machine learning & data mining - yisong yue · recap: bias-variance trade -off 0 20 40 60 80...

MachineLearning&DataMiningCMS/CS/CNS/EE155

Lecture2:Perceptron&StochasticGradient

Descent

Recap: BasicRecipe(supervised)

• TrainingData:

• ModelClass:

• LossFunction:

• LearningObjective:

S = (xi, yi ){ }i=1N

f (x |w,b) = wT x − b

L(a,b) = (a− b)2

LinearModels

SquaredLoss

x ∈ RD

y ∈ −1,+1{ }

argminw,b

L yi, f (xi |w,b)( )i=1

N

∑

Optimization Problem2

Recap:Bias-VarianceTrade-off

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Recap:CompletePipeline

S = (xi, yi ){ }i=1N

TrainingData

f (x |w,b) = wT x − b

ModelClass(es)

L(a,b) = (a− b)2

LossFunction

argminw,b

L yi, f (xi |w,b)( )i=1

N

∑

CrossValidation&ModelSelection Profit!

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Today

• TwoBasicLearningAlgorithms

• PerceptronAlgorithm

• (Stochastic)GradientDescent– Aka,actuallysolvingtheoptimizationproblem

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ThePerceptron

• Oneoftheearliestlearningalgorithms– 1957byFrankRosenblatt

• Stillagreatalgorithm– Fast– Cleananalysis– PrecursortoNeuralNetworks

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FrankRosenblattwiththeMark1PerceptronMachine

PerceptronLearningAlgorithm(LinearClassificationModel)

• w1 =0,b1 =0• Fort=1….– Receiveexample(x,y)– Iff(x|wt,bt)=y• [wt+1, bt+1]=[wt, bt]

– Else• wt+1=wt +yx• bt+1 =bt - y

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S = (xi, yi ){ }i=1N

y ∈ +1,−1{ }

TrainingSet:

Gothroughtrainingsetinarbitraryorder(e.g.,randomly)

f (x |w) = sign(wT x − b)

• Lineisa1D,Planeis2D• Hyperplane ismanyD– IncludesLineandPlane

• Definedby(w,b)

• Distance:

• SignedDistance:

Aside:Hyperplane Distance

wT x − bw

wT x − bw

w

un-normalizedsigneddistance!

LinearModel=

b/|w|

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PerceptronLearning

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Misclassified!

PerceptronLearning

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PerceptronLearning

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Correct!

PerceptronLearning

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AllTrainingExamplesCorrectlyClassified!

PerceptronLearning

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AllTrainingExamplesCorrectlyClassified!

PerceptronLearning

Recap:PerceptronLearningAlgorithm(LinearClassificationModel)

• w1 =0,b1 =0• Fort=1….– Receiveexample(x,y)– Iff(x|wt)=y• [wt+1, bt+1]=[wt, bt]

– Else• wt+1=wt +yx• bt+1 =bt - y

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S = (xi, yi ){ }i=1N

y ∈ +1,−1{ }

TrainingSet:

Gothroughtrainingsetinarbitraryorder(e.g.,randomly)

f (x |w) = sign(wT x − b)

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ComparingtheTwoModels

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ConvergencetoMistakeFree=LinearlySeparable!

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Margin

γ =maxwmin(x,y)

y(wT x)w

LinearSeparability

• AclassificationproblemisLinearlySeparable:– Existswwithperfectclassificationaccuracy

• SeparablewithMarginγ:

• LinearlySeparable:γ >0

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γ =maxwmin(x,y)

y(wT x)w

PerceptronMistakeBound

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#MistakesBoundedBy: R2

γ 2

Margin

R =maxx

x

**IfLinearlySeparable

MoreDetails:http://www.cs.nyu.edu/~mohri/pub/pmb.pdf

Holdsforanyorderingoftrainingexamples!

“Radius”ofFeatureSpace

IntheRealWorld…

• MostproblemsareNOTlinearlyseparable!

• Mayneverconverge…

• Sowhattodo?

• Usevalidationset!

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EarlyStoppingviaValidation

• RunPerceptronLearningonTrainingSet

• EvaluatecurrentmodelonValidationSet

• Terminatewhenvalidationaccuracystopsimproving

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https://en.wikipedia.org/wiki/Early_stopping

OnlineLearningvs BatchLearning

• OnlineLearning:– Receiveastreamofdata(x,y)– Makeincrementalupdates(typically)– PerceptronLearningisaninstanceofOnlineLearning

• BatchLearning– Givenallthedataupfront– Canuseonlinelearningalgorithmsforbatchlearning– E.g.,streamthedatatothelearningalgorithm

53https://en.wikipedia.org/wiki/Online_machine_learning

Recap: Perceptron

• Oneofthefirstmachinelearningalgorithms

• Benefits:– Simpleandfast– Cleananalysis

• Drawbacks:– Mightnotconvergetoaverygoodmodel–Whatistheobjectivefunction?

