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Concepts of Machine Learning: Transitioning to TensorFlow 2.0

Have you not heard about the recent release of TensorFlow 2.0? Well, you might be thinking that this is an unusual question to start with. But with the entire internet going gaga over this newly released version of TensorFlow, Google’s open-source library for building machine learning models, it is unlikely that you haven’t heard about the same. So in an attempt to keep up with this trending new version of the machine learning platform, we have decided to add to these discussions and debates by giving our readers a brief up on this brand new version of the machine learning platform and how Google has transitioned from TensorFlow to TensorFlow 2.0.

We would like to first touch on the history of TensorFlow prior to taking this story forward. TensorFlow initially was open-sourced in the year 2015 when this library was mainly used for providing high-performance APIs to neural networks. Through the years, integrations were slowly made to the library transform it into a complete machine learning ecosystem. Though this earlier version provided the users with several features for building machine learning models such as ease of visualization, easy trainability, the users of the platform expected more from this product of Google and threw out a bunch of feedback which Google had taken into serious consideration while building the new version.

Following are some of the changes that Google has made while moving from TensorFlow to TensorFlow 2.0.

1. TensorFlow initially granted its users access to multiple libraries such as tf.contrib  for model building. This availability complicated model building as the selection of the right API had left many confused. TensorFlow2.0 has eliminated this confusion by integrating a “tighter” Keras as well as Estimators. Through the integration of these two APIs, Google aims at further easing the process of model building.
2. Version 2.0 also eased the process of execution. Earlier, writing the code for building computational graphs and execution of this code were two separate steps but in the newer version, the second step has been eliminated by automating the execution process.
3. In the earlier version, building a model needed the usage of placeholders, dummy variables used for storage of data. A layered approach through the usage of several available APIs was used for model building. In the newer version, the need for usage of placeholders has been eliminated. Model building through definition of mathematical operations, a feature that was also present in the former version, is made easier in the second version due to the presence of a higher level keras API.
4. The use of the higher level Keras API has also made model building easy where as the Estimator API has eased the process of model deployment.


Google’s team has also revealed that this version has also been well optimized for python developers. TensorFlow allows users to write their functions in a pure python syntax and also optimise their functions. Another important feature which we would like to point out is that the tf.function decorator has the ability to directly run the immediate function block as a single graph.


Also, Google has enabled a higher flexibility of deployment. The tensorflow 2.0 doesn’t only allow you to run the built model on a local host but also allows deployment on a multi-server environment.

Google has also made the migration of users from TensorFlow to TensorFlow 2.0 an easy task by providing the users with a complete guide for transitioning.

All of the above features have been made in view of transforming this machine learning platform into an easier and a more flexible ecosystem to build and deploy machine learning models. According to us, Google has definitely made a tremendous effort in making this possible and though, the final verdict on this platform’s usage is yet to come out, the overall review for the features that this platform promises to provide is that it has definitely met everybody’s expectations.

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Dedeepya Bypuneedi

A Computer Science graduate by education and a content writer by profession. Currently fulfilling her zeal to write by putting pen to paper every time she comes across something that is interesting enough to let the world know

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