2017 파이썬 연말세미나

2. 우리도 4차
산업하자
.

3. 뭘 해야 하나 …

4. 뭘 해야 하나 …
뭘 할줄 아나 …

5. 공개 된 자료 (2017.1월 경)
• CNN (Inception V3, LeNet, VGG …)
• RNN (LSTM)
• GAN

6. 공개 된 자료 (2017.1월 경)
• CNN (Inception V3, LeNet, VGG …)
• RNN (LSTM)
• GAN

7. 공개 된 자료 (2017.1월 경)
• CNN (Inception V3, LeNet, VGG …)
• RNN (LSTM)
• GAN

8. 공개 된 자료 (2017.1월 경)
• CNN (Inception V3, LeNet, VGG …)
• RNN (LSTM)
• GAN
!

9. •
•

10. classification

11. •
•

12. •
•

13. ?
x o
= visible
=
x
x
30%

14. CNN .
Multi layer perceptron
tensorflow .

16. Don’t re-invent wheel
• CNN
• 4 (conv->conv->fc->fc)
• CNN
• 96% (Inception V3, VGG …)
• Inception V3 48

17. Don’t re-invent wheel
• CNN 4
• 70%
• class a, b 20%
•
• Inception V3

19. • “transfer learning
”
• “ 3
"
• “VGG net
”
• “ ”
.

20. Transfer Learning

21. Transfer Learning

22. Transfer Learning
https://www.tensorflow.org/tutorials/image_retraining

23. Transfer Learning
https://www.tensorflow.org/tutorials/image_retraining

24. Transfer Learning
softmax
layer
https://www.tensorflow.org/tutorials/image_retraining

25. Transfer Learning
1. tensorflow
2.
3. bazel-bin/tensorflow/examples/image_retraining/retrain
--image_dir ~/flower_photos
4.

26. – .
“ 3 ”

27. – .
“ 3 ”

28. • 80%
• Hyper-parameter tuning 85%
• learning rate
• learning epoch
• batch size
• random noise

31. •
• 5 / /a/b
•
•

33. • 92%
• 2
• 40 (cpu)

34. • 92%
• 2
• 40 (cpu)

35. regression

36. • 1, 2, 3, 4, 5
• 7
• ( )
• fun predict(image): score

37. tensorflow
transfer learning

39. 1
• 1, 2, 3, 4, 5 Inception V3
• noise
•
• ->

40. • 1, 2, 3, 4, 5 Inception V3
• noise
•
• ->
1 FAIL

41. Lesson from 1
Outlier
overfitting
classification line tight

42. 2
• Neural Net old fashioned
• Inception V3 softmax FC
• bottleneck
• 2048 PCA 700
• 90%
• outlier robust huber normalization
• classification SVM

43. 2

44. 2
• ( , )
• 5
• 2
• 5 40%
• 2 = 2

45. 2
• Neural Net old fashioned
• Inception V3 softmax FC
• bottleneck
• 2048 PCA 700
• 90%
• outlier robust huber normalization
• classification SVM
FAIL

46. Lesson from 2
1, 5 2, 3, 4

47. 3
Regression
Huber norm, PCA
regressor sklearn Robust regression

48. 3

49. 3
Regression
Huber norm, PCA
regressor sklearn Robust regression
FAIL

50. Lesson from 3
• under-fitting
• 3 (regression toward the mean)
• 700 fitting
• (train accuracy) 1.8

51. 4
Regression
DNN regression
loss function huber loss function
class imbalance 20
[1, 2), [2, 3), [3, 4), [4, 5) 1500
under-fitting

52. 4

53. AWS
AMI

54. AWS
Spot
82%

55. AWS
ap-northeast-2c
aws ami spot instance
-> instance snapshot

56. 4

57. 4
• Regression
• DNN regression
• loss function huber loss function
• under-fitting
FAIL

58. Lesson from 4
• 0.4
• over-fitting
•

59. 5
augmentation
+
over-fitting batch-norm

60. 5

61. 5

62. 5
0.7
CNN

63. 5
0.7
CNN

65. 5

66. “ raw data”

67. “ raw data”
“ ”

68. “ raw data”
“ ”
“ ”

69. “ raw data”
“ ”
“ ”
“ ”

70. “ raw data”
“ ”
“ ”
“ ”
“ ”