UGUR HALICI Dept. EEE, NSNT, Middle East Technical ...UGUR HALICI Dept. EEE, NSNT, Middle East...

UGURHALICI

Dept.EEE,NSNT,MiddleEastTechnicalUniversity

11.03.2015

Outline

•  Biological‐arKficialneuron•  Networkstructures

•  MulKlayerPerceptron–  BackpropagaKonalgorithm

•  DeepNetworks–  ConvoluKonalNeuralNetworks(CNN)–  StackedAutoencoders–  DeepBoltzmannMachine

1

BiologicalNeuron

•  It is estaimated that the human central nervous system iscomprised of about 1,3x1010 neurons and that about 1x1010 ofthemtakesplaceinthebrain

•  Aneuronhasaroughlysphericalcellbodycalledsoma

•  The extensions around the cell body like bushy tree are calleddendrites, which are responsible from receiving the incomingsignalsgeneratedbyotherneurons.

2

BiologicalNeuron

•  The signals generated in soma are transmiWed to other neuronsthroughanextensiononthecellbodycalledaxon.

•  Anaxon,havingalengthvaryingfromafracKonofamillimetertoameter in human body, prolongs from the cell body at the pointcalledaxonhillock.

3

BiologicalNeuron

•  Attheotherend,theaxonisseparatedintoseveralbranches,attheveryendofwhichtheaxonenlargesandformsterminalbuWons.

•  TerminalbuWonsareplacedinspecialstructurescalledthsynapseswhicharethejuncKonstransmiYngsignalsfromoneneurontoanother.

•  Aneurontypicallydrive103to104synapKcjuncKons

4

BiologicalNeuron

•  The transmissionof a signal fromoneneuron to another throughsynapses is a complex chemical process in which specifictransmiWersubstancesare releasedfromthesendingsideofthejuncKon.

•  The effect is to raise or lower the electrical potenKal inside thebodyof thereceivingcell,dependingonthetypeandstrengthofthesynapses.

•  If this potenKal reaches a threshold, the neuron fires and sendpulsesthroughtheaxon.

•  ItisthischaracterisKcthatthearKficialneuronmodelproposedbyMcCullochandPiWs(1943)aWemptreproduce.

5

ArKficialNeuronModel:Perceptron

•  TheneuronmodelshowninFigureiscalledperceptronanditiswidelyusedinarKficialneuralnetworkswithsomeminormodificaKonsonit.

•  IthasNinput,denotedasu1,u2,...uN.

6

ArKficialNeuronModel:Perceptron

•  EachlineconnecKngtheseinputstotheneuronisassignedaweight,whicharedenotedasw1,w2,..,wNrespecKvely.

•  WeightsinthearKficialmodelcorrespondtothesynapKcconnecKonsinbiologicalneurons.

•  θrepresentthresholdinarKficialneuron.

•  Theinputsandtheweightsarerealvalues.

7

ArKficialNeuronModel:NeuronAcKvaKon

TheacKvaKonisgivenbytheformula:

•  AnegaKvevalueforaweightindicatesaninhibitoryconnecKonwhilea posiKvevalueindicatesanexcitatoryone.

•  Althoughinbiologicalneurons,θhasanegaKvevalue,itmaybeassignedaposiKvevalueinarKficialneuronmodels.IfθisposiKve,itisusuallyreferredasbias.ForitsmathemaKcalconvenience,(+)signisusedintheacKvaKonformula.

8

ArKficialNeuronModel:NeuronOutput

•  TheoutputvalueoftheneuronisafuncKonofitsacKvaKoninananalogytothefiringfrequencyofthebiologicalneurons:

•  OriginallytheneuronoutputfuncKonf(a)inMcCullochPiWsmodelproposedasthresholdfuncKon,howeverlinear,rampandsigmoidfuncKonsarealsowidelyusedoutputfuncKons.

9

a)thresholdfunc0onb)rampfunc0on,c)sigmoidfunc0on,d)Gaussianfunc0on

ArKficialNeuronModel:NeuronOutput•  Linear:

•  Threshold:

•  Ramp:

•  Sigmoid:

10

Note:tanh(a)=2*sigmoid(a)-1

NetworkArchitectures

•  NeuralcompuKngisanalternaKvetoprogrammedcompuKng,whichisamathemaKcalmodelinspiredbybiologicalmodels.

