What is the difference between keras and tf. Keras ?

The main differences between Keras and tf.keras are as follows:

1. Source and Maintenance of the Library:

   • Keras is an independent open-source project initiated by François Chollet in 2015. This library was originally designed as a high-level API for rapidly experimenting with machine learning models.
   • tf.keras is the official version of Keras integrated into TensorFlow. Starting from TensorFlow 1.10, tf.keras was incorporated into the TensorFlow core library and became the recommended model development API in TensorFlow 2.x.

2. API Compatibility:

   • Keras supports multiple backends, such as TensorFlow, Theano, or CNTK. This enables users to switch between these different backends seamlessly.
   • tf.keras is specifically designed for TensorFlow, optimizing its features and performance. All tf.keras models are built exclusively for TensorFlow and are not compatible with other backends.

3. Features and Update Speed:

   • Since tf.keras is part of TensorFlow, it can more quickly adopt new TensorFlow features, such as distributed training. Additionally, tf.keras typically leverages the TensorFlow ecosystem more effectively, including TensorFlow Serving or TensorFlow Lite.
   • Keras, as an independent project, may not receive updates as quickly as tf.keras, but it provides a more universal API suitable for users who do not exclusively rely on TensorFlow.

4. Performance:

   • tf.keras usually delivers more optimized performance because it is directly built on TensorFlow. This results in model execution being more closely integrated with TensorFlow's core implementation.

5. Use Cases:

   • If a user is already using TensorFlow and has no plans to switch to other backends, using tf.keras is a more natural choice due to its seamless integration and higher performance.
   • For users who need to switch between different deep learning frameworks or lack specific requirements for TensorFlow features, using standalone Keras may be preferable.

Based on the above comparison, choosing between Keras and tf.keras primarily depends on the user's specific needs and the other technologies they are using.