TF 2.0 Showcase

The code here shows idiomatic ways to write TensorFlow 2.0 code. It doubles as an integration test.

General guidelines for showcase code:

• Code should minimize dependencies and be self-contained in one file. A user should be able to copy-paste the example code into their project and have it just work.

• Code should emphasize simplicity over performance, as long as it performs within a factor of 2-3x of the optimized implementation.

• Code should work on CPU and single GPU.

• Code should run in Python 3.

• Code should conform to the Google Python Style Guide

• Code should follow these guidelines:

  • Prefer Keras.
  • Split code into separate input pipeline and model code segments.
  • Don‘t use tf.cond or tf.while_loop; instead, make use of AutoGraph’s functionality to compile Python for, while, and if statements.
  • Prefer a simple training loop over Estimator
  • Save and restore a SavedModel.
  • Write basic TensorBoard metrics - loss, accuracy,