人工神经网络教案01

人工神经网络教案01 Neural Networks for Machine Learning Lecture1a Whydo weneed machine learning?Geoffrey Hinton with Nitish Srivastava Kevin Swersky Whatis Machine Learning?It isvery hard to writeprograms thatsolve problemslike recognizinga three-dimensional objectfrom anovel viewpointin newlighting conditionsin acluttered scene.?We dont knowwhat program to writebecause wedont knowhow itsdone inour brain.?Even ifwe hada goodidea abouthow todo it,the programmight behorrendously plicated.?It ishard to write a programto pute theprobability thata creditcard transactionis fraudulent.?There maynot beany rulesthat areboth simpleand reliable.We need to binea verylarge number of weakrules.?Fraud is a movingtarget.The programneeds tokeep changing.The MachineLearning Approach?Instead ofwriting aprogram byhand foreach specifictask,we collectlots ofexamples thatspecify the correct outputfor agiven input.?A machinelearning algorithmthen takesthese examplesand producesaprogramthat does the job.?The programproduced by the learning algorithm maylook verydifferent froma typicalhand-written program.It maycontain millionsof numbers.?If wedo itright,the programworksfornew casesas wellas theones wetrained iton.?If thedata changesthe programcan changetoo bytraining on the newdata.?Massive amountsof putationare nowcheaper thanpaying someoowritea task-specific program.Some examplesof tasksbest solvedby learning?Recognizing patterns:?Objects inreal scenes?Facial identitiesor facialexpressions?Spoken words?Recognizing anomalies:?Unusual sequencesof creditcard transactions?Unusual patternsof sensorreadings ina nuclearpower plant?Prediction:?Future stockprices orcurrency exchangerates?Which movieswill aperson like?A standardexample of machinelearning?A lotof geicsis doneon fruitflies.?They areconvenient because they breedfast.?We alreadyknow alot aboutthem.?The MNISTdatabase ofhand-written digitsis thethe machinelearning equivalentof fruitflies.?They arepublicly availableand wecan learn them quitefast ina moderate-sized neural .?We knowa hugeamount abouthow wellvarious machinelearning methodsdo onMNIST.?We willuse MNISTas ourstandard task.It isvery hardto say what makesa2Beyond MNIST:The ImageNettask?1000different objectclasses in1.3million high-resolution trainingimages fromthe web.?Best systeminxxpetition got47%error for its firstchoice and25%error forits top5choices.?Jitendra Malik(an eminentneuralsceptic)said thatthis petitionis agood testof whetherdeep neural works workwell forobject recognition.?A verydeep neural(Krizhevsky et.al.xx)gets lessthat40%error forits firstchoice andless than20%foritstop5choices(see lecture5).Some examplesfrom anearlier versionof the Itcan dealwith awide rangeof objectsIt makessome reallycool errorsThe SpeechRecognition Task?A speech recognition systemhas severalstages:?Pre-processing:Convert thesound waveinto avector of acoustic coefficients.Extract anew vectorabout every10mille seconds.?The acoustic model:Use afew adjacentvectors of acoustic coefficientsto placebets onwhich partof whichphoneme isbeing spoken.?Decoding:Find thesequence ofbets thatdoesthebest jobof fittingthe acousticdata andalso fittinga modelof thekinds ofthings peoplesay.?Deep neuralworks pioneeredby GeorgeDahl and Abdel-rahman Mohamedare nowreplacing theprevious machinelearning method for theacousticmodel.Phone recognitionon theTIMIT benchmark(Mohamed,Dahl,&Hinton,xx)?After standardpost-processing usinga bi-phone model,a deep with8layers gets20.7%error rate.?The bestprevious speaker-independent resulton TIMITwas24.4%and thisrequired averagingseveral models.?Li Deng(at MSR)realised thatthis resultcould changethe wayspeechrecognitionwas done.15frames of40filterbank outputs+their temporalderivatives2000logistic hiddenunits2000logistic hiddenunits2000logistic hiddenunits183HMM-state labelsnot pre-trained5more layers of pre-trained weightsWord errorrates fromMSR,IBM,&Google(Hinton et.al.IEEE SignalProcessing Magazine,Novxx)The taskHours oftraining dataDeep neuralwork GaussianMixture ModelGMM withmore dataSwitchboard(Microsoft Research)30918.5%27.4%18.6%(2000hrs)English broadcastnews(IBM)5017.5%18.8%Google voicesearch(android4.1)5,87012.3%(and falling)16.0%(5,870hrs)Neural