Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
What is Deep Learning ?
Search
urakarin
May 02, 2017
Technology
1
1k
What is Deep Learning ?
(Japanese document)
History and introductions of Neural Network,
社内ゼミでの資料です。
urakarin
May 02, 2017
Tweet
Share
More Decks by urakarin
See All by urakarin
WiFi講座(3)
urakarin
0
350
BadUSB
urakarin
0
410
Other Decks in Technology
See All in Technology
AIに全任せしないコーディングとマネジメント思考
kikuchikakeru
0
420
Bet "Bet AI" - Accelerating Our AI Journey #BetAIDay
layerx
PRO
4
1.5k
私とAWSとの関わりの歩み~意志あるところに道は開けるかも?~
nagisa53
1
160
GMOペパボのデータ基盤とデータ活用の現在地 / Current State of GMO Pepabo's Data Infrastructure and Data Utilization
zaimy
3
190
僕たちが「開発しやすさ」を求め 模索し続けたアーキテクチャ #アーキテクチャ勉強会_findy
bengo4com
0
1.7k
alecthomas/kong はいいぞ
fujiwara3
6
1.4k
Google Cloud で学ぶデータエンジニアリング入門 2025年版 #GoogleCloudNext / 20250805
kazaneya
PRO
11
2.5k
✨敗北解法コレクション✨〜Expertだった頃に足りなかった知識と技術〜
nanachi
1
310
KubeCon + CloudNativeCon Japan 2025 Recap
donkomura
0
150
Strands Agents & Bedrock AgentCoreを1分でおさらい
minorun365
PRO
6
220
VLMサービスを用いた請求書データ化検証 / SaaSxML_Session_1
sansan_randd
0
210
【新卒研修資料】数理最適化 / Mathematical Optimization
brainpadpr
23
10k
Featured
See All Featured
The Invisible Side of Design
smashingmag
301
51k
GraphQLとの向き合い方2022年版
quramy
49
14k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.5k
Building Applications with DynamoDB
mza
95
6.5k
Building a Modern Day E-commerce SEO Strategy
aleyda
43
7.4k
Rails Girls Zürich Keynote
gr2m
95
14k
Scaling GitHub
holman
461
140k
How to Think Like a Performance Engineer
csswizardry
25
1.8k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
48
2.9k
Designing for humans not robots
tammielis
253
25k
We Have a Design System, Now What?
morganepeng
53
7.7k
Typedesign – Prime Four
hannesfritz
42
2.7k
Transcript
σΟʔϓϥʔχϯάͬͯԿʁ
[email protected]
2017.02.08
