Hi, i am currently using the neural network with Matlab and i want to plot a confusion matrix for the training, validation and testing stage. When i implement the confusion matrix with the code figure, plotconfusion(targets,outputs) – i only get the overall confusion matrix please can any one help me with this. Here is my full code.
inputs = totalnew;
targets = targetnew;
% Create a Pattern Recognition Network
hiddenLayerSize = 20;
net = patternnet(hiddenLayerSize);
% Choose Input and Output Pre/Post-Processing Functions
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing
net.divideFcn = 'divideind';
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainInd = 1:700;
net.divideParam.valInd = 701:850;
net.divideParam.testInd = 851:1000;
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
net.performFcn = 'mse'; % Mean squared error
% Choose Plot Functions
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
figure, plotconfusion(targets,outputs)
Please Help If You Can
Best Answer