Keras

 Keras, Keras support the claim of being multi-backend and multi-platform, How to Install Keras on Windows, Face Recognition Neural Network with Keras Why we need Recognition

Keras is an open-source high-level Neural Network library, which is written in Python is capable enough to run on Theano, TensorFlow, or CNTK. It was developed by one of the Google engineers, Francois Chollet. It is made user-friendly, extensible, and modular for facilitating faster experimentation with deep neural networks. It not only supports Convolutional Networks and Recurrent Networks individually but also their combination.

It cannot handle low-level computations, so it makes use of the Backend library to resolve it. The backend library act as a high-level API wrapper for the low-level API, which lets it run on TensorFlow, CNTK, or Theano.

Initially, it had over 4800 contributors during its launch, which now has gone up to 250,000 developers. It has a 2X growth ever since every year it has grown. Big companies like Microsoft, Google, NVIDIA, and Amazon have actively contributed to the development of Keras. It has an amazing industry interaction, and it is used in the development of popular firms likes Netflix, Uber, Google, Expedia, etc.

What makes Keras special?

• Focus on user experience has always been a major part of Keras.
• Large adoption in the industry.
• It is a multi backend and supports multi-platform, which helps all the encoders come together for coding.
• Research community present for Keras works amazingly with the production community.
• Easy to grasp all concepts.
• It supports fast prototyping.
• It seamlessly runs on CPU as well as GPU.
• It provides the freedom to design any architecture, which then later is utilized as an API for the project.
• It is really very simple to get started with.
• Easy production of models actually makes Keras special.

Keras user experience

1. Keras is an API designed for humans
   Best practices are followed by Keras to decrease cognitive load, ensures that the models are consistent, and the corresponding APIs are simple.
2. Not designed for machines
   Keras provides clear feedback upon the occurrence of any error that minimizes the number of user actions for the majority of the common use cases.
3. Easy to learn and use.
4. Highly Flexible
   Keras provide high flexibility to all of its developers by integrating low-level deep learning languages such as TensorFlow or Theano, which ensures that anything written in the base language can be implemented in Keras.

How Keras support the claim of being multi-backend and multi-platform?

Keras can be developed in R as well as Python, such that the code can be run with TensorFlow, Theano, CNTK, or MXNet as per the requirement. Keras can be run on CPU, NVIDIA GPU, AMD GPU, TPU, etc. It ensures that producing models with Keras is really simple as it totally supports to run with TensorFlow serving, GPU acceleration (WebKeras, Keras.js), Android (TF, TF Lite), iOS (Native CoreML) and Raspberry Pi.

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Advantages of Keras

Keras encompasses the following advantages, which are as follows:

• It is very easy to understand and incorporate the faster deployment of network models.
• It has huge community support in the market as most of the AI companies are keen on using it.
• It supports multi backend, which means you can use any one of them among TensorFlow, CNTK, and Theano with Keras as a backend according to your requirement.
• Since it has an easy deployment, it also holds support for cross-platform. Following are the devices on which Keras can be deployed:
  1. iOS with CoreML
  2. Android with TensorFlow Android
  3. Web browser with .js support
  4. Cloud engine
  5. Raspberry pi
• It supports Data parallelism, which means Keras can be trained on multiple GPU's at an instance for speeding up the training time and processing a huge amount of data.

Disadvantages of Keras

• The only disadvantage is that Keras has its own pre-configured layers, and if you want to create an abstract layer, it won't let you because it cannot handle low-level APIs. It only supports high-level API running on the top of the backend engine (TensorFlow, Theano, and CNTK).

Overview of Keras

Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical computation tasks. TensorFlow is the most famous symbolic math library used for creating neural networks and deep learning models. TensorFlow is very flexible and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as Python, C#, C++ or standalone machine learning toolkits. Theano and TensorFlow are very powerful libraries but difficult to understand for creating neural networks.

Keras is based on minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano. Keras is designed to quickly define deep learning models. Well, Keras is an optimal choice for deep learning applications.

Features

Keras leverages various optimization techniques to make high level neural network API easier and more performant. It supports the following features −

• Consistent, simple and extensible API.

• Minimal structure - easy to achieve the result without any frills.

• It supports multiple platforms and backends.

• It is user friendly framework which runs on both CPU and GPU.

• Highly scalability of computation.

Benefits

Keras is highly powerful and dynamic framework and comes up with the following advantages −

• Larger community support.

• Easy to test.

• Keras neural networks are written in Python which makes things simpler.

• Keras supports both convolution and recurrent networks.

• Deep learning models are discrete components, so that, you can combine into many ways.