TensorFlow 是由 Google 所公布的開源機器學習平台，根據 Github 的數據統計，TensorFlow 成為2016年最受關注的十大開源專案之一。此次分享將介紹，如何在 NAS 上整合TensorFlow 及相關 Open source project，以展示幾種相關的資料分析應用。

1. NAS 也可以揀土豆
Open source application

2. Hello!
I am Cage Chung
I am here because I like to
share my experiences.
You can find me at:
QNAP 雲端應用部資深工程師
/ http://kaichu.io

3. Andy

4. https://www.facebook.com/groups/GCPUG.TW/
https://plus.google.com/u/0/communities/116100913832589966421
Google Cloud Platform User Group Taiwan
我們是Google Cloud Platform Taiwan User Group。在Google雲端服務在台灣地區展露頭角之後，
有許多新的服務、新的知識、新的創意，歡迎大家一起分享，一起了解 Google雲端服務...
GCPUG透過網際網路串聯喜好 Google Cloud的使用者，分享與交流使用 GCP的點滴鑑驗。如果您
是Google Cloud Platform的初學者，您應該來聽聽前輩們的使用經驗；如果您是 Google Cloud
Platform的Expert，您應該來分享一下寶貴的經驗，並與更多高手互相交流；如果您還沒開始用
Google Cloud Platform，那麼您應該馬上來聽聽我們是怎麼使用 Google Cloud的！

5. “
Lessons learned building a
classifier for NAS. Try to get a big
picture, get some useful
keywords
I cannot explain everything, you cannot get every details

6. General user
○ Videos
○ Music
○ Movies
○ Photos
○ Files
○ Games
Photographer
○ Photos (jpeg, RAW)
Musicians
○ Music files (mp3)
○ Videos
NAS | usage

7. Our scenario is easy
Portrait
landscape
wildlife
sports
folk
Retrain Inception classifier
Photographer
Product
NAS

8. outline
◎ Machine learning
◎ Deep learning
○ Neural Network
○ Convolutional neural network
◎ Building a classifier for NAS
◎ Study information

9. 1.
Machine Learning
Let’s start with the first set of slides

10. Supervised Learning
[image](http://www.safebee.com/family/5-healthy-hygiene-habits-your-child-needs-learn)

11. Supervised Learning workflow
Raw Data
Labes
Feature
Extraction
Train
the
Model
Eval
Model
Model
Feature
Extraction Predict
New Data
Model
Labels
Training
Predicting

12. Supervised Learning workflow
Raw Data
Labes
Feature
Extraction
Train
the
Model
Eval
Model
Model
Feature
Extraction Predict
New Data
Model
Labels
Training
Predicting
Ripe Raw
Color/Shape
etc...
Ripe
Raw
Ripe
Raw

13. Unsupervised Learning
[image](http://thoughtcatalog.com/nikolao-montaya/2014/06/how-to-be-cool-in-high-school/)

14. Ripe
Ripe
Raw
Raw
Raw Data Automated Clusters
Learning
Algorithm

15. [image](http://www.artbooms.com/blog/primo-toy-cubetto-robot-legno-programmazione-prescolare)
Semi-Supervised Learning

16. Reinforcement Learning
[image](https://www.shutterstock.com/search/horse%20carrot)

17. Reinforcement Learning (RL)
[AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree
…](http://www.slideshare.net/KarelHa1/alphago-mastering-the-game-of-go-with-deep-neural-networks-and-tree-search)

18. Mario - Machine Learning for Video Games 1
[NEATEvolve.lua - Pastebin.com](http://pastebin.com/ZZmSNaHX)

19. Mario - Machine Learning for Video Games 2
[NEATEvolve.lua - Pastebin.com](http://pastebin.com/ZZmSNaHX)

20. Smart
programs can
learn from
examples
[image](https://www.engadget.com/2016/07/12/machine-learning-ai/)

21. 2.
Deep Learning
Let’s start with the second set of
slides

22. Deep learning
[Deep Learning - Convolutional Neural Networks](http://www.slideshare.net/perone/deep-learning-convolutional-neural-networks)