͢͜ͱɺ͞ͳ͍͜ͱ • ͢͜ͱ • χϡʔϥϧωοτϫʔΫͷֶతͳΈ • ॳظͷܾΊํɺධՁํ๏ • ύϥϝʔλྔɺܭࢉྔͷϘϦϡʔϜײ •
ϗοτͳ • ͞ͳ͍͜ͱ • πʔϧͷ • ࣜͷ • χϡʔϥϧωοτϫʔΫҎ֎ͷػցֶश • γϯΪϡϥϦςΟͳͲͷਓೳͷະདྷ ग़య wedge.ismedia.jp
Agenda • σΟʔϓϥʔχϯάͱʁ • ྺ࢙ • χϡʔϥϧωοτϫʔΫ͔ΒσΟʔϓͳχϡʔϥϧωοτϫʔΫ • ୈҰ࣍AIϒʔϜ •
ୈೋ࣍AIϒʔϜ • ୈࡾ࣍AIϒʔϜ • Ԡ༻ྫ • ·ͱΊ
• ਂֶशͱݴ͏ • ʢਆܦࡉ๔ʣͷಇ͖Λֶͨ͠शΞϧΰϦζϜͰ͋Δ Neural Network(NN)Λ༻͍ͨਓೳͷߏஙٕज़ͷ૯শ • ͦͷதͰਂ͘େنͳߏΛ࣋ͭ͜ͱ͕ಛ σΟʔϓϥʔχϯάͱʁ
GoogLeNet, 22Layers (ILSVRC 2014)
༻ޠͷؔੑ ਓೳʢAIʣ ػցֶश χϡʔϥϧωοτϫʔΫ ਂֶश
දతͳൃද Neural Networkͷ ϒϨʔΫεϧʔͱ ౙͷ࣌ දతͳਓࡐ֫ಘ Google ͕DNN ResearchΛങऩ )JOUPO
Google ͕ Deep MindΛങऩ Baidu ͕Institute of Deep LearningΛઃཱ "OESFX/H Facebook ͕AI Research Lab.Λઃཱ -F$VO SGD (Amari) Neocognitron (Fukushima) Boltzmann Machine (Hinton+) Conv. net (LeCun+) Sparse Coding (Olshausen&Field) 1950 1960 1970 1980 1990 2000 2010 2020 Microsoft ͕MaluubaΛങऩ #FOHJP ୈҰ࣍"*ϒʔϜ ਪɾ୳ࡧ ୈೋ࣍"*ϒʔϜ ࣝදݱ &YQFSU4ZTUFN ୈࡾ࣍"*ϒʔϜ ػցֶश ਂֶश Perceptron (Rosenblatt) Back Propagation (Rumelhart) Deep Learning (Hinton+) Big Data GPU Cloud Computing ઢܗෆՄೳ YPS͕ղ͚ͳ͍ ͍ɺաֶशɺ 47.ਓؾ χϡʔϥϧωοτϫʔΫͷྺ࢙ NN ୈҰͷౙ NN ୈೋͷౙ
Torontoେֶ New Yorkେֶ Montrealେֶ
NN ͔Β DNN Neural Network Deep Neural Network
ୈҰ࣍AIϒʔϜ
୯७ύʔηϓτϩϯ ʹྖҬఆثͱͯ͠ͷχϡʔϩϯ
NAND AND OR XOR ୯७ύʔηϓτϩϯ
ୈҰͷౙ • xor͕දݱͰ͖ͳ͍
ୈೋ࣍AIϒʔϜ
ڭࢣ৴߸ ޡࠩؔ ೖྗ ग़ྗ தؒ 1 1 1 x y
t ⇥w 4 ⇥ w3 ⇥w2 ⇥w1 x1 x2 x3 x4 ⇥w 0 ⌃f y1 y2 y3 1. ଟԽ 2. ׆ੑԽؔ 3. ޡࠩؔ 4. ޡࠩٯൖ๏ ଟύʔηϓτϩϯʢMLPʣ
ଟԽʹΑͬͯxorͷ࣮ݱ NAND OR AND s2 s1 x1 x2 y x1
x2 s1 s2 y 0 0 1 0 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 = 1. ଟԽ
γάϞΠυؔɾۂઢਖ਼ؔ ඍ͕Ͱ͖ͳ͍ ֶशͰ͖ͳ͍ ʢޡࠩٯൖ๏ʣ ʹೖྗ৴߸ͷ૯Λग़ྗ৴߸ʹม͢Δؔ ׆ੑԽؔ 2. ׆ੑԽؔ 1 ⇥w
4 ⇥ w3 ⇥w2 ⇥w1 x1 x2 x3 x4 ⇥w 0 ⌃f εςοϓؔ ύʔηϓτϩϯͷ߹
3. ଛࣦؔ ޡࠩؔʢଛࣦؔʣ 1 2 N X n=1 ky tk2
N Y n=1 p(dn | x ) d=0/1ͷࣄޙ֬pʹରͯ͠࠷ਪఆΛߦ͏ ೋޡࠩͱ͢Δ ڭࢣ৴߸ ޡࠩؔ ग़ྗ y t y1 y2 y3 ճؼ ೋྨ ଟΫϥεྨ ڭࢣ৴߸ΛOne-hotදݱͱ͠ɺ ࠷ऴஈͷ׆ੑԽؔΛιϑτϚοΫεؔͱ্ͨ͠Ͱ ަࠩΤϯτϩϐʔؔ
ڭࢣ৴߸ ޡࠩؔ ೖྗ ग़ྗ தؒ 1 1 1 x y
t ⇥w 4 ⇥ w3 ⇥w2 ⇥w1 x1 x2 x3 x4 ⇥w 0 ⌃f y1 y2 y3 4. ޡࠩٯൖ๏ ޡࠩٯൖ๏
+ ^2 x y t z @z @z @z @z