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(Stochastic)GradientDescent

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BacktoOptimizingObjectiveFunctions

• TrainingData:

• ModelClass:

• LossFunction:

• LearningObjective:

S = (xi, yi ){ }i=1N

f (x |w,b) = wT x − b

L(a,b) = (a− b)2

LinearModels

SquaredLoss

x ∈ RD

y ∈ −1,+1{ }

argminw,b

L yi, f (xi |w,b)( )i=1

N

∑

OptimizationProblem56

BacktoOptimizingObjectiveFunctions

• Typically,requiresoptimizationalgorithm.

• Simplest:GradientDescent

• ThisLecture:stickwithsquaredloss– Talkaboutvariouslossfunctionsnextlecture

argminw,b

L(w,b) ≡ L yi, f (xi |w,b)( )i=1

N

∑

GradientReviewforSquaredLoss

∂wL(w,b) = ∂w L yi, f (xi |w,b)( )i=1

N

∑

L(a,b) = (a− b)2

= ∂wL yi, f (xi |w,b)( )i=1

N

∑

= −2(yi − f (xi |w,b))∂w f (xi |w,b)i=1

N

∑

f (x |w,b) = wT x − b= −2(yi − f (xi |w,b))xii=1

N

∑

LinearityofDifferentiation

ChainRule

GradientDescent

• Initialize:w1 =0,b1 =0

• Fort=1…

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wt+1 = wt −η t+1∂wL(wt,bt )

bt+1 = bt −η t+1∂bL(wt,bt )

“StepSize”

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HowtoChooseStepSize?

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L

η =1 ∂wL(w) = −2(1−w)

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L

η =1 ∂wL(w) = −2(1−w)

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L

η =1 ∂wL(w) = −2(1−w)

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L

η =1 ∂wL(w) = −2(1−w)

OscillateInfinitely!

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L

η = 0.0001 ∂wL(w) = −2(1−w)

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L

η = 0.0001 ∂wL(w) = −2(1−w)

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L

η = 0.0001 ∂wL(w) = −2(1−w)

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L

η = 0.0001 ∂wL(w) = −2(1−w)

TakesReallyLongTime!

HowtoChooseStepSize?

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Iterations

Loss

NotethattheabsolutescaleisnotmeaningfulFocusontherelativemagnitude differences

AsLargeAsPossible!(WithoutDiverging)

BeingScaleInvariant

• Considerthefollowingtwogradientupdates:

• Suppose:– Howarethetwostepsizesrelated?

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wt+1 = wt −η t+1∂wL(wt,bt )

wt+1 = wt −η̂ t+1∂wL̂(wt,bt )

L̂ =1000L

η̂ t+1 =η /1000

PracticalRulesofThumb

• DivideLossFunctionbyNumberofExamples:

• Startwithlargestepsize– Iflossplateaus,dividestepsizeby2– (Canalsouse advancedoptimizationmethods)– (Stepsizemustdecreaseovertimetoguaranteeconvergencetoglobaloptimum)

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wt+1 = wt −η t+1

N"

#$

%

&'∂wL(w

t,bt )

Aside: Convexity

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1/15/2015 ConvexFunction.svg

file:///Users/yyue/Downloads/ConvexFunction.svg 1/2

ImageSource:http://en.wikipedia.org/wiki/Convex_function

Easytofindglobaloptima!

Strictconvexifdiffalways>0

NotConvex

Aside: Convexity

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L(x2 ) ≥ L(x1)+∇L(x1)T (x2 − x1)

Functionisalwaysabovethelocallylinearextrapolation

Aside:Convexity

• Alllocaloptimaareglobaloptima:

• Strictlyconvex:uniqueglobaloptimum:

• Almostallstandardobjectivesare(strictly)convex:– SquaredLoss,SVMs,LR,Ridge,Lasso– Wewillseenon-convexobjectiveslater(e.g.,deeplearning)

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GradientDescentwillfindoptimum

Assumingstepsizechosensafely

Convergence

• AssumeLisconvex• Howmanyiterationstoachieve:

• If:– ThenO(1/ε2)iterations

• If:– ThenO(1/ε)iterations

• If:– ThenO(log(1/ε))iterations

74MoreDetails:Bubeck TextbookChapter3

L(a)− L(b) ≤ ρ a− b Lis“ρ-Lipschitz”

L(w)− L(w*) ≤ ε

∇L(a)−∇L(b) ≤ ρ a− bLis“ρ-smooth”

L(a) ≥ L(b)+∇L(b)T (a− b)+ ρ2a− b 2

Lis“ρ-stronglyconvex”

Convergence

• Ingeneral,takesinfinitetimetoreachglobaloptimum.• Butingeneral,wedon’tcare!

