Goによる勾配降下法 - 理論と実践 - / gradient-descent-in-golang

Transcript

1. ࡾ୐༔հ
   
   (.0
   1&1"#0
   JOD
   
   ϓϩάϥϚͷͨΊͷ਺ֶษڧձ!෱Ԭ
    (PʹΑΔޯ഑߱Լ๏
   
   ཧ࿦ͱ࣮ફ
   

2. ϓϦϯγύϧΤϯδχΞ ࡾ୐༔հ
   
   !NPOPDISPNFHBOF NJOOFࣄۀ෦ IUUQCMPHNPOPDISPNFHBOFDPN

3. ໨࣍ wޯ഑߱Լ๏ͱ͸
   w࠷ٸ߱Լ๏
   w֬཰తޯ഑߱Լ๏
   wޯ഑߱Լ๏ͷ࠷దԽ
   w·ͱΊ

4. ޯ഑߱Լ๏ͱ͸

5. ޯ഑߱Լ๏ͱ͸ wػցֶशʹ͓͍ͯϞσϧʹରֶͯ͠शΛਐΊΔͨΊͷख๏ͷͻͱͭɻ
   wτϨʔχϯάର৅ͷσʔλʹରͯ͠Ϟσϧͱͷޡ͕ࠩ࠷খʹͳΔΑ͏ʹϞσϧ ಺ͷύϥϝλΛߋ৽͍ͯ͘͜͠ͱɻ
   wύϥϝλߋ৽͸ɺޡࠩΛఆٛͨؔ͠਺Λඍ෼ͯ͠࠷খʹ͚ۙͮΔૢ࡞Λ܁Γฦ ͢͜ͱͰߦ͏ɻ

6. ͳΔ΄Ͳʁʁ

7. ྫ͑͹ɺ͜͜ʹ ޡࠩΛఆٛͨؔ͠਺ͱͯ͠ ͕͋Δͱ͢Δɻ
   ͜ΕΛ࠷খԽ͢Δ
   Y
   ͷ஋͕ٻ·Δ
   
   ޡ͕ࠩ࠷খʹͳΔͱߟ͑Δɻ f ( x ) =

   ( x 1)2

8. ͭ·Γɺ ͻͨ͢Βඍ෼ͯ͠܏͖͕ʹͳΔͱ͜ ΖΛ୳͢ɻ

9. ͋ͯͣͬΆ͏ʁ ͦΕͩͱऴΘΒͳ͍ͷͰɺٻΊͨ܏͖ Λݩʹ
   Y
   Λ૿΍͠ʢݮΒ͠ʣͯΛ܁ Γฦ͢ x := x d dx f

   ( x ) ಋؔ਺ͷූ߸͕ෛͰ͋Ε͹ɺYΛ૿΍͠ɺ
   ಋؔ਺ͷූ߸͕ਖ਼Ͱ͋Ε͹ɺYΛݮΒ͢ɻ

10. ֶश཰ ֶश཰Б
    ͸
    Y
    ͷߋ৽౓߹͍Λௐ੔͢ Δɻ x := x ⌘ d dx f

    ( x ) େ͖͗͢Δͱ
    Y
    ͷҠಈྔ͕૿͑ͯɺऩ ଋ͠ͳ͍৔߹΍ൃࢄͯ͠͠·͏৔߹͕ ͋Δɻ
    খ͗͢͞Δͱ
    YͷҠಈྔ͕ݮΓɺ܁Γ ฦ͠ճ਺͕૿͑ΔՄೳੑ͕͋Δɻ

11. ໨తؔ਺ wτϨʔχϯάର৅ͷσʔλʹର͢ΔϞσϧͱͷޡࠩΛఆٛͨ͠΋ͷ ٻΊΔύϥϝλΛВͱஔ͘ E ( ✓ ) = 1 2

    n X i=1 ( yi f✓( xi))2 τϨʔχϯάσʔλ Z ͱ͋Δ࣌఺ͷύϥϝλВΛ ࢖ͬͨϞσϧ͔Βࢉग़͞Εͨ༧ଌ஋ͷࠩʢޡࠩʣ શͯͷτϨʔχϯάσʔλʹର͢Δޡࠩͷೋ৐࿨

