# Keras

Keras is an open-source high-level deep learning API created by Francois Chollet in 2015, known for making neural network experimentation accessible and serving as the official high-level interface of TensorFlow.

Keras is an open-source deep learning library that provides a high-level, human-friendly API for building and training [neural networks](https://www.wikiprompt.org/wiki/neural-network). Created by [Francois Chollet](https://www.wikiprompt.org/wiki/francois-chollet) and first released in March 2015, it became one of the most widely used entry points into [deep learning](https://www.wikiprompt.org/wiki/deep-learning), prized for letting researchers and students define models in a few readable lines of code.

## History

Keras began as an independent library able to run on top of multiple backends, initially Theano and later [TensorFlow](https://www.wikiprompt.org/wiki/tensorflow) and Microsoft's CNTK. Its popularity grew quickly alongside the post-[AlexNet](https://www.wikiprompt.org/wiki/ilsvrc-2012) deep learning wave, and in 2017 Google adopted it as the official high-level API of TensorFlow, shipping it as `tf.keras`. In 2023 the project returned to its multi-backend roots with Keras 3, which runs on TensorFlow, [PyTorch](https://www.wikiprompt.org/wiki/pytorch) and JAX from a single codebase, a deliberate bet against framework lock-in at a time when research had consolidated around PyTorch.

## Design philosophy

Chollet described the guiding principle as "deep learning for humans": consistent and simple interfaces, sensible defaults, progressive disclosure of complexity, and clear error messages. A model is assembled from composable layers, compiled with a [loss function](https://www.wikiprompt.org/wiki/loss-function) and optimizer, and trained with a single `fit` call that handles [gradient descent](https://www.wikiprompt.org/wiki/gradient-descent), batching, validation splits and callbacks. This abstraction made Keras a standard teaching tool, featured in influential online courses by [Andrew Ng](https://www.wikiprompt.org/wiki/andrew-ng) and in Chollet's own widely read textbook Deep Learning with Python.

## Reception and influence

Keras has been praised for documentation quality and pedagogical clarity, and criticized in research settings for hiding too much of the training loop; power users often drop to lower-level APIs for custom architectures. Its layer-oriented ergonomics shaped later frameworks and high-level wrappers across the ecosystem, and its error-message philosophy became a reference point in discussions of developer experience for [machine learning](https://www.wikiprompt.org/wiki/machine-learning) tooling. While large-scale [LLM](https://www.wikiprompt.org/wiki/large-language-model) research today is dominated by PyTorch, Keras remains widely used in industry, education and applied work, and is credited with lowering the barrier to entry during deep learning's formative years.

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Source: https://www.wikiprompt.org/wiki/keras
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T22:00:27.734875+00:00