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High level-api in tensorflow

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The guide for design wrapper of tensorflow to build model easily.
All the codes above are available on my github.
https://github.com/NySunShine/fusion-net

Published in: Software
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High level-api in tensorflow

  1. 1. High Level APIs In Tensorflow SCG AI Research Group Hyungjoo Cho
  2. 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. 3. What is Tensorflow??
  4. 4. Tensorflow • Open source software library for numerical computation using data flow graphs.
  5. 5. Why should we use Tensorflow??
  6. 6. *http://chainer.org/general/2017/02/08/Performance-of-Distributed-Deep-Learning-Using-ChainerMN.html
  7. 7. *https://arxiv.org/pdf/1608.07249.pdf
  8. 8. …???
  9. 9. Features • Very low level (Flexible) • Extensible • Maintainable • Higher level primitives (X)
  10. 10. High level API
  11. 11. Imagenet challenge
  12. 12. Break-through 2012 CHALLANGE
  13. 13. What happened??
  14. 14. Alex-net
  15. 15. Alex-net in Tensorflow
  16. 16. How about deeper net
  17. 17. VGG-Net
  18. 18. Its code
  19. 19. How about more deeper …
  20. 20. Deep Residual Networks We might use a network which has more than 1k layers * https://github.com/daviddao/awesome-very-deep-learning
  21. 21.
  22. 22. Let’s make it as a module
  23. 23. VGG-Net
  24. 24. Wide-Res-Net *https://github.com/szagoruyko/wide-residual-networks
  25. 25. Wide-Res-Net
  26. 26. Fusion-Net
  27. 27. Activation functions tf.nn.sigmoid tf.nn.tanh tf.nn.relu *http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
  28. 28. Others… Leaky Relu/Parametric Relu There’s no function…
  29. 29. Let’s make!!
  30. 30. Loss functions • L1 • L2 • Binomial Cross Entropy • Multinomial Cross Entropy • Gan loss • Pixel wise loss • …
  31. 31. Make!!
  32. 32. Benefits • Fast iteration • Best practices • Easily modify
  33. 33. Should we make this ourselves?
  34. 34. There is a number of High level API in Tensorflow
  35. 35. High level API in TF • TF-Slim • TF-layers, losses, metrics • TF-Learn • Keras
  36. 36. Thanks❤ https://github.com/NySunShine/fusion-net

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