Binary Classification _ problem with data structure

Hi, i want to create neural network for binary classification so when i read in matlab doc for patternet that Classification problems involving only two classes can be represented using either format. The targets can consist of either scalar 1/0 elements or two-element vectors, with one element being 1 and the other element being 0.(link= https://www.mathworks.com/help/nnet/gs/classify-patterns-with-a-neural-network.html) so i tried to set each scalar target value to either 0 or 1 but in the confusion matrix i got NAN values for the second class

 [ I N ] = [ 9 981 ] 

 [ O N ] = [ 1 981 ]

And this is the code

 rng('default');
x = patientInputs;
t = patientTargets ;
 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'};

 % 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)

i don't know why? can anyone tell me please?

Answer 1

[Greg Heath]

There might be a caveat in the confusion matrix code that only accepts {0,1} 1-d outputs.

check it out.

Greg

[afef]

I thought that the problem is wwith this part in the code

net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

Because when i removed

 net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
 net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

and i change it with this

 inputs = patientInputs;
targets = patientTargets;
x = mapminmax(inputs); 
t=targets;

it works .So i want to know am i doing right ?

[afef]

And i want to know when you said 1-d outputs. 'd' referes to what ??