Keras is a model-level library that allows you to develop deep-learning models. It works on top of open-source machine-learning libraries such as TensorFlow, CNTK, and Theano. Keras provides an easy way to create deep learning models, and that is the reason it is called an optimal choice for creating deep learning applications.
Features of Keras:
There are a number of features of Keras that include the following:
- Keras is easy to learn and implement, which allows faster deployment of models created on the network.
- Keras has a large community base that supports the market and its developers continuously.
- Data parallelism is also supported by Keras, where you can train multiple GPUs in very less time and with a huge amount of data.
- Keras has several powerful backend libraries that include CNTK, TensorFlow, and Theano.
Out of all its features, Keras has only one disadvantage, and that is its pre-configured layers. While using Keras, you cannot handle low-level APIs as it only supports high-level APIs. So, this is the only disadvantage of the Keras framework.
