Introduction to Keras

 

In this post, we'll look at Keras, a popular deep learning system written in Python. Keras' modular architecture makes dealing with deep learning extremely simple and quick. Keras handles all higher-level deep learning modelling parts of your workstation's GPU and CPU with ease.

 

Keras with TensorFlow is the most popular and commonly used deep learning framework, which comes as no surprise. This post will provide you with an overview of the framework so that you may start utilising it for your deep learning experiments with confidence.

 

After reading this article, you will have a basic understanding of Keras, such as:

 

●     What is Keras and why is it so popular in the field of deep learning?

●     Keras framework has a number of important features.

●     Keras's modular architecture.

●     TensorFlow, Theano, and Microsoft's Cognitive Technology, or CNTK in short.

●     A quick comparison of the frameworks.

 

So, let's get started.

Introduction to Keras

It's a Python-based open-source library for creating neural network models. Keras is fast enough to run deep learning experiments quickly while also being user-friendly and adaptable thanks to its modular architecture.

 

Francois Chollet created Keras as part of the ONEIROS research project (Open Ended Electronic Intelligent Robot Operating System). Francois Chollet is a Google engineer and the inventor of the Xception deep neural network model.

 

Keras is released under the MIT permissive licence, which allows it to be freely used even for commercial purposes. One of the major reasons for its appeal can be attributed to this.

Keras’ popularity

TensorFlow is always popular in the machine learning area because it is the default Python library for all kinds of basic operations. The sci-kit learn library, which is well-known for machine learning and in particular for its many shallow learning algorithms, follows suit.

 

However, despite its high-end use in neural models, Keras appears to be a popular keyword. We must keep in mind that deep learning is a highly specialised field with only a few researchers from specific companies using it.

 

Keras was created to allow academics in academia and research institutions like NASA, NIH, and CERN to undertake deep learning experiments. Keras was also employed by CERN's Large Hadron Collider in their deep learning search for undiscovered particles.

 

Many startups, in addition to well-known companies like Google, Uber, and Netflix, rely significantly on Keras for deep learning applications in various R&D operations.

Keras’ Features

Keras has a number of distinguishing characteristics.

●     The same code runs on both the CPU and the GPU, and it's compatible with Python 2.7 and 3.5.

●     With the full deployment features of the TensorFlow platform, deployment is a breeze.

●     Keras models can be executed straight from browsers by exporting them as Java script or TensorFlow light for use on iOS, Android, and other mobile devices.

●     Keras can handle both convolutional and recurrent networks, as well as a combination of the two.

●     Keras is popular in scientific research because its low-level flexibility makes it easier to apply arbitrary research ideas while also giving high-level convenience to speed up experimentation cycles.

●     In the Keras framework, any task involving computer vision or sequence processing runs quickly and smoothly.

●     Keras can construct any type of deep learning model. It could be a generative adversarial network, a neural Turing machine, a multi-input/output model, or something else. Keras will construct them quickly and efficiently.

Keras’ modularity

Keras is only meant to handle the high-level model construction portion of the deep learning ecosystem. It allows well-optimized tensors to take care of the lower-level programming. All of the basic tensor operations and transformations are maintained by Google's TensorFlow framework as specialised tensors.

 

Keras uses two more backends in this modular approach: Theano and Microsoft's CNTK, both of which are popular deep learning execution engines. These aren't just for Keras, though. You can switch to any of them at any time if you discover that it is a better and faster way to do the assignment. Keras can now run on both the CPU and the GPU with these three libraries.

TensorFlow

This is a Google-developed deep learning library that can handle all tensor operations with ease. Keras, which is based on TensorFlow 2.0, has the potential to become the industry standard for creating huge GPUs.

 

In 2017, Google decided to include Keras support in the TensorFlow core library. Keras, according to Francois Chollet, is designed as an interface for doing high-end deep learning model construction tasks rather than a fully functioning machine learning framework.

 

TensorFlow wraps Keras code in a low-level package called Eigen, allowing it to run on the CPU.

Theano

It's also a Python library that's designed to execute a variety of mathematical operations, such as matrix manipulation and transformations, and it works well with NumPy. The MILA lab of the University of Montreal in Quebec, Canada, created Theano. It can also run on both the CPU and the GPU.

 

Theano can generate symbolic graphs that can be used to calculate gradient descent.

CNTK

This is the Cognitive Tool Kit from Microsoft, formerly known as the Computational Network Toolkit. It's a free toolkit that's simple to use but delivers commercial-grade performance when it comes to deep learning algorithm training.

 

Soon after Google chose to include Keras support in TensorFlow's core library in 2017, Microsoft decided to add CNTK to Keras' back end.

 

Because of its robustness, TensorFlow is the most commonly used of these three Keras implementation backends. It's also quite scalable.

A comparison of the frameworks.

To wrap up this essay, I'll go over some of Keras' features and how it differs from other popular frameworks. In the table below, the popular three frameworks TensorFlow, Keras, and Theano are compared.

 

We already know the fundamental distinction between them. Keras serves as the implementation interface for deep learning, while the rest of the libraries serve as the back end. However, there are some significant distinctions between them. Let's go over them one by one.

 

 

Property	Keras	TensorFlow	Theano	CNTK
Use Case	Experimentation with Deep Learning	Experimentation with Machine Learning	Multidimensional Matrix operations	CUDA support
Support for reinforcement learning	No	Yes	No	No
API level	High Level	Low Level	Low Level	Low Level
Performance	With Theano and TensorFlow in the back end, it's quite efficient.	High memory requirements because it is optimised for large models.	Run time and memory are competitive.	Similar to Theano and TensorFlow

 

Conclusion

These frameworks (TensorFlow, Keras, CNTK, and Theano) don't work separately; rather, they collaborate to compensate for each other's flaws. As a result, we were able to create a very powerful Deep Learning execution engine.

 

So, hopefully, this post has given you a basic understanding of Keras as a deep learning framework. This information is provided solely to let you feel more at ease before taking the initial step.

 

As you delve deeper into the deep learning applications, you'll discover more interesting facts and the concepts offered here will begin to make more sense.