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- W1=net.IW{1,1};
- W2=net.LW{2,1};
- b1=net.b{1,1};
- b2=net.b{2,1};
- % Solve a Pattern Recognition Problem with a Neural Network
- % Script generated by NPRTOOL
- % Created Tue May 22 22:05:57 CEST 2012
- %
- % This script assumes these variables are defined:
- %
- % input - input data.
- % target - target data.
- inputs = input;
- targets = target;
- % Create a Pattern Recognition Network
- hiddenLayerSize = 10;
- net = patternnet(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
- 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, plotconfusion(targets,outputs)
- %figure, ploterrhist(errors)
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