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
- 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.
- 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.
- Easy to learn and use.
- 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.
Keras being a model-level library helps in developing deep learning models by offering high-level building blocks. All the low-level computations such as products of Tensor, convolutions, etc. are not handled by Keras itself, rather they depend on a specialized tensor manipulation library that is well optimized to serve as a backend engine. Keras has managed it so perfectly that instead of incorporating one single library of tensor and performing operations related to that particular library, it offers plugging of different backend engines into Keras.
Keras consist of three backend engines, which are as follows:
TensorFlow is a Google product, which is one of the most famous deep learning tools widely used in the research area of machine learning and deep neural network. It came into the market on 9th November 2015 under the Apache License 2.0. It is built in such a way that it can easily run on multiple CPUs and GPUs as well as on mobile operating systems. It consists of various wrappers in distinct languages such as Java, C++, or Python.
Theano was developed at the University of Montreal, Quebec, Canada, by the MILA group. It is an open-source python library that is widely used for performing mathematical operations on multi-dimensional arrays by incorporating scipy and numpy. It utilizes GPUs for faster computation and efficiently computes the gradients by building symbolic graphs automatically. It has come out to be very suitable for unstable expressions, as it first observes them numerically and then computes them with more stable algorithms.
Microsoft Cognitive Toolkit is deep learning’s open-source framework. It consists of all the basic building blocks, which are required to form a neural network. The models are trained using C++ or Python, but it incorporates C# or Java to load the model for making predictions.
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:
- iOS with CoreML
- Android with TensorFlow Android
- Web browser with .js support
- Cloud engine
- 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).
This Keras tutorial is made for both beginners and professionals, to help them understand the fundamental concept of Keras. After the completion of this tutorial, you will find yourself at a moderate level of expertise from where you can take yourself to the next level.
Since Keras is a deep learning’s high-level library, so you are required to have hands-on Python language as well as basic knowledge of the neural network.
We assure you that you will not find any difficulty in this tutorial. In case you come up with a query, or you find any mistake in this tutorial, do let us know by posting it in the contact form so that we can further improve it.