 
     3. You can use PURELIN as the output function.
        However, that allows output to escape 
        the [ 0,1] constraint
Hope this helps.
Thank you for formally accepting this answer
Greg
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