While deep neural networks are all the rage, the complexity of the major frameworks has been a barrier to their use for developers new to machine learning. There have been several proposals for improved and simplified high-level APIs for building neural network models, all of which tend to look similar from a distance but show differences on closer examination.

Keras is one of the leading high-level neural networks APIs. It is written in Python and supports multiple back-end neural network computation engines.

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Keras and TensorFlow

Given that the TensorFlow project has adopted Keras as the high-level API for the upcoming TensorFlow 2.0 release, Keras looks to be a winner, if not necessarily the winner. In this article, we'll explore the principles and implementation of Keras, with an eye towards understanding why it’s an improvement over low-level deep learning APIs.

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