This document discusses using TensorFlow for Android development. It provides an overview of machine learning concepts like supervised and unsupervised learning, classification, and regression. It then discusses TensorFlow and how it can be used on mobile, including building image classifiers with MobileNets and optimizing models for mobile through quantization. It also covers adding a trained model to an Android app by loading the model graph and feeding input data to perform inference.

1. TENSORFLOWFOR ANDROID DEVELOPERS
JOE BIRCH - @HITHEREJOE - ANDROID LEAD @BUFFER - GDE @ANDROID

2. MACHINE LEARNING 101
Get data
Clean, prep &
manipulate data
Train ModelTest data
Improve

3. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

4. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

5. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

6. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

7. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

8. MACHINE LEARNING 101
Unsupervised
Learning
Supervised
Learning
Clustering
Classification
Regression

9. MACHINE LEARNING AND MOBILE

10. MACHINE LEARNING AND MOBILE

11. TENSORFLOW

12. TENSORFLOW

13. COMPUTATION GRAPHS
C
D FA
B E

14. NEURAL NETWORKS
Some Image Result

15. NEURAL NETWORKS
Some Image Result

16. NEURAL NETWORKS
Some Image Result

17. NEURAL NETWORKS
Some Image Result
Pre-trained model

18. BUILDING OUR OWN IMAGE
CLASSIFIER USING A MOBILE NET

19. TENSORBOARD

20. TENSORBOARD

21. TENSORBOARD
tensorboard --logdir tf_files/training_summaries &

22. TRAINING DATA

23. RETRAINING THE MODEL

24. RETRAINING THE MODEL

25. RETRAINING THE MODEL

26. RETRAINING THE MODEL

27. RETRAINING THE MODEL

28. RETRAINING THE MODEL

29. RETRAINING THE MODEL

30. RETRAINING THE MODEL

31. RETRAINING THE MODEL

32. RETRAINING THE MODEL

33. RETRAINING THE MODEL

34. HOW TRAINING WORKS?

35. HOW TRAINING WORKS?

36. HOW TRAINING WORKS?

37. HOW TRAINING WORKS?

38. HOW TRAINING WORKS?

39. HOW TRAINING WORKS?

40. MODEL ACCURACY

41. MODEL ACCURACY

42. OPTIMISING THE MODEL
Model
Operation
Model
Operation
Model
Operation
Model
Operation
Load graph
Don’t load
graph
Operation
supported?

43. OPTIMISING THE MODEL

44. OPTIMISING THE MODEL

45. OPTIMISING THE MODEL

46. OPTIMISING THE MODEL

47. OPTIMISING THE MODEL

48. QUANTISATION

49. QUANTISATION

50. QUANTISATION

51. QUANTISATION

52. OPTIMISING THE MODEL

53. ADDING THIS TO AN APP
Add Dependancy
Create TF
Reference
Feed data Run inference
Fetch result
Handle
confidence

54. ADDING THIS TO AN APP

55. ADDING THIS TO AN APP
// convert to 3d array (width / height / color)

56. ADDING THIS TO AN APP

57. ADDING THIS TO AN APP

58. ADDING THIS TO AN APP
Shape of our input

59. ADDING THIS TO AN APP

60. ADDING THIS TO AN APP

61. ADDING THIS TO AN APP

62. ADDING THIS TO AN APP
https://github.com/tensorflow/tensorflow

63. CONCLUSION