MAPE for out-of-sample in neural network

How to compute MAPE for out-of-sample in neural network?

Input value is 12x1505 double. Target value is 1x1505 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

Answer 1

[Greg Heath]

Not sure why you would use a measure that has the possibility of being infinite. However

mape = mean(abs(e./t))

Hope this helps

Thank you for formally accepting my answer

Greg

[Andres Romero]

There is a way to separete Inf and -Inf values? from a vector A = [1;Inf;-Inf;2;6;3]

[Angelina Tan]

what does t stands for