97 lines
3.1 KiB
Matlab
97 lines
3.1 KiB
Matlab
% Solve a Pattern Recognition Problem with a Neural Network
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% Script generated by Neural Pattern Recognition app
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% Created 02-Mar-2021 18:31:05
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%
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% This script assumes these variables are defined:
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%
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% cancerInputs - input data.
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% cancerTargets - target data.
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x = cancerInputs;
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t = cancerTargets;
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% Choose a Training Function
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% For a list of all training functions type: help nntrain
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% 'trainlm' is usually fastest.
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% 'trainbr' takes longer but may be better for challenging problems.
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% 'trainscg' uses less memory. Suitable in low memory situations.
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trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation.
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% Create a Pattern Recognition Network
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hiddenLayerSize = 10;
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net = patternnet(hiddenLayerSize, trainFcn);
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% Choose Input and Output Pre/Post-Processing Functions
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% For a list of all processing functions type: help nnprocess
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net.input.processFcns = {'removeconstantrows','mapminmax'};
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% Setup Division of Data for Training, Validation, Testing
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% For a list of all data division functions type: help nndivision
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net.divideFcn = 'dividerand'; % Divide data randomly
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net.divideMode = 'sample'; % Divide up every sample
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net.divideParam.trainRatio = 50/100;
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net.divideParam.valRatio = 0/100;
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net.divideParam.testRatio = 50/100;
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% Choose a Performance Function
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% For a list of all performance functions type: help nnperformance
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net.performFcn = 'crossentropy'; % Cross-Entropy
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% Choose Plot Functions
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% For a list of all plot functions type: help nnplot
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net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
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'plotconfusion', 'plotroc'};
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% Train the Network
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[net,tr] = train(net,x,t);
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% Test the Network
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y = net(x);
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e = gsubtract(t,y);
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performance = perform(net,t,y)
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tind = vec2ind(t);
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yind = vec2ind(y);
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percentErrors = sum(tind ~= yind)/numel(tind);
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% Recalculate Training, Validation and Test Performance
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trainTargets = t .* tr.trainMask{1};
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valTargets = t .* tr.valMask{1};
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testTargets = t .* tr.testMask{1};
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trainPerformance = perform(net,trainTargets,y)
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valPerformance = perform(net,valTargets,y)
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testPerformance = perform(net,testTargets,y)
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% View the Network
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view(net)
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% Plots
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% Uncomment these lines to enable various plots.
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figure, plotperform(tr)
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%figure, plottrainstate(tr)
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%figure, ploterrhist(e)
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figure, plotconfusion(t,y)
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%figure, plotroc(t,y)
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% Deployment
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% Change the (false) values to (true) to enable the following code blocks.
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% See the help for each generation function for more information.
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if (false)
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% Generate MATLAB function for neural network for application
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% deployment in MATLAB scripts or with MATLAB Compiler and Builder
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% tools, or simply to examine the calculations your trained neural
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% network performs.
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genFunction(net,'myNeuralNetworkFunction');
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y = myNeuralNetworkFunction(x);
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end
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if (false)
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% Generate a matrix-only MATLAB function for neural network code
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% generation with MATLAB Coder tools.
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genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');
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y = myNeuralNetworkFunction(x);
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end
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if (false)
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% Generate a Simulink diagram for simulation or deployment with.
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% Simulink Coder tools.
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gensim(net);
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end
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