12. ໨తؔ਺ w͋ͱ͸ɺޡࠩΛఆٛͨؔ͠਺Ͱ͋Δ໨తؔ਺Λύϥϝλʹରͯ͠ඍ෼ͯ͠ޡࠩ Λ࠷খʹ͍͚ͯ͠͹Α͍ ˠ
    ࠷ٸ߱Լ๏

13. ࠷ٸ߱Լ๏ - gradient descent, GD -

14. ۩ମྫ

15. ଟ߲ࣜճؼ τϨʔχϯάηοτ
    ਖ਼ݭؔ਺Λσʔλੜ੒ݩͱͯ͠ඪ४ภ ࠩͷཚ਺ΛՃ͑ͨ΋ͷ Ϟσϧ
    ࣍ͷଟ߲ࣜΛ༻͍ͯ༧ଌ f✓( x ) =

    ✓0 + ✓1x + ✓2x 2 + ✓3x 3

16. ଟ߲ࣜճؼ ໨తؔ਺ E ( ✓ ) = 1 2 n

    X i=1 ( yi f✓( xi))2 f✓( x ) = ✓0 + ✓1x + ✓2x 2 + ✓3x 3 ΛϞσϧ ͷύϥϝλͰ͋Δ
    В   
    ʹରͯ͠ ภඍ෼Λߦͬͨಋؔ਺Λ༻͍ͯύϥϝ λͷߋ৽Λߦ͏

17. ଟ߲ࣜճؼ ໨తؔ਺ E ( ✓ ) = 1 2 n

    X i=1 ( yi f✓( xi))2 f✓( x ) = ✓0 + ✓1x + ✓2x 2 + ✓3x 3 ΛϞσϧ ͷύϥϝλͰ͋Δ
    В   
    ʹରͯ͠ ภඍ෼Λߦͬͨಋؔ਺Λ༻͍ͯύϥϝ λͷߋ৽Λߦ͏ ✓0 := ✓0 ⌘ n X i=1 ( f✓( xi) yi) ✓1 := ✓1 ⌘ n X i=1 ( f✓( xi) yi) xi ✓2 := ✓2 ⌘ n X i=1 ( f✓( xi) yi) x 2 i ✓3 := ✓3 ⌘ n X i=1 ( f✓( xi) yi) x 3 i ύϥϝλߋ৽ࣜ В@
    ʹ͍ͭͯภඍ෼ В@
    ʹ͍ͭͯภඍ෼ В@
    ʹ͍ͭͯภඍ෼ В@
    ʹ͍ͭͯภඍ෼

18. ࠷ٸ߱Լ๏ʹΑΔଟ߲ࣜճؼ
    
    (PMBOH
     // fθ(x) Ϟσϧ func PredictionFunction(x float64, thetas []float64) float64

    { result := 0.0 for i, theta := range thetas { result += theta * math.Pow(x, float64(i)) } return result } // E(θ) ໨తؔ਺ func ObjectiveFunction(trainings DataSet, thetas []float64) float64 { result := 0.0 for _, training := range trainings { result += math.Pow((training.Y - PredictionFunction(training.X, thetas)), 2) } return result / 2.0 }

19. ࠷ٸ߱Լ๏ʹΑΔଟ߲ࣜճؼ
    
    (PMBOH
     // ύϥϝλ͝ͱͷޯ഑ func gradient(dataset DataSet, thetas []float64, index int,

    batchSize int) float64 { result := 0.0 for _, data := range dataset[0:batchSize] { result += ((PredictionFunction(data.X, thetas) - data.Y) * math.Pow(data.X, float64(index))) } return result } ✓0 := ✓0 ⌘ n X i=1 ( f✓( xi) yi) ✓1 := ✓1 ⌘ n X i=1 ( f✓( xi) yi) xi ✓2 := ✓2 ⌘ n X i=1 ( f✓( xi) yi) x 2 i ✓3 := ✓3 ⌘ n X i=1 ( f✓( xi) yi) x 3 i

20. ࠷ٸ߱Լ๏ʹΑΔଟ߲ࣜճؼ
    
    (PMBOH
     // learning (update parameters) for i := 0; i

    < opt.Epoch; i++ { // update parameter by gradient descent org_thetas := make([]float64, cap(thetas)) copy(org_thetas, thetas) shuffled := dataset.Shuffle() for j, _ := range thetas { // compute gradient gradient := gradient(shuffled, org_thetas, j, batchSize) // update parameter thetas[j] = org_thetas[j] - (opt.LearingRate * gradient) } }

