This document provides an overview of TensorFlow 2.0 and discusses several key features: - TensorFlow 2.0 allows for deployment anywhere and supports eager execution for interactive development. - Keras APIs can be used for both symbolic and imperative model building. Estimators provide high-level tools for working with models at scale. - TensorFlow Hub contains pre-trained models that can be used for transfer learning. Examples of image and text models are listed. - Custom models can be built using GradientTape for automatic differentiation and custom training loops. Data can be loaded from files, datasets, or TensorFlow Datasets.

1. Walking through
Tensorflow 2.0
Houston Machine Learning Meetup
Nov. 2, 2019

2. TensorFlow 2.0

3. Deploy anywhere

4. TensorFlow 2.0 Overview

5. Additional support

6. TensorFlow 2.0 Specifics

7. Eager execution

8. Keras: Symbolic vs Imperative APIs

9. Building models with tensorflow Keras API

10. Estimators
• Estimator: High level tools for working with models.

11. Create Estimators from Keras
Input function with no args
return a tf.data.Dataset
Great for:
Scaling to large datasets or
multi-device training

12. tf.function

13. AutoGraph makes this possible
https://www.youtube.com/watch?v=Up9CvRLIIIw

14. Customization with GradientTape
Implement linear regression
Define a model
Define the loss function

15. Customization with GradientTape
Implement linear regression
Preparing training data
Automatic differentiation
decrementing a value

16. Customization with GradientTape
Implement linear regression
Define a training loop

17. Load Data
Read from CSV file
Convert from Pandas DataFrame
Convert from Numpy Array

18. Load Image
More efficient with tf.data.Dataset!
https://www.tensorflow.org/tutorials/load_data/images

19. TensorFlow Datasets

20. Load data from tensorflow_datasets

21. TensorFlow Hub https://youtu.be/y_qUJIfkbPs

22. Image models
• NASNet (2018 CVPR): Neural
Architecture Search Network
• MobileNet (including its recent
V2, 2018 CVPR):
• Inception (including Inception v4
and Inception-ResNet)
• ResNet
https://medium.com/@culurciello/analysis-of-deep-neural-
networks-dcf398e71aae

23. Text models
• Transformer
• Universal-sentence-encoder-multilingual-qa for 16 languages
• Bidirectional Encoder Representations from Transformers
• DAN (Deep Average Network)
• Universal-sentence-encoder
• ELMo (Embeddings from Language Models)
• Deep contextualized word representations
• NNLM (feed-forward Neural-Net Language Models, trained on google
news 30B)
• Word2vec (trained on English Wikipedia corpus)

24. Using hub model
import tensorflow_hub as hub

25. Transfer Learning

26. Transfer Learning: Example
doesn't include the classification layers at the top

27. Going big: Multi-GPU

28. Code Walkthrough
• Text classification with TensorFlow Hub
https://www.tensorflow.org/tutorials/keras/text_classification_with_h
ub
• Neural style transfer
https://www.tensorflow.org/tutorials/generative/style_transfer

29. For more
details
https://www.tensorflow.org/tutorials
Homework: Create an art piece of self-portrait
with neural transfer!
RL meetup of dynamic programming on Nov. 16
Reinforcement workshop on Dec. 7.
Call for submissions!!!
https://www.meetup.com/Houston-Machine-
Learning/