load('trmat.mat'); %60 x 11 load('test.mat'); %1 x 60 x=transpose(trmat); t=test; inputs = x targets = t % Create a Pattern Recognition Network hiddenLayerSize = 10; net = patternnet(hiddenLayerSize); % Set up Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 30/100; net.divideParam.valRatio = 13/100; net.divideParam.testRatio = 57/100;
% Train the Network [net,tr] = train(net,inputs,targets); % Test the Network outputs = net(inputs); errors = gsubtract(targets,outputs) performance = perform(net,targets,outputs) % View the Network view(net) % Plots % Uncomment these lines to enable various plots. figure, plotperform(tr) figure, plottrainstate(tr) figure, plotconfusion(targets,outputs) [c,cm] = confusion(targets,outputs) fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c)); fprintf('Percentage Incorrect Classification : %f%%\n', 100*c); figure, ploterrhist(errors) 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)