– Aslongaswe’recloseenoughtotheglobaloptimum

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Loss

Howdoweknowifwe’rehere?

Andnothere?

WhentoStop?

• Convergenceanalyses=worst-caseupperbounds– Whattodoinpractice?

• Stopwhenprogressissufficientlysmall– E.g.,relativereductionlessthan0.001

• Stopafterpre-specified#iterations– E.g.,100000

• Stopwhenvalidationerrorstopsgoingdown

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LimitationofGradientDescent

• Requiresfullpassovertrainingsetperiteration

• Veryexpensiveiftrainingsetishuge

• Doweneedtodoafullpassoverthedata?

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∂wL(w,b | S) = ∂w L yi, f (xi |w,b)( )i=1

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StochasticGradientDescent

• SupposeLossFunctionDecomposesAdditively

• Gradient=expectedgradientofsub-functions

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L(w,b) = 1N

Li (w,b)i=1

N

∑ = Ei Li (w,b)[ ]

EachLi correspondstoasingledatapoint

∂wL(w,b) = ∂w Ei Li (w,b)[ ] = Ei ∂wLi (w,b)[ ]Li (w,b) ≡ yi − f (xi |w,b( )2

StochasticGradientDescent

• Sufficestotakerandomgradientupdate– Solongasitmatchesthetruegradientinexpectation

• Eachiterationt:– Choosei atrandom

• SGDisanonlinelearningalgorithm!

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wt+1 = wt −η t+1∂wLi (w,b)

bt+1 = bt −η t+1∂bLi (w,b)

ExpectedValueis: ∂wL(w,b)

Mini-BatchSGD

• EachLi isasmallbatchoftrainingexamples– E.g,.500-1000examples– Canleveragevectoroperations– Decreasevolatilityofgradientupdates

• Industrystate-of-the-art– Everyoneusesmini-batchSGD– Oftenparallelized• (e.g.,differentcoresworkondifferentmini-batches)

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CheckingforConvergence

• Howtocheckforconvergence?– Evaluatinglossonentiretrainingsetseemsexpensive…

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Loss

CheckingforConvergence

• Howtocheckforconvergence?– Evaluatinglossonentiretrainingsetseemsexpensive…

• Don’tcheckaftereveryiteration– E.g.,checkevery1000iterations

• Evaluatelossonasubsetoftrainingdata– E.g.,theprevious5000examples.

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Recap:StochasticGradientDescent

• Conceptually:– DecomposeLossFunctionAdditively– ChooseaComponentRandomly– GradientUpdate

• Benefits:– Avoiditeratingentiredatasetforeveryupdate– Gradientupdateisconsistent(inexpectation)

• IndustryStandard

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PerceptronRevisited(WhatistheObjectiveFunction?)

• w1 =0,b1 =0• Fort=1….– Receiveexample(x,y)– Iff(x|wt,bt)=y• [wt+1, bt+1]=[wt, bt]

– Else• wt+1=wt +yx• bt+1 =bt - y

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S = (xi, yi ){ }i=1N

y ∈ +1,−1{ }

TrainingSet:

Gothroughtrainingsetinarbitraryorder(e.g.,randomly)

f (x |w) = sign(wT x − b)

Perceptron(Implicit)Objective

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Li (w,b) =max 0,−yi f (xi |w,b){ }

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Loss

Recap:CompletePipeline

S = (xi, yi ){ }i=1N

TrainingData

f (x |w,b) = wT x − b

ModelClass(es)

L(a,b) = (a− b)2

LossFunction

argminw,b

L yi, f (xi |w,b)( )i=1

N

∑

CrossValidation&ModelSelection Profit!

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UseSGD!

NextWeek

• DifferentLossFunctions– HingeLoss(SVM)– LogLoss(LogisticRegression)

• Non-linearmodelclasses– NeuralNets

• Regularization

• NextThursdayRecitation:– LinearAlgebra&Calculus

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