Networks for MachineLearning Lecture1b Whatare neuralworks?Geoffrey Hintonwith NitishSrivastava KevinSwersky Reasonsto studyneural putation?To understandhow the brain actually works.?Its verybig and very plicatedand made of stuff that dieswhen youpoke itaround.So weneed touse putersimulations.?To understanda styleof parallelputation inspiredby neuronsand theiradaptive connections.?Very differentstyle fromsequential putation.?should begood forthings thatbrains aregood at(e.g.vision)?Should bebad forthings thatbrains arebad at(e.g.23x71)?To solvepractical problemsby usingnovel learningalgorithms inspiredbythe brain(this course)?Learning algorithmscan bevery usefuleven ifthey arenot howthebrainactuallyworks.A typicalcortical neuron?Gross physicalstructure:?There is one axonthat branches?There is a dendritic tree thatcollects inputfrom otherneurons.?Axons typicallycontact dendritictrees atsynapses?A spike of activityin theaxon causescharge to be injectedinto thepost-synaptic neuron.?Spike generation:?There is an axon hillock thatgenerates outgoingspikes wheneverenough chargehas flowedin atsynapses todepolarize thecell membrane.axon bodydendritictreeaxonhillockSynapses?When aspike of activity travelsalong anaxon andarrives ata synapseit causesvesicles of transmitter chemicaltobereleased.?There areseveral kindsoftransmitter.?The transmittermolecules diffuse across thesynaptic cleftand bindto receptor molecules in the membraneof thepost-synaptic neuronthus changingtheir shape.?This opensup holesthat allowspecific ionsin orout.How synapsesadapt?The effectivenessof thesynapse can be changed:?vary thenumber ofvesicles oftransmitter.?vary thenumber ofreceptormolecules.?Synapses areslow,but theyhave advantagesover RAM?They arevery smallandverylow-power.?They adaptusing locallyavailable signals?But whatrules dothey useto decidehow tochange?How thebrain workson oneslide%?Each neuronreceives inputsfrom otherneurons-?A fewneurons alsoconnect toreceptors.-?Cortical neuronsuse spikesto municate.?The effectof each input lineontheneuron iscontrolled bya synapticweight?The weights canbepositive ornegative.?The synapticweights adaptso thatthe wholework learnsto performuseful putations?Recognizing objects,understanding language,making plans,controlling thebody.?You haveabout neuronseach with about weights.?A hugenumberofweightscanaffect theputation ina veryshort time.Much betterbandwidth thana workstation.1011104Modularity and thebrain?Different bitsof thecortex dodifferent things.?Local damageto thebrain hasspecific effects.?Specific tasksincrease theblood flowto specificregions.?But cortexlooks prettymuch the same allover.?Early braindamage makesfunctions relocate.?Cortex ismadeofgeneral purposestuffthathas theability toturn intospecial purposehardware inresponse toexperience.?This givesrapid parallelputation plusflexibility.?Conventional putersget flexibilityby havingstored sequentialprograms,but thisrequires veryfast centralprocessors toperform longsequential putations.Neural Networks for MachineLearning Lecture1c Somesimple modelsof neuronsGeoffrey Hintonwith NitishSrivastava KevinSwersky Idealizedneurons?To modelthings wehave toidealize them(e.g.atoms)?Idealization removesplicated detailsthat arenot essentialfor understandingthe mainprinciples.?It allowsus toapply mathematicsand tomake analogiesto other,familiar systems.?Once weunderstand thebasic principles,its easyto addplexity tomake the model morefaithful.?It is often worthunderstanding modelsthat areknown tobe wrong(but wemust notforget thatthey arewrong%)?E.g.neurons thatmunicate realvalues ratherthan discretespikes of activity.Linear neurons?These aresimple butputationally limited?If wecan makethem learnwe mayget insightinto moreplicated neurons.iii wx b y+=output biasindex overinput connectionsi inputth ith weighton inputLinear neurons?These aresimple butputationally limited?If wecan makethem learnwe mayget insightinto moreplicated neurons.iii wxb y+=00y iiiwxb+Binary threshold neurons?McCulloch-Pitts (1943):influenced VonNeumann.?First putea weighted sum of the inputs.?Then sendout afixed sizespikeofactivity ifthe weightedsum exceedsa threshold.?McCulloch andPitts thought that eachspike