Keras is a neural network library providing a high-level API in Python and R. Use this tag for questions relating to how to use this API. Please also include the tag for the language/backend ([python], [r], [tensorflow], [theano], [cntk]) that you are using. If you are using tensorflow's built-in keras, use the [tf.keras] tag.
Keras is a high-level deep learning API, written in python, similar in spirit to torch and lasagne. It is developed with a focus on enabling fast experimentation and now solely uses tensorflow as backend. Additionally, it also has a r interface.
Having a simple API with less capabilities, Keras is often seen as a good place to start experimenting with deep learning. For beginners, the Sequential API is easy to learn. For intermediate users, the Functional API has more capabilities and flexibility, but it comes at the cost of simplicity. For expert users, the Subclassing API enable ultimate capabilities, that should only be used in experimental settings.
Starting from TensorFlow 1.8 versions, Keras is also integrated in the TensorFlow framework. The creator of Keras, Francois Chollet, recommends that Keras should to be used from inside TensorFlow, as of TensorFlow version 2.0, since the latter package is much better maintained and will be updated in the future/less prone to errors as compared to the plain Keras library.
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