台大-李宏毅-B站机器学习视频-课件神经网络与深度学习CNN

ConvolutionalNeuralNetwork,WhyCNNforImage?,Canthenetworkbesimplifiedbyconsideringthepropertiesofimages?,Themostbasicclassifiers,Use1stlayerasmoduletobuildclassifiers,Use2ndlayerasmodule,Zeiler,M.D.,ECCV2014,Representedaspixels,WhyCNNforImage,Somepatternsaremuchsmallerthanthewholeimage,Aneurondoesnothavetoseethewholeimagetodiscoverthepattern.,“beak”detector,Connectingtosmallregionwithlessparameters,WhyCNNforImage,Thesamepatternsappearindifferentregions.,“upper-leftbeak”detector,“middlebeak”detector,Theycanusethesamesetofparameters.,Doalmostthesamething,WhyCNNforImage,Subsamplingthepixelswillnotchangetheobject,subsampling,bird,bird,Wecansubsamplethepixelstomakeimagesmaller,Lessparametersforthenetworktoprocesstheimage,ThewholeCNN,FullyConnectedFeedforwardnetwork,catdog,Convolution,MaxPooling,Convolution,MaxPooling,Flatten,Canrepeatmanytimes,ThewholeCNN,Convolution,MaxPooling,Convolution,MaxPooling,Flatten,Canrepeatmanytimes,Somepatternsaremuchsmallerthanthewholeimage,Thesamepatternsappearindifferentregions.,Subsamplingthepixelswillnotchangetheobject,Property1,Property2,Property3,ThewholeCNN,FullyConnectedFeedforwardnetwork,catdog,Convolution,MaxPooling,Convolution,MaxPooling,Flatten,Canrepeatmanytimes,CNNConvolution,6x6image,Filter1,Filter2,Thosearethenetworkparameterstobelearned.,Matrix,Matrix,Eachfilterdetectsasmallpattern(3x3).,Property1,CNNConvolution,6x6image,Filter1,3,-1,stride=1,CNNConvolution,6x6image,Filter1,3,-3,Ifstride=2,Wesetstride=1below,CNNConvolution,6x6image,Filter1,3,-1,-3,-1,-3,1,0,-3,-3,-3,0,1,3,-2,-2,-1,stride=1,Property2,CNNConvolution,6x6image,3,-1,-3,-1,-3,1,0,-3,-3,-3,0,1,3,-2,-2,-1,Filter2,-1,-1,-1,-1,-1,-1,-2,1,-1,-1,-2,1,-1,0,-4,3,Dothesameprocessforeveryfilter,stride=1,4x4image,FeatureMap,CNNColorfulimage,Filter1,Filter2,Colorfulimage,image,convolution,Convolutionv.s.FullyConnected,Fully-connected,6x6image,Filter1,1:,2:,3:,7:,8:,9:,13:,14:,15:,Onlyconnectto9input,notfullyconnected,4:,10:,16:,1,0,0,0,0,1,0,0,0,0,1,1,3,Lessparameters!,Filter1,1:,2:,3:,7:,8:,9:,13:,14:,15:,4:,10:,16:,1,0,0,0,0,1,0,0,0,0,1,1,3,-1,Sharedweights,6x6image,Lessparameters!,Evenlessparameters!,ThewholeCNN,FullyConnectedFeedforwardnetwork,catdog,Convolution,MaxPooling,Convolution,MaxPooling,Flatten,Canrepeatmanytimes,CNNMaxPooling,3,-1,-3,-1,-3,1,0,-3,-3,-3,0,1,3,-2,-2,-1,Filter2,-1,-1,-1,-1,-1,-1,-2,1,-1,-1,-2,1,-1,0,-4,3,Filter1,CNNMaxPooling,6x6image,3,0,1,3,-1,1,3,0,2x2image,Eachfilterisachannel,Newimagebutsmaller,Conv,MaxPooling,ThewholeCNN,Convolution,MaxPooling,Convolution,MaxPooling,Canrepeatmanytimes,Anewimage,Thenumberofthechannelisthenumberoffilters,Smallerthantheoriginalimage,ThewholeCNN,FullyConnectedFeedforwardnetwork,catdog,Convolution,MaxPooling,Convolution,MaxPooling,Flatten,