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
1 个评论
Greg Heath
2013-11-11
编辑：Greg Heath
2013-11-11
As you can see from my second answer, you didn't use (:,tInd) for the input indices.
Please accept the 2nd answer.
Greg
采纳的回答
Greg Heath
2013-11-11
编辑：Greg Heath
2013-11-11
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?
更多回答（2 个）
Greg Heath
2013-11-11
0 个投票
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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