MATLAB: Do i have NAN values in the confusion matrix only in the validation test

Deep Learning Toolboxneural network

I wanted to create neural network for binary classification for dataset with input matrix with size [9 981] and output matrix [1 981]and this is the code that i used
   rng('default');
inputs = patientInputs;
targets = patientTargets;
x = mapminmax(inputs); 
t=targets; 
 trainFcn = 'trainbr'; 
% Create a Pattern Recognition Network
hiddenLayerSize =10;
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';  
 % 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)
At first i used the default trainFcn 'trainscg' then i tried to use 'trainbr' the accuracy improved but i got NAN values in the confusion matrix only in the validation test as you can see it here
Can anyone help me please?

Best Answer

MATLAB doesn't allow a validation set for trainbr because they think it isn't necessary for generalization.
Although they are correct, I have found in the literature that using BOTH trainbr and a validation set is better in most cases.
Although you cannot force trainbr to have validation set, I think you can work around it using trainlm and/or trainscg with regularization.
Hope this helps
Thank you for formally accepting my answer
Greg
PS: I don't remember details.
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