Anewimage,Anewimage,Flatten,Flatten,3,0,1,3,-1,1,0,3,FullyConnectedFeedforwardnetwork,Onlymodifiedthenetworkstructureandinputformat(vector-3-Dtensor),CNNinKeras,Convolution,MaxPooling,Convolution,MaxPooling,input,Thereare253x3filters.,Input_shape=(1,28,28),1:black/weight,3:RGB,28x28pixels,3,-1,-3,1,3,Onlymodifiedthenetworkstructureandinputformat(vector-3-Dtensor),CNNinKeras,Convolution,MaxPooling,Convolution,MaxPooling,input,1x28x28,25x26x26,25x13x13,50 x11x11,50 x5x5,Howmanyparametersforeachfilter?,Howmanyparametersforeachfilter?,9,225,Onlymodifiedthenetworkstructureandinputformat(vector-3-Dtensor),CNNinKeras,Convolution,MaxPooling,Convolution,MaxPooling,input,1x28x28,25x26x26,25x13x13,50 x11x11,50 x5x5,Flatten,1250,FullyConnectedFeedforwardnetwork,output,LiveDemo,Convolution,MaxPooling,Convolution,MaxPooling,input,253x3filters,503x3filters,WhatdoesCNNlearn?,50 x11x11,Theoutputofthek-thfilterisa11x11matrix.,Degreeoftheactivationofthek-thfilter:,=111=111,11,11,x,=max,(gradientascent),Convolution,MaxPooling,Convolution,MaxPooling,input,253x3filters,503x3filters,WhatdoesCNNlearn?,50 x11x11,Theoutputofthek-thfilterisa11x11matrix.,Degreeoftheactivationofthek-thfilter:,=111=111,=max,(gradientascent),Foreachfilter,WhatdoesCNNlearn?,Convolution,MaxPooling,input,Convolution,MaxPooling,flatten,=max,Eachfigurecorrespondstoaneuron,Findanimagemaximizingtheoutputofneuron:,Convolution,MaxPooling,input,Convolution,MaxPooling,flatten,WhatdoesCNNlearn?,=max,Canweseedigits?,0,1,2,3,4,5,6,7,8,DeepNeuralNetworksareEasilyFooled,WhatdoesCNNlearn?,0,1,2,3,4,5,6,7,8,0,1,2,3,4,5,6,7,8,=max,=max+,Overallpixelvalues,DeepDream,Givenaphoto,machineaddswhatitsees,CNN,,Modifyimage,CNNexaggerateswhatitsees,DeepDream,Givenaphoto,machineaddswhatitsees,DeepStyle,Givenaphoto,makeitsstylelikefamouspaintings,DeepStyle,Givenaphoto,makeitsstylelikefamouspaintings,DeepStyle,CNN,CNN,content,style,CNN,?,ANeuralAlgorithmofArtisticStyle/abs/1508.06576,MoreApplication:PlayingGo,Network,19x19vector,Black:1,white:-1,none:0,19x19vector,Fully-connectedfeedforwardnetworkcanbeused,ButCNNperformsmuchbetter.,19x19matrix(image),MoreApplication:PlayingGo,CNN,CNN,recordofpreviousplays,Target:“天元”=1else=0,Target:“五之5”=1else=0,Training:,WhyCNNforplayingGo?,SomepatternsaremuchsmallerthanthewholeimageThesamepatternsappearindifferentregions.,AlphaGouses5x5forfirstlayer,WhyCNNforplayingGo?,Subsamplingthepixelswillnotchangetheobject,AlphaGodoesnotuseMaxPooling,MaxPooling,Howtoexplainthis?,MoreApplication:Speech,Time,Frequency,Spectrogram,CNN,Image,Thefiltersmoveinthefrequencydirection.,MoreApplication:Text,Sourceofimage:/viewdoc/download?doi=03.6858&rep=rep1&type=pdf,Tolearnmore,Themethodsofvisualizationintheseslideshttps:/blog.keras.io/how-convolutional-neural-networks-see-the-world.h