Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
2019-09-02, 11:00–12:30, Track 3 (Oteiza)

GANs are one of the hottest topics in the ML arena; however, they present a challenge for the researchers and the engineers alike. This workshop will guide you through both the theory and the code needed to build a GAN and put into production.

GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.

The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues.

We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0.

The workshop is composed of

  • Theoretical introduction
  • GANs from Scratch in TensorFlow 2.0
  • High-performance input data pipeline with TensorFlow Datasets
  • Introduction to the AshPy API
  • Implementing, training, and visualizing DCGAN using AshPy
  • Serving TF2 Models with Google Cloud Functions

The materials of the workshop will be openly provided via GitHub ( prior to the event and will be run on Colab leveraging the free GPU

Note: the workshop requires Python 3.7 to run, therefore the colab support is still uncertain. The attendees are encouraged to bring their own devices with Python 3.7 installed and ready to use.

Requirements and set up instructions

Two options available:

  1. (recommended). Use Google Colab & Binder. Every notebook has a button to lunch the correct tool. Just use it.
  2. Local setup: follow the instructions in the README

Python Skill Level


Domain Expertise



Image Processing, Machine Learning, Statistics

Abstract as a tweet

GANs are hard, but need not to be. Come with us on a voyage from theory to production leveraging TensorFlow 2.0

Project Homepage / Git