Keras that was initially developed as part of the research effort of the project named ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) is an open-source high-level neural networks API, that is written in Python and is capable of running on the top of TensorFlow, CNTK, Theano and was developed with a focus on enabling fast experimentation and the mind behind it or the author and maintainer is a Google engineer named François Chollet.
Keras was designed in order to enable fast experimentation with deep neural networks. The deep learning library focuses on being minimal, modular as well as extensible alongside and effectively wraps the numerical computation libraries like Theano and TensorFlow as already mentioned and also allows its users to define and train neural network models in only a few short lines of code. The Keras API should seem familiar enough to anyone who has previously worked with the well-known and well-accepted Scikit-learn API.
One can make the use of Keras if the below mentioned is what is required in a deep learning library:
A deep learning library that allows easy and fast prototyping through user friendliness, modularity, and extensibility alongside.
Supports both convolutional networks as well as recurrent networks, and also combinations of the two.
Runs effortlessly on CPU and GPU.
The Features of the Open-source deep learning library include:
This open-source deep learning library consists of numerous implementations of very commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and also a host of tools in order to make working with both image and text data easier.
Taking a note of its compatibility, Keras is compatible with: Python 2.7-3.6.
The Guiding principles are mentioned below:
User-friendly: Keras is an API that has been designed for human beings and not machines. It puts user experience on its front. The library follows best practices in order to reduce cognitive load and, therefore, offers consistent & simple APIs, minimizes the number of user actions required for use cases that are common, and also provides in return a clear and actionable feedback upon user error.
Modularity: A model can be understood as a graph or a sequence of fully-configurable, standalone modules that can be plugged together with restrictions that are as little as possible. To be particular, optimizers, initialization schemes, neural layers, regularization schemes, cost functions, activation functions, are all standalone modules that can be combined by one in order to create new models.
Easy extensibility: New modules prove to be simpler to add new classes and functions, and modules that already exist provide many examples. The ability to be able to create new modules easily allows the user for total expressiveness, therefore, making the library suitable for advanced research.
Work with Python: There is no presence of any separate model configuration files in a declarative format. Models have been described in Python code, which proves to be compact, easier to debug, and also allows for ease of extensibility.
We can, therefore, conclude by saying that Keras is deep learning in Python through which one can learn to preprocess the data, model, evaluate as well as optimize neural networks. The deep learning Library turns out to be a high-level alternative to both TensorFlow and Theano.
So, you can get going with this library by focusing on model structure, and avoid the complexity of numerical programming on GPUs. Start with Keras: Play faster and go deeper.
For More Information: GitHub