21. ࠷ٸ߱Լ๏ʹΑΔଟ߲ࣜճؼ

22. ֬཰తޯ഑߱Լ๏ - stochastic gradient descent, SGD -

23. ࠷ٸ߱Լ๏ͷ՝୊

24. ࠷ٸ߱Լ๏ͷ՝୊ wύϥϝλߋ৽ຖͷޡࠩͷܭࢉʹશτϨʔχϯάηοτͷ߹ܭ͕ඞཁʹͳΔ
    wˠτϨʔχϯάηοτ͕ͱͯ΋େ͖͍৔߹ʹܭࢉྔ͕๲େʹͳͬͯ͠·͏ E ( ✓ ) = 1 2

    n X i=1 ( yi f✓( xi))2 wશτϨʔχϯάηοτΛ࢖͏ͨΊ࣮֬ʹޯ഑ΛԼͬͯ͠·͏
    wˠہॴղʹั·ΔՄೳੑ͕ߴ͍

25. ֬཰తޯ഑߱Լ๏ - stochastic gradient descent, SGD -

26. 4(%ʹΑΔଟ߲ࣜճؼ ύϥϝλߋ৽ࣜ
    ޡࠩೋ৐࿨Λ࢖ΘͣɺϥϯμϜʹબ୒ ͨ͠σʔλΛ༻͍ͯύϥϝλߋ৽Λߦ ͏ ✓0 := ✓0 ⌘ 1

    X i=1 ( f✓( xi) yi) ✓1 := ✓1 ⌘ 1 X i=1 ( f✓( xi) yi) xi ✓2 := ✓2 ⌘ 1 X i=1 ( f✓( xi) yi) x 2 i ✓3 := ✓3 ⌘ 1 X i=1 ( f✓( xi) yi) x 3 i J
    ͔Β
    
    ·Ͱɻͻͱ͚ͭͩͷ࿨ ൪໨ͷτϨʔχϯάηοτݻఆͰ ֶश͢ΔͷͰ͸ͳ͘ɺຖճγϟοϑ ϧ্ͨ͠Ͱͷઌ಄σʔλΛ࢖ͬͯύ ϥϝλߋ৽Λߦ͏

27. ֬཰తޯ഑߱Լ๏ʹΑΔଟ߲ࣜճؼ
    
    (PMBOH
     // GD: batchSize=len(dataset), SGD: batchSize=1 batchSize := len(dataset) if

    opt.Algorithm == "sgd" { if opt.BatchSize == -1 { batchSize = 1 } }

28. ֬཰తޯ഑߱Լ๏
    
    ֶश཰ʹΑΔऩଋਪҠ
    

29. ϛχόονޯ഑߱Լ๏ - mini-batch gradient descent, mini-batch SGD -

30. NJOJCBUDI
    4(% 
    㱡
    #
    
    MFO USBJOHJOH
    TFU
    ͱͳΔ όοναΠζΛఆΊͯύϥϝλߋ৽Λ ߦ͏͜ͱͰ࠷ٸ߱Լ๏ͱ֬཰తޯ഑߱ Լ๏ͷ͍͍ͱ͜औΓΛૂ͏ɻ
    ֬཰తޯ഑߱Լ๏͸
    #ͷಛघܕͱ ݴ͑Δɻ J
    ͔Β
    ϛχόοναΠζ·Ͱͷ࿨

    ຖճγϟοϑϧ্ͨ͠Ͱઌ಄͔Βϛ χόοναΠζ·ͰͷσʔλΛ࢖ͬ ͯύϥϝλߋ৽Λߦ͏ ✓0 := ✓0 ⌘ B X i=1 ( f✓( xi) yi) ✓1 := ✓1 ⌘ B X i=1 ( f✓( xi) yi) xi ✓2 := ✓2 ⌘ B X i=1 ( f✓( xi) yi) x 2 i ✓3 := ✓3 ⌘ B X i=1 ( f✓( xi) yi) x 3 i

31. ޯ഑߱Լ๏ͷ࠷దԽ - optimization -

32. .PNFOUVN

33. ޯ഑߱Լ๏ͷऩଋΛૣΊΔ

34. .PNFOUVN ύϥϝλߋ৽ʹϞϝϯλϜʢ׳ੑʣͷ ߟ͑ํΛऔΓೖΕΔ͜ͱͰऩଋΛૣΊ Δɻ ϞϝϯλϜͷͳ͍4(% ϞϝϯλϜͷ͋Δ4(% vk = vk 1