•  ThiscompuKngsystemismadeupofanumberofarKficialneuronsandahugenumberofinterconnecKonsbetweenthem.

•  AccordingtothestructureoftheconnecKons,weidenKfydifferentclassesofnetworkarchitectures.

11

a)layeredfeedforwardneuralnetworkb)nonlayeredrecurrentneuralnetwork(therearaealsobackwardconnecKons)

FeedforwardNeuralNetworks

•  Infeedforwardneuralnetworks,theneuronsareorganizedintheformoflayers.

•  Theneuronsinalayergetinputfromthepreviouslayerandfeedtheiroutputtothenextlayer.

•  InthiskindofnetworksconnecKonstotheneuronsinthesameorpreviouslayersarenotpermiWed.

12

inputhiddenoutputlayerlayerlayer


•  Thelastlayerofneuronsiscalledtheoutputlayerandthelayersbetweentheinputandoutputlayersarecalledthehiddenlayers.

•  Theinputlayerismadeupofspecialinputneurons,transmiYngonlytheappliedexternalinputtotheiroutputs.

13

inputhiddenoutputlayerlayerlayer


•  Inanetwork,ifthereisonlythelayerofinputnodesandasinglelayerofneuronsconsKtuKngtheoutputlayerthentheyare calledsinglelayernetwork.

•  Ifthereareoneormorehiddenlayers,suchnetworksarecalledmul3layernetworks(ormul3layerperceptron)

14

BackpropagaKonAlgorithm

•  ThebackpropagaKonalgorithmlooksfortheminimumoftheerrorfunc3oninweightspaceusingthemethodofgradientdescent.

•  SincethismethodrequirescomputaKonofthegradientoftheerrorfuncKonateachiteraKonstep,theerrorfuncKonshouldbedifferenKable.

•  ErrorfuncKonmaybechosenasmeansquareerrorbetweentheactualanddesiredoutputsforasetofsamples.(othererrorfuncKonsareavailableintheliteraturedependingonthestructureofthenetwork)

15

BackpropagaKonAlgorithm

•  ThecombinaKonofweightswhichminimizestheerrorfuncKonisconsideredtobeasoluKonofthelearningproblem.

•  Thenetworkistrainedbyusingasetofsamplestogetherwiththeirdesiredoutput,calledtrainingset.

•  Usuallythereisanothersetcalledtestset,againconsistsofsamplestogetherwithdesiredoutputs,formeasuringperformance

16

TheBackpropagaKonAlgorithm

17

Step 0. Initialize weights: to small random values;

Step 1. Apply a sample: apply to the input a sample vector uk having desired output vector yk;

Step 2. Forward Phase: Starting from the first hidden layer and propagating towards the output layer: 2.1. Calculate the activation values for the units at layer L as: 2.1.1. If L-1 is the input layer 2.1.2. If L-1 is a hidden layer

2.2. Calculate the output values for the units at layer L as:

in which use index io instead of hL if it is an output layer


18

Step 4. Output errors: Calculate the error terms at the output layer as:

Step 5. Backward Phase Propagate error backward to the input layer through each layer L using the error term

in which, use ioinstead of i(L+1) if L+1 is an output layer;


19

Step 6. Weight update: Update weights according to the formula

Step7. Repeat steps 1-6 until the stop criterion is satisfied, which may be chosen as the mean of the total error

is sufficiently small.

DeepLearning:MoKvaKon

•  Shallowmodels(SVMs,one‐hidden‐layerNNets,boosKng,etc…)areunlikelycandidatesforlearninghigh‐levelabstracKonsneededforAI

•  Supervisedtrainingofmany‐layeredNNetsisadifficultopKmizaKonproblemsincetherearetoomanyweightstobeadjustedbutlabeledsamplesarelimited.

