Tensorflow 2.0: models migration and new design
Tensorflow 2.0 will be a major milestone for the most popular machine learning framework: lots of changes are coming, and all with the aim of making ML accessible to everyone. These changes, however, requires for the old users to completely re-learn how to use the framework: this article describes all the (known) differences between the 1.x and 2.x version, focusing on the change of mindset required and highlighting the pros and cons of the new and implementations. This article can be a good starting point also for the novice: start thinking in the Tensorflow 2.0 way right now, so you don’t have to re-learn a new framework (unless until Tensorflow 3.0 will be released).
Tensorflow 2.0: why and when? The idea is to make Tensorflow easier to learn and apply. The first glimpse on what Tensorlow 2.0 will be has been given by Martin Wicke, one of the Google Brain Engineers, in the Announcements Mailing List, here.
In short: Eager execution will be a central feature of 2.0. It aligns users’ expectations about the programming model better with TensorFlow practice and should make TensorFlow easier to learn and apply. Support for more platforms and languages, and improved compatibility and parity between these components via standardization on exchange formats and alignment of APIs.