neural network performance analysis
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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
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Greg Heath
le 11 Nov 2013
Modifié(e) : Greg Heath
le 11 Nov 2013
As you can see from my second answer, you didn't use (:,tInd) for the input indices.
Please accept the 2nd answer.
Greg
Réponse acceptée
Greg Heath
le 11 Nov 2013
Modifié(e) : Greg Heath
le 11 Nov 2013
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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Plus de réponses (2)
Greg Heath
le 11 Nov 2013
Initialize the random number generator at the beginning. So we can compare, use
rng(0).
Also make 10 designs in a loop over random weight initializations obtained by using
net = configure(net,x,t);
[net tr ] = ...
Hope this helps,
Thank you for formally accepting my answer
Greg
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