TensorFlow 2.x vs 1.x: Key Differences

Some differences between TensorFlow 2.x and 1.x versions include:

1. In TensorFlow 2.x, Eager Execution mode is enabled by default, allowing immediate results without the need to build a computation graph, unlike TensorFlow 1.x which requires building a graph before execution.
2. Integration of Keras: In TensorFlow 2.x, Keras is integrated as part of its high-level API, making the construction, training, and evaluation of models simpler and more intuitive.
3. tf.function in TensorFlow 2.x introduces the ability to convert Python functions into TensorFlow computation graphs, increasing the efficiency of code execution.
4. Some less frequently used modules and functions have been removed, simplifying the API.
5. Improved the way models are deployed, supporting technologies like TensorFlow Serving and TensorRT.
6. New features have been introduced, such as AutoGraph, tf.data, tf.keras.layers, making model development more convenient and efficient.

Overall, TensorFlow 2.x is more user-friendly, flexible, and efficient compared to version 1.x.