23. Deep learning
[自然言語処理のためのDeep Learning](http://www.slideshare.net/yutakikuchi927/deep-learning-26647407)

24. one
architecture
to rule them
all
[image](http://www.consultparagon.com/blog/what-is-leadership-digital-transformation)

25. 2.1.
Neural Network

26. Neural Network Architecture
[Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/chap1.html)

27. First Example: MNIST handwritten digits

32. a few seconds
60,000 images
MNIST resource
Intel HD Graphics
CPU build-in graphics
Piece of cake ...
90% AccuracyFeeling good? But Google said it’s shameful ...

33. 2.2.
Convolutional
Neural Network
The Google “Inception” deep neural network architecture. Source: Christian Szegedy et. al. Going deeper with convolutions. CVPR 2015.

34. WHAT IS CONVOLUTION?
Source: http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
Convolution with 3×3 Filter.

35. WHAT IS CONVOLUTION?
Source: http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution

36. Convolutional Neural Network, CNN
[Understanding Convolutional Neural Networks for NLP –
WildML](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)

37. Convolutional Neural Network, CNN

38. Convolutional Neural Network, CNN

39. becomes
Peeking inside Convnets
[Peeking inside Convnets | Audun M Øygard](https://auduno.github.io/2016/06/18/peeking-inside-convnets/)

40. a few hours
60,000 images
MNIST resource
Intel i5 2.5GhzCPU build-in graphics
Not as easy as we think ...
99% AccuracyMy Goodness ...

41. Deep learning is
a kind of neural
network, and a
neural network
is a kind of
machine
learning.
[image](https://www.hpcwire.com/2015/04/30/machine-learning-guru-sees-future-in-multi-gpu
-clusters/)

42. 3.
Building a
classifier for NAS
Let’s start with the third set of
slides

43. Outline | Building a classifier for NAS
◎ How to train ?
○ Train from scratch
○ Re-train from inception modal
◎ Photo classifier Case study
○ Flickr Photos/ Google search
○ Fuji photography society monthly competition
○ Imagenet sources
◎ Image type classifier (photos、scan document、business card)
◎ Demo
◎ Next Steps
○ video post-processing?
○ Musicians?
◎ Search

44. 2 weeksSpend a lot of time
14,197,122 images
ImageNet resource
8 NVIDIA Tesla K40s
High-end professional graphics
[Research Blog: Train your own image classifier with Inception in
TensorFlow](https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html)

45. Retrain Inception's Final Layer for New Categories
◎ reuse Imagenet
pre-trained model
extract features to
predict new tasks?
◎ Transfer learning
◎ General visual features
DeCAF: A Deep Convolutional Activation
Feature for Generic Visual Recognition

46. Retrain Inception's Final Layer for New Categories Cont.
◎ Installing and Running the TensorFlow Docker Image
(gcr.io/tensorflow/tensorflow:latest-devel)
◎ Preparing target images
○ Quantity > 100
○ representation
◎ Use Python to train your own image classifier
○ Distortions (--random_crop, --random_scale ects.)
○ Hyper-parameters (--learning_rate ects.)
◎ Classify images with your trained classifier
[TensorFlow For Poets](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html?index=..%2F..%2Findex#0)

47. Case study: Types of Photography via Flickr
Portrait landscape wildlife
folk sport art

48. 68.8 %Final test accuracy
23,666 images
Flickr photos & Google search
Art x 4545 , Folk x 4782, Landscape x 3379
Portrait x 3706, Sport x 4967, Wildlife x 2887
5000 times
Retain iterator

49. 89.0 %Final test accuracy
10,349 images
Flickr photos
Folk x 2083, Landscape x 3497
Portrait x 3215, Wildlife x 1554
4000 times
Retain iterator

