MATLAB: Neural network performance analysis

Deep Learning Toolboxneural network

Hello I have trained a neural network using house_dataset in matlab. When I trained the network through the following code the tr.best_tperf was reasonable: [inputs,targets] = house_dataset; hiddenLayerSize = 10; net = fitnet(hiddenLayerSize); net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; [net,tr] = train(net,inputs,targets); outputs = net(inputs); performance = perform(net,targets,outputs)
however, when we extract the test dataset and consider the performance using these dataset the performance was unacceptable: tInd = tr.testInd; tstOutputs = net(inputs(tInd)); tstPerform = perform(net,targets(tInd),tstOutputs)
Could you please kindly explain the reason for this. Should we use another data division algorithm? best

Best Answer

result =
    ntrial    R2trn     R2val     R2tst
    1.0000    0.8610    0.8746    0.8218
    2.0000    0.8532    0.8778    0.8648
    3.0000    0.8607    0.8503    0.8547
    4.0000    0.8464    0.8942    0.8585
    5.0000    0.8944    0.8883    0.8843
    6.0000    0.8958    0.8423    0.9184
    7.0000    0.9085    0.7988    0.9078
    8.0000    0.8906    0.8690    0.9186
    9.0000    0.8978    0.8637    0.9001
   10.0000    0.8810    0.9125    0.9267
 >> ytst = net(inputs(:,tr.testInd));
 R2tstx = 1 - mse(ttst-ytst)/MSEtst00
 R2tstx = 
           0.9267
 Rtstx = sqrt(R2tstx) 
 Rtstx =
           0.9627
How did you get 0.999?
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