How to compute MAPE for out-of-sample in neural network?
Input value is 12×1505 double. Target value is 1×1505 double.
Here is my code:
x = Input';t = Target';trainFcn = 'trainlm'; hiddenLayerSize = 3;net = feedforwardnet(hiddenLayerSize,trainFcn);net.input.processFcns = {'removeconstantrows','mapminmax'};net.output.processFcns = {'removeconstantrows','mapminmax'}; net.divideFcn = 'divideind'; net.divideParam.trainInd = 1:903; net.divideParam.valInd = 904:1204; net.divideParam.testInd = 1205:1505.net.performFcn = 'mse'; [net,tr] = train(net,x,t);y = net(x);e = gsubtract(t,y);performance = mse(net,t,y)trainTargets = t .* tr.trainMask{1};valTargets = t .* tr.valMask{1};testTargets = t .* tr.testMask{1};trainPerformance = perform(net,trainTargets,y)valPerformance = perform(net,valTargets,y)testPerformance = perform(net,testTargets,y)view(net)
Thank you very much
Janthorn
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