islike thetruth valueofaproposition andeach neuronbines truth values topute thetruthvalueof anotherproposition%output weightedinput10threshold Binarythreshold neurons?There aretwo equivalentways towrite theequations fora binarythresholdneuron:=yiii wxz=z1if0otherwise=yz=b+x iiw iz01if0otherwise=?bRectified LinearNeurons(sometimes calledlinear thresholdneurons)y=z=b+x iiw izif z00otherwise yz0They putea linearweightedsumof theirinputs.The output isanon-linear function of thetotal input.Sigmoid neurons?These givea real-valued output that isa smoothand boundedfunctionof their totalinput.?Typically theyusethe logistic function?They havenice derivativeswhich makelearning easy(see lecture3).y=11+e?z0.5001zyz=b+x iiw iStochasticbinary neurons?These usethesameequations aslogistic units.?But theytreat the output of thelogisticas theprobability ofproducing aspike ina shorttime window.?We cando asimilar trickfor rectifiedlinear units:?The output is treatedas thePoisson ratefor spikes.p(s=1)=11+e?z0.5001zpz=b+x iiw iNeuralNetworksforMachineLearningLecture1dAsimple exampleof learning Geoffrey Hintonwith NitishSrivastava KevinSwersky Avery simpleway torecognize handwrittenshapes?Consider aneuralworkwith twolayersofneurons.?neurons in the top layer representknown shapes.?neurons in the bottomlayer representpixel intensities.?A pixelgets tovote ifit hasink onit.?Each inkedpixel canvote for several differentshapes.?The shapethat getsthe mostvotes wins.0123456789How todisplay the weights Giveeach outputunit itsown“map”of the input imageand displaytheweighting fromeach pixelin thelocation ofthat pixelinthemap.Use ablack orwhite blobwith thearea representing the magnitudeof theweight andthe colorrepresentingthesign.The inputimage1234567890How to learn the weights Showthe workan imageand incrementthe weightsfrom activepixels tothecorrectclass.Then decrementtheweightsfrom activepixels towhatever classthe workguesses.The image1234567890The image1234567890The image1234567890The image1234567890The image1234567890The image1234567890The learnedweights Theimage1234567890The detailsof thelearningalgorithmwill beexplained infuture lectures.Why thesimple learningalgorithm isinsufficient?A twolayer workwithasingle winnerinthetoplayeris equivalentto havinga rigidtemplate foreach shape.?The winneris thetemplate thathas thebiggest overlapwith theink.?The waysin whichhand-written digitsvary aremuch tooplicated tobe capturedby simpletemplate matchesof wholeshapes.?To captureall theallowable variationsofadigit weneedtolearnthe features thatit isposed of.Examples ofhandwritten digitsthat canbe recognizedcorrectly thefirst timethey areseen NeuralNetworksforMachineLearningLecture1e Threetypes oflearningGeoffreyHintonwithNitishSrivastavaKevinSwerskyTypes oflearning task?Supervised learning?Learn topredict anoutput whengiven aninput vector.?Reinforcement learning?Learn toselect anaction tomaximize payoff.?Unsupervised learning?Discover agood internalrepresentation of the input.Two typesof supervisedlearning?Each training case consistsof aninput vectorx anda target outputt.?Regression:The targetoutputisa realnumber ora wholevector ofreal numbers.?The priceofastock in6months time.?The temperatureat noontomorrow.?Classification:The targetoutputisa classlabel.?The simplestcase isa choicebetween1and0.?We canalso havemultiple alternativelabels.How supervisedlearning typicallyworks?We startby choosinga model-class:?A model-class,f,isaway ofusing somenumerical parameters,W,to mapeachinputvector,x,into apredicted outputy.?Learning usuallymeans adjustingthe parametersto reducethe discrepancybetween thetargetoutput,t,on eachtrainingcaseandtheactual output,y,produced bythemodel.?For regression,isoftena sensiblemeasure ofthe discrepancy.?For classificationthere areother measuresthat aregenerally moresensible(they alsowork better).12(y?t)2y=f(x;W)Reinforcement learning?In reinforcement learning,theoutputis anaction orsequence ofactions andthe onlysupervisory signalisanoasional scalarreward.?The goalin selectingeach actionis tomaximize theexpected sumofthefuture rewards.?We usuallyuseadiscount factorfor delayedrewards sothat wedont haveto looktoo farinto thefuture.?Reinforcement learning is difficu