•  ThereisahugecollecKonofunlabeleddata,itishardtolabelthem.•  Unsupervisedlearningcoulddo“local‐learning”(eachmoduletries

itsbesttomodelwhatitsees)

20

DeepLearning:Overview

•  DeeplearningisusuallybestwheninputspaceislocallystructuredspaKallyortemporally:images,language,etc.vsarbitraryinputfeatures

•  Incurrentmodels,layersojenlearninanunsupervisedmodeanddiscovergeneralfeaturesoftheinputspace

•  Thenfinallayerfeaturesarefedintosupervisedlayer(s)–  AndenKrenetworkisojensubsequentlytunedusingsupervised

trainingoftheenKrenet,usingtheiniKalweighKngslearnedintheunsupervisedphase

•  Couldalsodofullysupervisedversions,etc.(earlyBPaWempts)

21

DeepLearning:Overview

•  MulKplelayersworktobuildanimprovedfeaturespace–  Firstlayerlearns1storderfeatures

(e.g.edges…)–  2ndlayerlearnshigherorderfeatures

(combinaKonsoffirstlayerfeatures,combinaKonsofedges,etc.)

–  furtherlayerslearnmorecomplexfeatures

22

ConvoluKonalNeuralNetworks(CNN)

•  Fukushima(1980)–Neo‐Cognitron•  LeCun(1998)–ConvoluKonalNeuralNetworks

–  SimilariKestoNeo‐Cognitron

CommonCharacterisKcs:•  Aspecialkindofmul3‐layerneuralnetworks.

•  Implicitlyextractrelevantfeatures.

•  Afeed‐forwardnetworkthatcanextracttopologicalproperKesfromanimage.

•  LikealmosteveryotherneuralnetworksCNNsaretrainedwithaversionoftheback‐propaga3onalgorithm.

23

ConvoluKonalNeuralNetworks

ConvNet(Y.LeCunandY.Bengio1995)

•  Neural network with specialized connectivity structure

•  Feed-forward:- Convolve input (apply filter)- Non-linearity (rectified linear)- Pooling (local average or max)

•  Train convolutional filters byback-propagating classification error

Normalization

Pooling

Non-linearity

Convolution Filters (Learned)

Input image

Feature maps

convolution layer

subsampling layer

24


ConvoluKonandSubsamplinglayersrepeatedmanyKmes

25


Connectivity & weight sharing depends on layer

Convolu3onlayerhasmuchsmallernumberofparametersbylocalconnecKonandweightsharing

Alldifferentweights Alldifferentweights Sharedweights

26


Convolution layer: filters •  DetectthesamefeatureatdifferentposiKonsintheinputimage

features

Filter (kernel)

Feature map

Input

27


Nonlinearity Tanh

Sigmoid:1/(1+exp(‐x))

RecKfiedlinear(ReLU):max(0,x)‐Simplifiesbackprop‐Makeslearningfaster‐Makefeaturesparse

→ PreferredopKon

28


Sub-sampling layer SpaKalPooling:AverageorMax

RoleofPooling‐InvariancetosmalltransformaKons‐reducetheeffectofnoisesandshijordistorKon

Max

Average

29

ConvoluKonalNeuralNetworksNormalizaKon

ContrastnormalizaKon(between/acrossfeaturemap)‐Equalizesthefeaturesmap

Feature maps before contrast normalization

Feature mapsafter contrast normalization

30

ConvoluKonalNeuralNetworksL5Net:ConvoluKonNetworkbyLeCunforHandwriWenDigitRecogniKon

•  C1,C3,C5:ConvoluKonallayer.(5×5ConvoluKonmatrix.)

•  S2,S4:Subsamplinglayer.(byfactor2)

•  F6:Fullyconnectedlayer.

About187,000connecKon.

