Keras is a high-level neural networks library that is running on top of TensorFlow, CNTK, and Theano. Using Keras in deep learning allows for easy and fast prototyping as well as running seamlessly on CPU and GPU. This framework is written in Python code which is easy to debug and allows ease for extensibility.
The main advantages of Keras are described below:
- User-Friendly: Keras has a simple, consistent interface optimized for common use cases which provides clear and actionable feedback for user errors.
- Modular and Composable: Keras models are made by connecting configurable building blocks together, with few restrictions.
- Easy To Extend: With the help of Keras, you can easily write custom building blocks for new ideas and researches.
- Easy To Use: Keras offers consistent & simple APIs which helps in minimizing the number of user actions required for common use cases, also provides clear and actionable feedback upon user error.
Why you should go for Keras?
Keras offers simple and consistent high-level APIs and follows best practices to reduce the cognitive load for the users. Kerasprovide high-level APIs for building and training models with ease. Keras is built in Python which makes it way more user-friendly than TensorFlow.
Keras is designed for deep neural networks while TensorFlow is designed for machine learning applications. A researcher must choose a framework depending upon the task s/he is going to perform.