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Oefeningen: Neurale Netwerken

en Datamining

Bart.Hamers@esat.kuleuven.ac.be

Patrick.Glenisson@esat.kuleuven.ac.be Office: 02.54 ESAT

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Function Approximation

W1,1 W1,2 W1,3 W2,1 W2,2 W2,3 Sum

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Function Approximation

• net=newff([-1,1],[5,1],{'tansig','purelin'},'trainscg');

1. Bereik invoerneuron

2. Aantal neurone in de ‘hidden’ layers 3. Transfert-functies van ‘hidden’ layers 4. Trainingsalgoritme

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Optimizatie Algoritmen:

• Back Propagation: ‘traingd’

– Leer parameter: ‘net.trainParam.lr=0.1’

– + momentum term: ‘traingdm’

• Momentum parameter: ‘net.trainParam.mc=0.1’

– Adaptive leerparameter:‘traingdx’

• Conjugate gradient: ‘trainscg’

• Quasi Newton: ‘trainbfg’

• Levenberg-Marquardt: ‘trainlm’

– Geheugen beperking van Hessiaanse matrix: ‘net.trainParam. Mem_reduc=2’

Vergelijk: 1. Snelheid

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Generalisatie

1. Early Stopping: gebruik validatie set bij trainen .

• ‘net.train(net,p,t,[],[],v)’

2. Regularisatie term in kost-functie:

• ‘net.performFcn=‘msereg’’

• ‘net.performParam.ratio=0.999’

3. Bayesiaanse regularisatie: ‘trainbr’

( )

    − + =

= n j j n w MSE MSEreg 1 2 1 1 γ γ

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