About14,000trainableweight.31


32


33


DeepFace:Taigmanvd,CVPR2014

Face Alignment

Representation(CNN)

34

StackedAuto‐Encoders

•  Bengio(2007)–AjerDeepBeliefNetworks(Hinton,2006)•  Stackmany(sparse)auto‐encodersinsuccessionandtrainthem

usinggreedylayer‐wisetraining(forexampleBackpropogaKon)•  DropthedecodeoutputlayereachKme

35


•  Dosupervisedtrainingonthelastlayerusingfinalfeatures

outputlayer

z1

z2

z3

36


•  UsingSojmaxclassifierattheoutputlayerresultsinbeWerperformance

z1

z2

z3

y1

y2

y3

€

P(y = 1 0 0[ ] x)

€

P(y = 0 1 0[ ] x)

€

P(y = 0 0 1[ ] x)

€

yi =ezi

ez jj∑

∂yi∂zi

= yi (1− yi)

37


Touseso>maxinBackpropaga3onlearning

€

yi =ezi

ez jj∑

∂yi∂zi

= yi (1− yi)

E = − t j lnj∑ y j

∂E∂zi

=∂E∂y j

∂y j

∂zij∑ = yi − ti

targetvalue

The output units use a non-local non-linearity:

The natural cost function is the negative log probability of the right answer

Use gradient of E for updating the weights in the previous layers in Backpropagation

38


•  ThendosupervisedtrainingontheenKrenetworktofine‐tuneallweights

z1

z2

z3

y1

y2

y3

€

P(y = 1 0 0[ ] x)

€

P(y = 0 1 0[ ] x)

€

P(y = 0 0 1[ ] x)

39

StackedAuto‐EncoderswithSparseEncoders

•  AutoencoderswillojendoadimensionalityreducKon–  PCA‐likeornon‐lineardimensionalityreducKon

•  Thisleadstoa"dense"representaKonwhichisniceintermsofuseofresources(i.e.numberofnodes)–  Allfeaturestypicallyhavenon‐zerovaluesforanyinputandthe

combinaKonofvaluescontainsthecompressedinformaKon

40

StackedAuto‐EncoderswithSparseEncoders

•  However,thisdistributedrepresentaKoncanojenmakeitmoredifficultforsuccessivelayerstopickoutthesalientfeatures

•  AsparserepresentaKonusesmorefeatureswhereatanygivenKmeasignificantnumberofthefeatureswillhavea0value–  ThisleadstomorelocalistvariablelengthencodingswhereaparKcular

node(orsmallgroupofnodes)withvalue1signifiesthepresenceofafeature(smallsetofbases)

•  Thisiseasierforsubsequentlayerstouseforlearning•  Forsparseencoding:

–  Usemorehiddennodesintheencoder

–  UseregularizaKontechniqueswhichencouragesparseness(e.g.asignificantporKonofnodeshave0outputforanygiveninput)

41

StackedAuto‐Encoders:Denoising

•  De‐noisingAuto‐Encoder–  StochasKcallycorrupttraininginstanceeachKme,butsKlltrainauto‐

encodertodecodetheuncorruptedinstance,forcingittolearncondiKonaldependencieswithintheinstance

–  BeWerempiricalresults,handlesmissingvalueswell

42

DeepBoltzmanMachine:BoltzmanMachine(BM)

•  BoltzmanmachineisarecurrentneuralnetworkhavingstochasKcneuronsanditsdynamicisdefinedintermsofenergiesofjointconfiguraKonsofthevisibleandhiddenunits

hidden units

visible units

43

DeepBoltzmanMachine:StochasKcNeuron

StochasKcneuronhaveastateof1or0whichisastochasKcfuncKonoftheneuron’sbias,b,andtheinputitreceivesfromotherneurons.

whereTisacontrolparameterhavinganalogyintemperature.

€

p(si = 1) = 1

1+ exp(−bi − s jw ji) /Tj∑

= 1

1+ exp(−ΔEi /T)

44

DeepBoltzmanMachine:StochasKcNeuron

0.5

00

1

€

bi + s jw jij∑

45

hightempraturelowtemprature

€

p(si = 1) = 1

1+ exp(−bi − s jw ji) /Tj∑

DeepBoltzmanMachine:BMStateUpdate

InaBoltzmannmachine,atrialforastatetransiKonisatwo‐stepprocess.

1.  Givenastateofvisibleandhiddenunits(v,h),firstaunitjisselectedasacandidatetochangestate.TheselecKonprobabilityusuallyhasuniformdistribuKonovertheunits.

