TensorFlow and Keras are both open-source libraries used for deep learning and artificial intelligence tasks. TensorFlow, developed by Google, is a powerful and versatile library used for various machine learning applications, including deep learning models. Keras, on the other hand, is a high-level neural networks API that is built on top of TensorFlow, as well as other backend libraries such as Theano and Microsoft Cognitive Toolkit (CNTK).

The relationship between TensorFlow and Keras can be understood as Keras being an interface that makes it easier to work with TensorFlow. Keras provides a simplified API for building, training, and evaluating neural networks, abstracting many of the complexities involved in working directly with TensorFlow. This makes it easier for developers, especially those who are new to deep learning, to quickly create and experiment with different models.

In 2017, Google decided to integrate Keras directly into TensorFlow as its official high-level API, starting from TensorFlow version 1.4. Consequently, Keras became a part of TensorFlow, and developers can now use Keras directly from the TensorFlow library by importing it as `tensorflow.keras`. This integration provides an even tighter relationship between TensorFlow and Keras, making it seamless to use Keras with TensorFlow's powerful features and backend optimizations.

In summary, the relationship between TensorFlow and Keras is that Keras is a high-level neural networks API built on top of TensorFlow, providing an easier and more user-friendly way to work with deep learning models.
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