TensorFlow 1.x vs 2.x. – summary of changes

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are very convenient to use. The writing of code was divided into two parts: building the computational graph and later creating a session to execute it. Eager Execution is implemented by default, i.e. you no longer need to create a session to run the computational graph, you can see the result of your code directly without the need of creating Session.