2.  Thenthechosenunitwillhaveastateof1or0acccordingtoformulagivenbefore

46

DeepBoltzmanMachine:BMEnergy

biasofuniti

weightbetweenunitsiandj

EnergywithconfiguraKonvonthevisibleunitsandhonthehiddenunits

binarystateofunitiinjointconfiguraKonv,h

indexeseverynon‐idenKcalpairofiandjonce

47

StackedDeepBoltzmanMachine:BMEnergy

•  The probability of a jointconfiguraKon over both visibleandhiddenunitsdependson theenergy of that joint configuraKoncompared with the energy of allotherjointconfiguraKons. (T: temprature, it drops in p(v,h)equaKonwhenitissetasT=1)

•  TheprobabilityofaconfiguraKonof the visible units is the sum ofthe probabiliKes of all the jointconfiguraKonsthatcontainit.

€

p(v,h) =e−E (v,h ) /T

e−E (u,g ) /Tu,g∑

€

p(v) =

e−E(v,h ) /Th∑e−E(u,g ) /T

u,g∑

parKKonfuncKon

48

DeepBoltzmanMachine:BMLearning

•  EverythingthatoneweightneedstoknowabouttheotherweightsandthedatainordertodomaximumlikelihoodlearningiscontainedinthedifferenceoftwocorrelaKons.

€

∂log p(v)∂wij

= sis j v− sis j free .

Derivative of log probability of one training vector

Expected value of product of states at thermal equilibrium when the training vector is clamped on the visible units

Expected value of product of states at thermal equilibrium when nothing is clamped

49

DeepBoltzmanMachine:RestrictedBM(RBM)

•  InrestrictedBoltzmanmachinetheconnecKvityisrestrictedtomakeinferenceandlearningeasier.–  Onlyonelayerofhiddenunits.–  NoconnecKonsbetweenhiddenunits.

•  InanRBM,thehiddenunitsarecondiKonallyindependentgiventhevisiblestates.Itonlytakesonesteptoreachthermalequilibriumwhenthevisibleunitsareclamped.–  Sowecanquicklygettheexactvalueof

<sisj>tobeusedintraining

hidden

i

j

visible

50

DeepBoltzmanMachine:SingleRBMLearning

Start with a training vector on the visible units.

Then alternate between updating all the hidden units in parallel and updating all the visible units in parallel.

i

j

i

j

i

j

i

j

t=0t=1t=2t=infinity

afantasy

51


Contras3vedivergencelearning:Aquickwaytotrain(learn)anRBM

•  Startwithatrainingvectoronthevisibleunits.•  Updateallthehiddenunitsinparallel•  Updatetheallthevisibleunitsinparalleltogeta“reconstrucKon”.•  Updatethehiddenunitsagain.

Thisisnotfollowingthegradientoftheloglikelihood.Butitworkswell.

€

Δwij = ε ( <sis j>0 − <sis j>

1)

i

j

i

j

t=0t=1

52


UsinganRBMtolearnamodelofadigitclass

i

j

i

j

t=0t=1

100 hidden units (features)

256 visible units (pixels)

Reconstructions by model trained on 2’s

Data

Reconstructions by model trained on 3’s

53

DeepBoltzmanMachine:Pre‐trainingDBMTrainingadeepnetworkbystackingRBMs•  Firsttrainalayeroffeaturesthatreceiveinputdirectlyfromthe

pixels.•  ThentreattheacKvaKonsofthetrainedfeaturesasiftheywere

pixelsandlearnfeaturesinasecondhiddenlayer.

•  Thendoitagainforthenextlayers

54

DeepBoltzmanMachine:Pre‐trainingDBMCombiningRBMstomakeaDeepBM

€

W1Train this RBM first

Then train this RBM

copy binary state h1 for each v

Compose the two RBM models to make a single DBN model

When only downward connections are used, it’s not a Boltzmann machine but Deep Belief Network

€

W1

€

W2

€

W2

€

W1T

55

DeepBoltzmanMachine:Pre‐trainingDBMInordertouseDBMforrecogniKon

(discriminaKon):

•  intopRBMusealsolabelunits:L‐  useKlabelnodesiffthereareKclasses

‐  alabelvectorisobtainedbyseYngthenodeinLcorrespondingtotheclassofthedatato“1”,whilealltheothersare“0”

•  trainthetopRBMtogetherwithlabelunits

•  Themodellearnsajointdensityforlabelsandimages.