50. 97.0 %Final test accuracy
13,685 images
Imagenet 11 categories
Agaric/bolete/buckeye, horse chestnut, conker/coral fungus/ear,
spike, capitulum/earthstar/gyromitra/hen-of-the-woods, hen of the
woods, Polyporus frondosus, Grifola frondosa/peanut/stinkhorn,
carrion fungus/toilet tissue, toilet paper, bathroom tissue/
4000 times
Retain iterator

51. Case study: Types of Photography via Fuji photography
society (富士生活攝影協會月賽)
Portrait landscape wildlife
folk sport conceptual

52. Case study: Types of Photography via Fuji photography
society (富士生活攝影協會月賽)
competitor
銅牌
佳作
入選乙
入選甲
優選
金牌 銀牌
碩學會士/博學會士

53. 45.8 %Final test accuracy
424 images
Fuji photography society monthly competition photos
佳作 x 32, 入選乙 x 137, 入選甲 x 255, 優選 x 2, 未入
選?
4000 times
Retain iterator

54. Demo
Custom Photo classifier

55. Image type classifier
Invoices Business cards Scan documents

56. Next Steps | video
[Large-scale Video Classification with Convolutional Neural Networks (CVPR 2014)](http://cs.stanford.edu/people/karpathy/deepvideo/)

57. Next Steps | music
[Large-scale Video Classification with Convolutional Neural Networks (CVPR 2014)](http://cs.stanford.edu/people/karpathy/deepvideo/)
[Deep Learning - Convolutional Neural Networks](http://www.slideshare.net/perone/deep-learning-convolutional-neural-networks)

58. Search

59. Open framework, models and worked examples for deep
learning | Caffe
Caffe offers the
○ model definitions
○ optimization settings
○ pre-trained weights
so you can start right away.
The BVLC models are licensed for
unrestricted use.
The community shares models in our
Model Zoo.
[DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe - Google
Slides](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_48)

60. Brewing by the Numbers … | Caffe
Speed with Krizhevsky's 2012 model:
○ 2 ms/image on K40 GPU
○ <1 ms inference with Caffe + cuDNN v4 on Titan X
○ 72 million images/day with batched IO
○ 8-core CPU: ~20 ms/image Intel optimization in progress
9k lines of C++ code (20k with tests)
[DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe - Google
Slides](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_48)

61. 4.
Study information
Let’s start with the fourth set of
slides

62. ◎ [Deep Learning |
Udacity](https://www.udacity.com/course/deep-learning--ud730)
◎ [Research Blog: Train your own image classifier with Inception in
TensorFlow](https://research.googleblog.com/2016/03/train-your-own-image-cla
ssifier-with.html)
◎ [jtoy/awesome-tensorflow: TensorFlow - A curated list of dedicated resources
http://tensorflow.org](https://github.com/jtoy/awesome-tensorflow)
◎ [Deep Learning - Convolutional Neural
Networks](http://www.slideshare.net/perone/deep-learning-convolutional-neura
l-networks)
◎ [Neural networks and deep
learning](http://neuralnetworksanddeeplearning.com/chap1.html)
◎ [Multiple Component Learning](http://valse.mmcheng.net/ftp/20150312/dsn.pdf)
◎ [Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7 -
YouTube](https://www.youtube.com/watch?v=Gj0iyo265bc)
Study information

63. ◎ [DIGITS/GettingStarted.md at master ·
NVIDIA/DIGITS](https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStar
ted.md)
◎ [How to Retrain Inception's Final Layer for New
Categories](https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining
/index.html)
◎ [TensorFlow For
Poets](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/i
ndex.html?index=..%2F..%2Findex#0)
◎ [DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe - Google
Slides](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71
UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_48)
◎ [Caffe | Deep Learning Framework](http://caffe.berkeleyvision.org/)
◎ [Understanding Convolutional Neural Networks for NLP –
WildML](http://www.wildml.com/2015/11/understanding-convolutional-neural-n
etworks-for-nlp/)
Study information

64. Thanks!
Any questions?
You can find me at:
http://kaichu.io
cage.chung@gmail.com