MATLAB: Same neural network training result each time at “dividerand” option

divideblockdividerandmachine learningneural network

I wonder why my NN results shows same performance(error) every run even though I separated data set training, validation and test sets randomly by using 'deviderand'.
I understand the weight and bias are always same because I use rng('default') code, however, I think the results must be different every time due to its randomness of data selection.
Is it because I misunderstand the concept of NN?
The code is the following:
% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% Created 28-Dec-2016 14:15:01
%

% This script assumes these variables are defined:
%
%   InputT12 - input data.
%   TargetT12 - target data.
rng('default')
x = InputT12;
t = TargetT12;
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideFcn = 'dividerand';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotfit(net,x,t)

Best Answer

When you reboot, the RNG goes into the default state.
Therefore begin with states different from default and 0.
Hope this helps
Thank you for formally accepting my answer
Greg
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