@z @t @z @z @z @t @t @x ͨͱ͑ z = ( x + y )2 ͱ͍͏ࣜ z = t2 t = x + y ͱ͍͏2ͭͷࣜͰߏ͞ΕΔɻ ࿈ͱɺ߹ؔͷඍʹ͍ͭͯͷੑ࣭Ͱ͋Δ @z @x = @z @t @t @x ޡࠩٯൖ๏ 4. ޡࠩٯൖ๏
ޡࠩٯൖ๏ Ճࢉϊʔυͷٯൖ + x y z + @L @z @L
@z · 1 @L @z · 1 ࢉϊʔυͷٯൖ x y z ⇥ @L @z ⇥ @L @z · x @L @z · y 4. ޡࠩٯൖ๏ 2 100 ⇥ ⇥ 200 1.1 220 1 1.1 200 110 2.2 ΓΜ͝ͷݸ ফඅ੫ ɹ۩ମྫɹ
֬తޯ߱Լ๏ • ϛχόονֶश • ֶशͷߋ৽ํ๏ • Momentum • AdaGrad •
Adam • RMSProp
ୈೋͷౙ • ܭࢉྔ͕ଟ͍͗ͯ͢ • ہॴղɾաֶशʹؕΓ͍͢ • ޯফࣦ
ୈࡾ࣍AIϒʔϜ
Deep Belief Network vs Auto Encoder ہॴղɾաֶश ରࡦ
Auto Encoder Deep Belief Network v3 h2 v1 h1 v2
Visible Hidden Visible Hidden Visible Hidden RBM RBM RBM 4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning ࠾༻ ֶश Input Hidden Output %FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning
දతͳൃද Neural Networkͷ ϒϨʔΫεϧʔͱ ౙͷ࣌ දతͳਓࡐ֫ಘ Google ͕DNN ResearchΛങऩ )JOUPO
Google ͕ Deep MindΛങऩ Baidu ͕Institute of Deep LearningΛઃཱ "OESFX/H Facebook ͕AI Research Lab.Λઃཱ -F$VO SGD (Amari) Neocognitron (Fukushima) Boltzmann Machine (Hinton+) Conv. net (LeCun+) Sparse Coding (Olshausen&Field) 1950 1960 1970 1980 1990 2000 2010 2020 Microsoft ͕MaluubaΛങऩ #FOJHO ୈҰ࣍"*ϒʔϜ ਪɾ୳ࡧ ୈೋ࣍"*ϒʔϜ ࣝදݱ &YQFSU4ZTUFN ୈࡾ࣍"*ϒʔϜ ػցֶश ਂֶश Perceptron (Rosenblatt) Back Propagation (Rumelhart) Deep Learning (Hinton+) Big Data GPU Cloud Computing ઢܗෆՄೳ YPS͕ղ͚ͳ͍ ͍ɺաֶशɺ 47.ਓؾ χϡʔϥϧωοτϫʔΫͷྺ࢙ NN ୈҰͷౙ NN ୈೋͷౙ
ωο τϫʔΫͷΤωϧΪʔ͕࠷খʹͳΔΑ͏ʹঢ়ଶมԽΛ܁Γฦ͢ %FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ
੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning هԱ1 هԱ2 هԱΛࢥ͍ग़͢ ͍ۙ͠σʔλΛ༩͑Δͱ… ը૾ΛهԱͨ͠ωοτϫʔΫ Hopfield Networkͱ هԱΛߦྻܭࢉͰγϛϡϨʔτͯ͠ΈΑ͏ http://www.gaya.jp/spiking_neuron/matrix.htm
%FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO
.BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning Boltzmann Machineͱ ֬Ϟσϧͷಋೖ Kullback LeiblerμΠόʔδΣϯε 2ͭͷۂઢʹ͍ͭͯɺॏͳΒͣʹ૬ҧʢμΠόʔδΣϯεʣ͍ͯ͠ΔྖҬʢࠩʣΛ࠷খԽ͢Δɻ ࣮ࡍͷೖྗʹ ΑΔ֬p ෮ݩ͞Εͨq ࠩͷੵ
%FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO
.BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning ੍͖Boltzmann Machine (RBN)ͱ v3 h2 v1 h1 v2 Visible Hidden
%FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO
.BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning Deep Belief Network (DBN)ͱ Visible Hidden Visible Hidden Visible Hidden RBM RBM RBM pre-training(ڭࢣͳ͠) + fine tuning (ڭࢣ͋Γ)
Auto Encoder Deep Belief Network v3 h2 v1 h1 v2
Visible Hidden Visible Hidden Visible Hidden RBM RBM RBM 4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning ࠾༻ ֶश Input Hidden Output %FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning
4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث
ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning ࠾༻ ֶश Input Hidden Output Auto Encoder (AE)ͱ
4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث
ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning Denoising Auto Encoder (DAE)ͱ ࠾༻ ֶश Input Hidden Output ϊΠζ
4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث
ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning Stacked Auto Encoder (SAE)ͱ
Auto Encoder Deep Belief Network v3 h2 v1 h1 v2
Visible Hidden Visible Hidden Visible Hidden RBM RBM RBM 4UBDLFE "VUP&ODPEFS "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث "VUP&ODPEFS ࣗݾූ߸Խث ଟஈԽ ʴϩόετੑ "VUP&ODPEFS ࣗݾූ߸Խث AE %FOPJTJOH "VUP&ODPEFS DAE SAE pre-training + fine tuning ࠾༻ ֶश Input Hidden Output %FFQ#FMJFG /FUXPSL )PQpFME /FUXPSL #PMU[NBOO .BDIJOF ֬Ϟσϧ ܭࢉྔͷݮ ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ੍͖ #PMU[NBOO .BDIJOF ଟஈԽ ੍͖ #PMU[NBOO .BDIJOF RBM DBN pre-training + fine tuning
γάϞΠυؔɾۂઢਖ਼ؔ ޯফࣦ ඍ ωοτϫʔΫ͕ਂ͍ͱޯ͕ফ͑ͯ͠·͏ɻɻɻ
γάϞΠυؔɾۂઢਖ਼ؔ ඍ ωοτϫʔΫ͕ਂ͍ͱޯ͕ফ͑ͯ͠·͏ɻɻɻ ReLU (Rectified Linear Unit) ൃՐ͍ͯ͠ͳ͍ ൃՐ͍ͯ͠Δ ޯফࣦͳ͠ʹൃՐ͍ͯ͠Δ
ࡉ๔ͷΈΛ௨ͬͯ͢Δɻ ޯফࣦ
• ϛχόονͷೖྗσʔλΛฏۉ0ɺࢄ1ͷσʔλʹม͢Δ • ׆ੑԽؔͷલɺ͘͠ޙʹૠೖ͢Δ͜ͱͰσʔλͷภΓΛݮΒ͢͜ͱ ͕Մೳ • ޮՌ • ֶशΛେ͖͘͢Δ͜ͱ͕ՄೳʢֶशΛૣ͘ਐߦͤ͞Δʣ •
