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?

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.

Tensorflow 2.0: Keras is not (yet) a simplified interface to Tensorflow

In Tensorflow 2.0 Keras will be the default high-level API for building and training machine learning models, hence complete compatibility between a model defined using the old tf.layers and the new tf.keras.layers is expected. In version 2 of the popular machine learning framework the eager execution will be enabled by default although the static graph definition + session execution will be still supported. In this post, you'll see that the compatibility between a model defined using tf.layers and tf.keras.layers is not always guaranteed.

Fixed camera setup for object localization and measurement

A common task in Computer Vision is to use a camera for localize and measure certain objects in the scene. In the industry is common to use images of objects on a high contrast background and use Computer Vision algorithms to extract useful information. There's a lot of literature about the computer vision algorithm that we can use to extract the information, but something that's usually neglected is how to correctly setup the camera in order to correctly address the problem. This post aim is to shed light on this subject.