Re: Troubling running TMultiLayerPerceptron with ROOT 5.08/00

Hi Nick, Christophe, Andrea,

TMultiLayerPerceptron was "upgraded" by Christophe and Andrea in version 5.10 in January.
See the comments in the development notes at http://root.cern.ch/root/html/examples/V5.10.txt.html see CVS change log of:

   2006-01-09 16:47 brun
   2006-01-13 10:10 brun

I expect that Christophe or/and Andrea will comment.

Rene Brun

On Wed, 8 Mar
2006, Nick West wrote:

> Dear RootTalk,
>
> We are using TMultiLayerPerceptron but recently its behaviour has
> changed in ways that we don't understand: fits the previously worked
> now give degenerate results.
>
> The network we are using has
>
> o 3 input neurons: trkPHperPlane
> eventPlanes
> shwPHperStrip
>
> o One hidden layer with 5 neurons
>
> o 1 output neuron: type
>
> We have a TTree called inTree containing the branches: trkPHperPlane,
> eventPlanes, shwPHperStrip and type. We take 4 out of every 5 events
> in the TTree for training purposes, so our constructor is:-
>
> TMultiLayerPerceptron("@trkPHperPlane, @eventPlanes,
> @shwPHperStrip:5:type",
> inTree,
> "Entry$%5",
> "!(Entry$%5)");
>
> We have been using the default training method (kBFGS - Broyden,
> Fletcher, Goldfarb, Shanno). We noticed the problem when migrating from
> ROOT 4.04/02 to 5.08/00. Sometimes the resulting net became quasi
> degenerate - giving basically the same output for any input. In
> exploring the problem we discovered that changing the method:-
>
> SetLearningMethod(TMultiLayerPerceptron::kStochastic)
>
> enhanced the effect. With 4.04/02 the fit gives a plausible network
> every time, but with 5.08/00 it is almost always degenerate.
>
> To demonstrate the problem we have a tar file containing:-
>
> exampleTree.root
> testMlp.C
>
> It can be found at:-
>
>
> http://www-pnp.physics.ox.ac.uk/~west/minos/TMultiLayerPerceptron_proble
> m.tar.gz
>
> Unwind the tar file and:-
>
> root -b -q testMlp.C
>
> testMlp.C consists of:-
>
>
> {
>
> gSystem->Load("libMLP");
>
> TFile input("exampleTree.root");
> TTree *inTree = input.Get("smallTree");
>
> TMultiLayerPerceptron *mlp = new
> TMultiLayerPerceptron("@trkPHperPlane, @eventPlanes,
> @shwPHperStrip:5:type", inTree, "Entry$%5","!(Entry$%5)");
> mlp->SetLearningMethod(TMultiLayerPerceptron::kStochastic);
> mlp->Train(50,"text,update=10");
> mlp->DrawResult(0,"test");
> cout << "Test set distribution. Mean : " << MLP_test0.GetMean() << "
> RMS " << MLP_test0.GetRMS() << endl;
>
> }
>
> After defining and training the net, the results of applying to the
> test set is displayed and then, using the wonders of CINT, the mean
> and RMS of that histogram are displayed.
>
> Here are some typical results from 4.04/02
>
> Test set distribution. Mean : 0.73054 RMS 0.212484
> Test set distribution. Mean : 0.93671 RMS 0.241446
> Test set distribution. Mean : 0.590702 RMS 0.231579
> Test set distribution. Mean : 0.727839 RMS 0.167396
>
> Contrast this with the results from 5.08/00
>
> Test set distribution. Mean : 0.49273 RMS 0.0481377
> Test set distribution. Mean : 0.515413 RMS 0.0805224
> Test set distribution. Mean : 0.9872 RMS 0.0317544
> Test set distribution. Mean : 0.891544 RMS 0.0351175
>
> Notice the much smaller RMS showing that the distribution has
> collapsed to a narrow peak. The same behaviour persists to the
> lastest ROOT as of last night.
>
> TMultiLayerPerceptron has changed significantly since 4.04/02, have
> we missed a significant change in usage?
>
> Our platform is Scientific Linux SL Release 3.0.4 (SL)
> with gcc version 3.2.3
>
> Thanks,
>
> Nick West.
>
Received on Wed Mar 08 2006 - 19:20:10 MET