ॳظʹͦΕ΄Ͳґଘ͠ͳ͍ • աֶशΛ੍͢Δ Batch Normalization
• DropOut (Drop Connect) • ΞϯαϯϒϧֶशʹରԠ • ਖ਼ଇԽ • Weight
DecayʢޡࠩؔʹL2ϊϧϜΛՃ͑Δʣ • εύʔεਖ਼ଇԽ • σʔλ֦ுʢϊΠζɺฏߦҠಈɺճసɺ৭ʣ ͦͷଞͷ
ॳظͷܾΊํ
• 0ʹ͢ΔʁˠॏΈ͕ۉҰʹͳͬͯ͠·͍ॏෳͨ͠ʹͳͬͯ͠·͏ • ϥϯμϜͳॳظ͕ඞཁ • ׆ੑԽؔʹɺγάϞΠυؔtanhؔΛ༻͢Δ߹ɺ ʮXavierͷॳظʯ͕ద • ReLUΛ༻͍Δ߹ɺʮHeͷॳظʯ͕ద ॏΈߦྻͷॳظ
• લͷϊʔυͷ͕ɹ ݸͷ߹ɺɹɹΛඪ४ภࠩͱ͢ΔΨε n r 2 n • લͷϊʔυͷ͕ɹ ݸͷ߹ɺɹɹΛඪ४ภࠩͱ͢ΔΨε n r 1 n
• ֤ͷχϡʔϩϯ • όοναΠζ • ֶशɺֶशͷมԽ • Weight decayʢՙॏݮਰʣ •
DropOut • ͳͲ ϋΠύʔύϥϝʔλ NNʹɺॏΈόΠΞεύϥϝʔλͱผʹɺ ਓ͕ઃఆ͖͢ϋΠύʔύϥϝʔλ͕ଘࡏ͢Δɻ ύϥϝʔλܾఆʹଟ͘ͷࢼߦࡨޡ͕͍ɺ Ϟσϧͷੑೳʹେ͖͘Өڹ͢Δɻ • ઐ༻ͷݕূσʔλΛ༻ҙ͢Δ • ܇࿅σʔλςετσʔλΛͬͯੑೳධՁΛ͍͚ͯ͠ͳ͍ • ରεέʔϧͷൣғ͔ΒϥϯμϜʹαϯϓϦϯάͯ͠ධՁ͠ɺ ൣғΛߜΓࠐΜͰ͍͖ɺ࠷ޙʹͻͱͭΛϐοΫΞοϓ͢Δ σʔληοτ ܇࿅σʔλ ςετσʔλ ݕূσʔλ ֶश༻ ֶश݁Ռͷ ධՁ༻ ϋΠύʔύϥϝʔλͷධՁ༻
༧ଌੑೳͷධՁ
܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ςετσʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ςετσʔλ ܇࿅σʔλ
܇࿅σʔλ ܇࿅σʔλ ςετσʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ςετσʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ςετσʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ܇࿅σʔλ ݕূσʔλ ݕূσʔλ ݕূσʔλ ݕূσʔλ ݕূσʔλ ϗʔϧυΞτݕূ Kׂަࠩݕূ (Cross Validation) ੑೳධՁ
TP rate: ཅੑΛཅੑͱஅׂͨ͠߹ FP rate: ӄੑΛཅੑͱஅׂͨ͠߹ = = ROCۂઢͱAUC ROC:Receiver
Operating Characteristic ʢड৴ऀૢ࡞ಛੑʣ AUC:Area under the curve ʢROCۂઢԼ໘ੵʣ Predicted Condition Positive Negative True Condition Positive TP FN (type II error) Negative FP (Type I error) TN True Positive True Negative False Positive False Negative AUC
True Positive True Negative False Positive False Negative ࠶ݱ: ཅੑΛཅੑͱஅׂͨ͠߹
ʢRecallʣ = ద߹: ཅੑͱ༧ଌͨ͠σʔλͷ͏ͪɼ࣮ࡍʹཅੑͰ͋Δͷͷׂ߹ = ʢPrecisionʣ F: Fͷ࠷େ͓͓ΉͶذਫ਼ͱҰக͢Δɻ ௐฏۉɿٯͷฏۉͷٯ http://www004.upp.so-net.ne.jp/s_honma/mean/harmony2.htm Predicted Condition Positive Negative True Condition Positive TP FN (type II error) Negative FP (Type I error) TN
Ԡ༻ྫ • ը૾ೝࣝ (CNN) • ࣗવݴޠॲཧɺԻೝࣝ (RNN) • ը૾ʹର͢ΔΩϟϓγϣϯੜ (CNN
+ RNN) • ڧԽֶश (CNN + Qֶश) • ਂੜϞσϧ (CNN)
