Webinar on Tensorflow 2.0 and Keras in preparation to the ODSC conference in May 2019. Cover what's new in the upcoming Tensorflow 2.0 release and the differences with Keras.io

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TENSORFLOW 2.0 AND KERAS
Francesco Mosconi
These slides: bit.ly/odsc20190124
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TODAY
• Changes inTensorflow 2.0
• How it differsTensorflow 1.*
• How it differs from keras.io

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SOMETIME IN AUGUST 2018 …

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KEY POINTS
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity withTensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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TODAY
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity withTensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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KEY POINTS
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity withTensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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PUBLIC 2.0 DESIGN PROCESS
https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss
https://github.com/tensorflow/community/blob/master/governance/TF-RFCs.md
https://github.com/tensorflow/community/tree/master/rfcs

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WHAT IT MEANS FORYOU
• Release date not defined yet
=> rumored forTF Dev Summit (March 6-7, 2019)
• If you’re aTF 1.x developer
=> start updating your code as soon as RFPs are accepted
• There will be a conversion tool from 1.x to 2.0 (won’t be
perfect, but hey…)

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KEY POINTS
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity withTensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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EAGER EXECUTION
• Available since late 2017
• Following Pytorch and Chainer
• Imperative
• Define-by-run
• No static graph & session

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EAGER EXECUTION
• Faster debugging with Python tools
• Dynamic models with Python control flow
• Support for custom and higher-order gradients

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IS GRAPH GOING AWAY?
• No, you can still define models using the traditional
static graph approach

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Standard Tensorflow

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Standard Tensorflow
Eager Execution

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Standard Tensorflow

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Standard Tensorflow Eager Execution

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WHAT IT MEANS FORYOU
• Debug like Numpy, scale likeTensorflow
• Easier to build and test custom models
• Slower than static graph mode

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KEY POINTS
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity withTensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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MODEL APIS INTF 1.X
• tf.core
• tf.layers
• tf.keras.layers
• tf.estimator.Estimator
• tf.contrib.learn.Estimator
• …

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MODEL APIS INTF 2.0
• tf.core ====> Same
• tf.layers ====> Deprecated
• tf.keras.layers ====> Primary API
• tf.estimator.Estimator ====> API same, implementation changed to tf.keras
• tf.contrib.learn.Estimator ====> Deprecated
• …

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MODEL APIS INTF 2.0
• tf.core ====> Same
• tf.layers ====> Deprecated
• tf.keras.layers ====> Primary API
• tf.estimator.Estimator ====> API same, implementation changed to tf.keras
• tf.contrib.learn.Estimator ====> Deprecated
• …

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TF.KERAS
• What is it?
• How does it work?
• How does it differ from keras.io implementation?

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WHAT IS KERAS?
https://towardsdatascience.com/deep-learning-framework-power-scores-2018-23607ddf297a

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WHAT IS KERAS
• Keras is an API specification to design deep learning model
• https://keras.io/
=> independent reference implementation (usingTF or CNTK as backend)
• tf.keras
=>Tensorflow implementation of the same API spec
• Other frameworks implement some version of the API

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MODEL DEFINITION IN KERAS
• Sequential API
• Functional API
• Model API

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SEQUENTIAL API

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SEQUENTIAL API

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FUNCTIONAL API

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MODEL API

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DIFFERENCES
• Support for Eager Execution
• tf.dataVS python data generators
• Model Exporting
• Compatible with Feature Columns
• Compatible with Estimators

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KEY POINTS
• Public 2.0 design process
• Eager execution
• Remove deprecated APIs & reduce the amount of duplication
• Compatibility and continuity with Tensorflow 1.x
• Compatibility with 1.x exported models
• No more tf.contrib
• More platforms and languages

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bootcamp.zerotodeeplearning.com
March 25 - 29 San Francisco Bay Area

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SEEYOU IN MAY …
Francesco Mosconi
@framosconis fm@catalit.com
bootcamp.zerotodeeplearning.com
Data Weekends
Catalit
Data Science
Zero to
Deep Learning