how use all test indices for performance evaluation in matlab neural network?

I want to do classification using Neural Network toolbox in matlab. I'm using commands (instead of NNtool GUI) I split my data set into 3 subsets via my own function(getIndices) : training, validation and test as follows:

    trainRatio = 70/100;
    valRatio = 15/100;
    testRatio = 15/100;

    [TrueClass,TrainIndices,ValIndices,TestIndices] = getIndices(someParams);

    net = patternnet(hiddenLayerSize);

    [trainInd,valInd,testInd] = divideind(Total_size,TrainIndices,ValIndices,TestIndices);

    [net,tr] = train(net,inputs,targets);

    tInd = tr.testInd;
    tstOutputs = net(inputs(:, tInd));
    tstPerform = perform(net, targets(:,tInd), tstOutputs)

after training network, I want to test it with test dataset. So i get the test indices like the code above and do use usual commands. everything goes well when I use the specified ratios for splitting dataset. for example if i have 6500 samples, i will have 4550 training samples,975 validation samples and 975 test samples. so tInd would be a 1X975 array. but when i change ratios, for example,

    trainRatio = 50/100;
    valRatio = 0/100;
    testRatio = 50/100;

or other ratios, tInd does not change and it always remain the same size as before (1X975).

why this happens? is it any limitations or unwritten rule for data ratio?

I should write my own function for computing accuracy, so it's important for me to get all of the test indices specified,not a part of them.

(I know it seems a silly question but i'm really stuck and i need help :( )