•  ApplypaWernatboWomlayerandthenobserveLabelatthetoplayer 3 layer DBM

€

h3

€

W1

€

v€

W2

€

W3

€

W1T

€

W2T

€

L

56

DeepBoltzmanMachine:Pre‐trainingDBMTogeneratedata:UsegeneraKonconnecKons:

W3:recurrent;W1,W2:feedforward

1.ClampLtoalabelvectorandthengetanequilibriumsamplefromthetoplevelRBMbyperformingalternaKngGibbssamplingforalongKme(usinggreenconnecKons)

2.Performatop‐downpasstogetstatesforalltheotherlayers(usingblue

connecKons).

•  ThelowerlevelboWom‐up(red)connecKonsarenotpartofthegeneraKvemodel.

€

h3

3 layer DBN €

W1

€

v€

W2

€

W3

€

L

57

AneuralnetworkmodelofdigitrecogniKon&modeling•  handwriWendigitsas28x28

imagesfordigits0,1,2..9

•  10classesso10labelunits

seemoviesat:

hWp://www.cs.toronto.edu/~hinton/digits.html

2000 top-level units

500 units

500 units

28 x 28 pixel image

10 label units

58

AneuralnetworkmodelofdigitrecogniKon:finetuning•  FirstlearnonelayerataKme

bystackingRBMs:Treatthisas“pre‐training”thatfindsagoodiniKalsetofweightswhichcanthenbefine‐tunedbyalocalsearchprocedure.


500 units

500 units

28 x 28 pixel image


500 units

500 units

28 x 28 pixel image

•  Then add a 10-way softmax at the top and do backpropagation

10 softmax units

59

MoreApplicaKonexamplesonDeepNeuralNetworks

SpeechRecogniKonBreakthroughfortheSpoken,TranslatedWordhWps://www.youtube.com/watch?v=Nu‐nlQqFCKg

PublishedonNov8,2012MicrosojChiefResearchOfficerRickRashiddemonstratesaspeechrecogniKonbreakthroughviamachinetranslaKonthatconvertshisspokenEnglishwordsintocomputer‐generatedChineselanguage.ThebreakthroughispaWernedajerdeepneuralnetworksandsignificantlyreduceserrorsinspokenaswell

60

•  June2012,Googledemonstratedoneofthelargestneuralnetworksyet,withmorethanabillionconnecKons.AteamledbyStanfordcomputerscienceprofessorAndrewNgandGoogleFellowJeffDeanshowedthesystemimagesfrom10millionrandomlyselectedYouTubevideos.Onesimulatedneuroninthesojwaremodelfixatedonimagesofcats.Othersfocusedonhumanfaces,yellowflowers,andotherobjects.Byusingthepowerofdeeplearning,thesystemidenKfiedthesediscreteobjectseventhoughnohumanshadeverdefinedorlabeledthem.

hWps://www.youtube.com/watch?v=‐rIb_MEiylw

61

MoreApplicaKonexamples

ClosingRemarks

“Thebasicidea—thatsojwarecansimulatetheneocortex’slargearrayofneuronsinanarKficial“neuralnetwork”—isdecadesold,andithasledtoasmanydisappointmentsasbreakthroughs.ButbecauseofimprovementsinmathemaKcalformulasandincreasinglypowerfulcomputers,computerscienKstscannowmodelmanymorelayersofvirtualneuronsthaneverbefore.

WithmassiveamountsofcomputaKonalpower,machinescannowrecognizeobjectsandtranslatespeechinrealKme.ArKficialintelligenceisfinallygeYngsmart.”

RobertD.HofonApril23,2013,MITTechnologyreview

62

Credits

Deeplearningnetwork:hWp://deeplearning.net/

CourseraOpenCourse:NeuralNetworksbyGeofreyHintonhWps://www.coursera.org/course/neuralnets

METUEE543:Neurocomputers,LectureNotesbyUgurHalıcıhWp://www.eee.metu.edu.tr/~halici/courses/543LectureNotes/

543index.html

DeeplearningtutorialatStanford

hWp://ufldl.stanford.edu/tutorial/

63

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