The document discusses high-level APIs in TensorFlow, highlighting its applications in AI research, including human action recognition and medical image processing. It outlines the flexible and extensible nature of TensorFlow, its features, and compares various deep learning architectures like AlexNet, VGG-Net, and Deep Residual Networks. Additionally, it mentions different loss functions and the benefits of efficient iterations and modifications in TensorFlow development.

1. High Level APIs
In
Tensorflow
SCG AI Research Group
Hyungjoo Cho

2. Who I am
• (ex) LG Electronics, VC company
- Printing image processing
- Proximity sensing
- Gesture recognition
• Seoul City Gas, AI Research Group
- Gas meter recognition
- Text classification
- Automatic pipeline network design system
• Interest
- Human Action Recognition
- Medical Image Processing
- Generative Adversarial Networks

3. What is Tensorflow??

4. Tensorflow
• Open source software library
for numerical computation
using data flow graphs.

5. Why should we use
Tensorflow??

6. *http://chainer.org/general/2017/02/08/Performance-of-Distributed-Deep-Learning-Using-ChainerMN.html

7. *https://arxiv.org/pdf/1608.07249.pdf

9. …???

11. Features
• Very low level (Flexible)
• Extensible
• Maintainable
• Higher level primitives (X)

12. High level API

13. Imagenet challenge

14. Break-through
2012 CHALLANGE

15. What happened??

16. Alex-net

17. Alex-net in Tensorflow

18. How about deeper net

19. VGG-Net

20. Its code

21. How about more
deeper …

22. Deep Residual Networks
We might use a network which has more than 1k layers
* https://github.com/daviddao/awesome-very-deep-learning

23. …

24. Let’s make it as a module

25. VGG-Net

26. Wide-Res-Net
*https://github.com/szagoruyko/wide-residual-networks

27. Wide-Res-Net

28. Fusion-Net

29. Activation functions
tf.nn.sigmoid tf.nn.tanh tf.nn.relu
*http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/

30. Others…
Leaky Relu/Parametric Relu
There’s no function…

31. Let’s make!!

32. Loss functions
• L1
• L2
• Binomial Cross Entropy
• Multinomial Cross Entropy
• Gan loss
• Pixel wise loss
• …

33. Make!!

34. Benefits
• Fast iteration
• Best practices
• Easily modify

35. Should we make this
ourselves?

36. There is a number of
High level API in
Tensorflow

37. High level API in TF
• TF-Slim
• TF-layers, losses, metrics
• TF-Learn
• Keras

38. Thanks❤
https://github.com/NySunShine/fusion-net