What is the relationship and difference between Keras and TensorFlow?

Keras is an advanced neural network API that can run on multiple deep learning frameworks, including TensorFlow. TensorFlow is an open-source deep learning framework that offers many low-level operations and functionalities for building and training neural network models.

The relationship between Keras and TensorFlow is that Keras can serve as the frontend interface for TensorFlow, allowing users to access TensorFlow’s underlying functions. In fact, in TensorFlow version 2.0, Keras became the default high-level API of TensorFlow and is maintained by the TensorFlow team. This means that when using TensorFlow 2.0 and later versions, Keras can be directly accessed by importing tf.keras.

The main differences between the two are as follows:

1. Simplicity: Keras was designed with the intention of providing a user-friendly interface that is easy to use, focusing primarily on quickly building and training models. In contrast, TensorFlow offers more low-level operations, making it relatively more complex.
2. Functionality: TensorFlow offers a more comprehensive and flexible set of functions to support a wider range of deep learning tasks and model structures, while Keras focuses on providing a concise and efficient way to build and train neural network models.
3. Community Support: TensorFlow benefits from a large and active open-source community, as well as a rich ecosystem. As part of TensorFlow, Keras also receives support and resources from the TensorFlow community.
4. Portability: Because Keras can run on multiple deep learning frameworks, it is easy to transfer Keras models to other Keras-supported frameworks, while TensorFlow is more focused on its own ecosystem.

In general, Keras offers a simple and efficient way to build and train neural network models, while TensorFlow provides a more low-level and flexible functionality that can be used for a wider range of deep learning tasks.