training_script.m

inputs = x2;
targets = y2;

% Create a Fitting Network
hiddenLayerSize = 10;
net             = fitnet(hiddenLayerSize);

% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns  = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};


% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
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;

% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
% trainbfg  - BFGS quasi-Newton backpropagation.
% trainbr   - Bayesian Regulation backpropagation.
% traincgb  - Conjugate gradient backpropagation with Powell-Beale restarts.
% traincgf  - Conjugate gradient backpropagation with Fletcher-Reeves updates.
% traincgp  - Conjugate gradient backpropagation with Polak-Ribiere updates.
% traingd   - Gradient descent backpropagation.
% traingda  - Gradient descent with adaptive lr backpropagation.
% traingdm  - Gradient descent with momentum.
% traingdx  - Gradient descent w/momentum & adaptive lr backpropagation.
% trainlm   - Levenberg-Marquardt backpropagation.
% trainoss  - One step secant backpropagation.
% trainrp   - RPROP backpropagation.
% trainscg  - Scaled conjugate gradient backpropagation.
 
net.trainFcn = 'trainlm';  % Levenberg-Marquardt

% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse';  % Mean squared error

% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotregression', 'plotfit'};


% Train the Network
[net,tr] = train(net,inputs,targets);

% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)

% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
valTargets = targets  .* tr.valMask{1};
testTargets = targets  .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotfit(net,inputs,targets)
%figure, plotregression(targets,outputs)
%figure, ploterrhist(errors)