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I am using the neural network toolbox of Matlab to train a network. Now my code is as follows:

```
x = xdata.';
t = target1';
% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
net.layers{2}.transferFcn = 'softmax';
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 60/100;
net.divideParam.valRatio = 20/100;
net.divideParam.testRatio = 20/100;
net.trainFcn = 'trainscg'; % Scaled conjugate gradient
net.performFcn = 'mse';
net.performParam.regularization = 0.5;
%net.performParam.normalization = 0.01;
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit', 'plotconfusion'};
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
performance = perform(net,t,y)
% Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
```

Now I am supposed to get different accuracies with different run of data since the sampling (division of dataset into train test and validation set) is random. But I am getting a constant accuracy (89.7%). The variable 'xdata' contains only those features selected by a feature selection algorithms. Is there any reason why my accuracy value is constant?

I have trained an SVM too with the same dataset. There too I am getting the a stale accuracy even with multiple run (94%)

The output y contains 2 values. What do those values signify?

Haven't you fixed the random number generator seed somewhere with the 'rng' function? – rcpinto – 2016-08-08T14:35:13.830

@rcpinto But i am getting a stable result? Still do i need ti fix rng? – girl101 – 2016-08-09T03:09:12.693