    + ⌘rE(✓) ✓k = ✓k 1 vk લճ·Ͱͷޯ഑ҠಈΛ׳ੑͱͯ͠ྦྷੵ ͢Δɻͭ·Γಉ͡ํ޲΁ͷҠಈͰ͋Ε ͹׳ੑ͸૿Ճ͠ɺํ޲Λม͑ΔҠಈͰ ͋Ε͹ݱ৅ͤ͞Δɻ ޯ഑ ϞϝϯλϜͷྦྷੵ .PNFOUVN
    BOE
    -FBSOJOH
    3BUF
    "EBQUBUJPO
    IUUQTXXXXJMMBNFUUFFEVdHPSSDMBTTFTDTNPNSBUFIUNM

35. .PNFOUVNʹΑΔ࠷దԽ
    
    (PMBOH
     for j, _ := range thetas { // compute

    gradient gradient := gradient(shuffled, org_thetas, j, batchSize) // Use momentum if momentum option is passed velocities[j] = opt.Momentum*velocities[j] -(opt.LearingRate * gradient) // update parameter thetas[j] = org_thetas[j] + velocities[j] } vk = vk 1 + ⌘rE(✓) ✓k = ✓k 1 vk

36. .PNFOUVNʹΑΔ࠷దԽ
    
    ֶश཰ʹΑΔऩଋਪҠ
    

37. "EB(SBE

38. ֶश཰ΛࣗಈͰௐ੔͢Δ

39. "EB(SBE Ϟσϧͷֶशͷࡍʹɺֶश཰ΛࣗಈͰ ௐ੔͢Δख๏ͷͻͱͭɻ Gk = Gk 1 + (rE(✓k 1))2

    ✓k = ✓k 1 ⌘ p Gk 1 + ✏ rE(✓k 1) ॳظֶश཰БΛޯ഑ͷઈର஋ͷྦྷੵͰ ׂͬͨ΋ͷΛֶश཰ͱͯ͠࢖͏ ϝϦοτ
    ֤ύϥϝλ͝ͱʹֶश཰͕ௐ੔Ͱ͖ Δɻ
    มԽͷগͳ͍ύϥϝλʹରͯ͠͸େ ֶ͖͘श͠ɺมԽ͕ଟ͍ύϥϝλʹ ରͯ͠͸গֶͮͭ͠श͍ͯ͘͠ σϝϦοτ
    ޯ഑ͷྦྷੵΛ෼฼ͱ͢ΔҎ্ɺֶश ͕ਐΉͱֶश཰͸ඇৗʹখ͘͞ͳͬ ͯ͠·͏
    ˠॳظֶश཰Λେ͖Ίʹઃఆ͢Δ

40. "EB(SBEʹΑΔ࠷దԽ
    
    (PMBOH
     for j, _ := range thetas { ~~~~ //

    optimize by AdaGrad gradients[j] += math.Pow(gradient, 2) learningRate := opt.LearingRate / (math.Sqrt(gradients[j] + opt.Epsilon)) update = -(learningRate * gradient) ~~~~ } Gk = Gk 1 + (rE(✓k 1))2 ✓k = ✓k 1 ⌘ p Gk 1 + ✏ rE(✓k 1)

41. "EB(SBEʹΑΔ࠷దԽ
    
    ֶश཰ʹΑΔऩଋਪҠ
    

42. "EB%FMUB

43. ֶश཰ΛࣗಈͰௐ੔͢Δ

44. "EB%FMUB Ϟσϧͷֶशͷࡍʹɺֶश཰ΛࣗಈͰ ௐ੔͢Δख๏ͷͻͱͭɻ ֶश཰ͷ୯ௐݮগΛճආ
    ୯७ʹޯ഑ͷ߹ܭΛ༻͍ΔͷͰ͸ͳ͘ɺޯ഑ ΛݮਰฏۉԽ͢Δ͜ͱͰ௚ۙͷޯ഑ʹΑΔֶ श཰ͷࢉग़Λߦ͏ɻ
    ॳظֶश཰ͷઃఆ͕ෆཁ
    ·ͨॳظֶश཰Λύϥϝλߋ৽஋Λݮਰฏۉ Խͨ͠΋ͷʹஔ͖׵͑Δ
    

