Sep 21, 2019
Hands-On Neural Networks with TensorFlow 2.0
The first book on TensorFlow 2.0 and neural networks is out now!
May 10, 2019
Analyzing tf.function to discover AutoGraph strengths and subtleties - part 3
In this third and last part, we analyze what happens when tf.function is used to convert a function that contains complex Python constructs in its body. Should we design functions thinking about how they are going to be converted?
Apr 3, 2019
Analyzing tf.function to discover AutoGraph strengths and subtleties - part 2
In part 1 we learned how to convert a 1.x code to its eager version, the eager version to its graph representation and faced the problems that arise when working with functions that create a state. In this second part, we’ll analyze what happens when instead of a tf.Variable we pass a tf.Tensor or a Python native type as input to a tf.function decorated function. Are we sure everything is going to be converted to the Graph representation we expect?
Mar 21, 2019
Analyzing tf.function to discover AutoGraph strengths and subtleties - part 1
AutoGraph is one of the most exciting new features of Tensorflow 2.0: it allows transforming a subset of Python syntax into its portable, high-performance and language agnostic graph representation bridging the gap between Tensorflow 1.x and the 2.0 release based on eager execution. As often happens all that glitters is not gold: although powerful, AutoGraph hides some subtlety that is worth knowing; this article will guide you through them using an error-driven approach.