ը૾ೝࣝ • Convolutional Neural Network (ΈࠐΈχϡʔϥϧωοτϫʔΫ) • Convolution + Pooling
খ͞ͳը૾ͳΒ͜Ε·Ͱͷશ݁߹NNͰOK Convolution
ฏۉ ࠨӈʹΔΤοδ ্ԼʹΔΤοδ ͖ʹؔͳ͘Τοδ * ϑΟϧλྫ Convolution
None
None
ը૾ྨਓؒΛ͑ͨ ILSVRC = 2010͔Β࢝·ͬͨେنը૾ೝࣝͷڝٕձ 2012ͷILSVRCͰHintonઌੜͷνʔϜ͕Deep LearningͰѹউ 2015ʹILSVRCͷ݁ՌͰਓؒͷೝࣝੑೳΛ͑ͨɻ
ܭࢉྔ • CPUͱGPUͷੑೳͷҧ͍ • ಉ࣌ԋࢉՄೳʢ୯ਫ਼গʣ • CPU(Intel Core i7) :
AVX256bit -> 8ݸ • nVIDIA Pascal GP100 : 114,688ݸ
ࣗવݴޠॲཧɺԻೝࣝ • Recurrent Neural Network (RNN)
ڧԽֶश • CNN + Qֶश + …
Prisma
Prisma σΟʔϓϥʔχϯάΛͬͨΞʔτܥͷ จɺ͍Ζ͍Ζग़͍ͯΔ͕ Ұ൪ͷجૅͱͳΔͷ Gatys et al. 2016 ༻CNNVGG19ʢը૾ྨ༻ʹ܇࿅ࡁΈʣ͔Βશ݁߹Λ ൈ͍ͨͷ
“Image Style Transfer Using Convolutional Neural Networks”
Prisma ίϯςϯπ ελΠϧ http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf
Prisma ଛࣦؔʹίϯςϯπͷଛࣦʴελΠϧͷଛࣦ ࠷దԽʹ௨ৗೖྗ͕ݻఆͰॏΈ͕ߋ৽͞ΕΔ͕ɺٯͰॏΈ͕ݻఆͰೖྗը૾͕ߋ৽͞ΕΔ
Prisma ੜը૾ͷॳظ A:ίϯςϯπ B:ελΠϧ C:ϗϫΠτϊΠζ4ύλʔϯ ͲΕͰ΄ͱΜͲมΘΒͳ͍ͱ͍͏݁
Prisma
FaceApp
FaceApp VAE (Variational Autoencoder) CVAE (Conditional VA) Facial VAE
·ͱΊ • Deep LearningͱҰޱʹݴͬͯɺٕज़༻్༷ʑ • ը૾ೝࣝʢCNNʣ, ࣗવݴޠʢRNNʣ, ਂੜʢVAE, GANʣ,
ڧԽֶशʢDQNʣ, … • ଞͷٕज़ͪΐͬͱͨ͠ͳͲɺΞϓϩʔνํ๏ʹؔͯ͠ϒϧʔΦʔγϟϯͳ • 2014-2015ͷ2ؒͰɺ1500ͷؔ࿈จ • CNN + RNNͷΑ͏ͳɺֆʴԻɺݴ༿ʴֆɺηϯαʔʴจষɺͳͲɺ͜Ε·Ͱ༥߹ Ͱ͖ͳ͔ͬͨσʔλ͕༥߹͢Δ͜ͱͰ৽͍͠ՁΛੜΈग़͢༧ײ
ࢀߟࢿྉ • ॻ੶ • θϩ͔Β࡞ΔDeep Learning ―PythonͰֶͿσΟʔϓϥʔχϯάͷཧͱ࣮ http://amzn.asia/2CTyY4U • ػցֶशͷͨΊͷ֬ͱ౷ܭ
(ػցֶशϓϩϑΣογϣφϧγϦʔζ) http://amzn.asia/5SyEZVV • ΦϯϥΠϯػցֶश (ػցֶशϓϩϑΣογϣφϧγϦʔζ) http://amzn.asia/2kli98b • ΠϥετͰֶͿ σΟʔϓϥʔχϯά (KSใՊֶઐॻ) http://amzn.asia/8Kz11LV • ΠϥετͰֶͿ ػցֶश ࠷খೋ๏ʹΑΔࣝผϞσϧֶशΛத৺ʹ (KSใՊֶઐॻ) http://amzn.asia/6Zlo0pt • ਂֶश (ػցֶशϓϩϑΣογϣφϧγϦʔζ) http://amzn.asia/hZqrQ2w • ChainerʹΑΔ࣮ફਂֶश http://amzn.asia/5xDfvVJ • ࣮σΟʔϓϥʔχϯά http://amzn.asia/7YP7FPh • ͜Ε͔ΒͷڧԽֶश http://amzn.asia/gHUDp81 • ITΤϯδχΞͷͨΊͷػցֶशཧೖ http://amzn.asia/7SgiMwN • ҟৗݕͱมԽݕ (ػցֶशϓϩϑΣογϣφϧγϦʔζ) http://amzn.asia/6RC0jbt • PythonʹΑΔσʔλੳೖ ―NumPyɺpandasΛͬͨσʔλॲཧ http://amzn.asia/4f2ATnL • URL / SlideShare / pdf • ʢଟ͗ͯ͢লུʣ