    E ⇥ g2 ⇤ t = E ⇥ g2 ⇤ t 1 + (1 )g2 t ✓t = q E [ ✓2]t 1 + ✏ p E [g2]t + ✏ gt E ⇥ ✓2 ⇤ t = E ⇥ ✓2 ⇤ t 1 + (1 ) ✓2 t ✓t+1 = ✓t + ✓t

45. "EB%FMUB Ϟσϧͷֶशͷࡍʹɺֶश཰ΛࣗಈͰ ௐ੔͢Δख๏ͷͻͱͭɻ E ⇥ g2 ⇤ t = E

    ⇥ g2 ⇤ t 1 + (1 )g2 t ✓t = q E [ ✓2]t 1 + ✏ p E [g2]t + ✏ gt E ⇥ ✓2 ⇤ t = E ⇥ ✓2 ⇤ t 1 + (1 ) ✓2 t ✓t+1 = ✓t + ✓t ޯ഑ΛݮਰฏۉԽͯ͠஝ੵ ઌఔٻΊͨ஋Λ࢖ͬͯύϥϝ λߋ৽஋ͷݮਰฏۉ஝ੵ ௚ۙͷޯ഑ͱύϥϝλߋ৽஋ ͔Βֶश཰ΛٻΊͯ৽͍͠ύ ϥϝλߋ৽஋ΛಘΔ ύϥϝλߋ৽

46. "EB%FMUBʹΑΔ࠷దԽ
    
    (PMBOH
     for j, _ := range thetas { ~~~~ //

    optimize by AdaDelta gradients[j] = (opt.DecayRate * gradients[j]) + (1.0- opt.DecayRate)*math.Pow(gradient, 2) update = -(math.Sqrt(updates[j]+opt.Epsilon) / math.Sqrt(gradients[j] +opt.Epsilon)) * gradient updates[j] = (opt.DecayRate * updates[j]) + (1.0- opt.DecayRate)*math.Pow(update, 2) ~~~~ } E ⇥ g2 ⇤ t = E ⇥ g2 ⇤ t 1 + (1 )g2 t ✓t = q E [ ✓2]t 1 + ✏ p E [g2]t + ✏ gt E ⇥ ✓2 ⇤ t = E ⇥ ✓2 ⇤ t 1 + (1 ) ✓2 t ✓t+1 = ✓t + ✓t

47. "EB%FMUBʹΑΔ࠷దԽ
    
    ݮਰ཰ʹΑΔऩଋਪҠ
    

48. "EB(SBEͱ"EB%FMUBͷֶश཰ͷਪҠ

49. ൺֱ

50. ֤ޯ഑߱Լ๏ͱ࠷దԽʹΑΔऩଋਪҠͷൺֱ

51. ·ͱΊ

52. ·ͱΊ wػցֶशͰ͸ޯ഑߱Լ๏ʹΑͬͯޡࠩΛ࠷খԽ͢Δ͜ͱͰϞσϧͷֶशΛਐΊ Δ
    wޯ഑߱Լ๏ɺ࠷దԽͷछྨ͸༷ʑ͕ͩɺτϨʔχϯάηοτʹదͨ͠΋ͷΛબ ͿͨΊʹ͸ɺΞϧΰϦζϜͷબ୒ɺϋΠύʔύϥϝʔλʔͷௐ੔ͱ͍ͬͨࢼߦ ࡨޡ͕ݱ࣌఺Ͱ͸ඞཁ
    w࠷৽ͷख๏͕ৗʹΑ͍ͱ͸ݶΒͳ͍ʜ
    wϋΠύʔύϥϝʔλʔ͸ͳ͘ͳΒͳ͍ʜ
    wࣗ෼Ͱ࣮૷͢Δͱཧղ͕ਂ·ͬͯ٢ʂ

53. $PEF

54. $PEF $ go run cmd/gradient_descent/main.go \ -eta 0.075 \ -m

    3 \ -epoch 40000 \ -algorithm sgd \ -momentum 0.9 w(PݴޠʹΑΔޯ഑߱Լ๏ͷαϯϓϧ࣮૷ΛҎԼʹஔ͍͍ͯ·͢
    wIUUQTHJUIVCDPNNPOPDISPNFHBOFHSBEJFOU@EFTDFOU 6TBHF

55. ͓ΘΓ

56. ܅΋ϖύϘͰಇ͔ͳ͍͔ʁ ࠷৽ͷ࠾༻৘ใΛνΣοΫˠ !QC@SFDSVJU