KERAS : Under the Hood

Getting started with deep learning has become very simple and convenient, all thanks to wonderful duo of keras and tensorflow. You just need to do some imports, define some layers and bingo, you have your deep learning architecture ready to be trained and eventually give some amazing results. Keras has made such an amazing abstraction that even a total stranger to the topic as well can start training their own deep learning models. However if you are calling yourself a Data Scientist/Machine Learning Engineer then having some basic understanding of what’s happening under the hood is a must, I am not saying you need to exactly know the hundreds of lines of code behind it but at least have a some understanding what those lines of code are doing.

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Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

• It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
• Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
• Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
• It offers a consistent API that provides necessary feedback when an error occurs.
• Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

• The simple API structure of Keras is designed for both new developers and experts.
• The Keras interface is very user friendly and is pretty optimized for general use cases.
• In Keras, you can write custom blocks to extend it.
• Keras is the second most popular deep learning framework after TensorFlow.
• Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

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Keras Tutorial For Beginners | What is Keras | Keras Sequential Model | Keras Training

In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo.
Why Keras is important

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that.

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Why Learn Keras - Reasons Why Choose Keras - DataFlair

This article is the spotlight on the need for python deep learning library, Keras. Keras offers a uniform face for various deep learning frameworks including Tensorflow, Theano, and MXNet. Let us see why you should choose and learn keras now.

Why Learn Keras?

Keras makes deep learning accessible and local on your computer.It also acts as a frontend for other big cloud providers. It is the most voted recommendation for beginners who want to start their journey in machine learning. It provides a minimal approach to run neural networks. This allows students to learn complex features from input data sequentially.

Features of Keras

Let us see some of the features of keras that make you learn Keras.

1. Simple API

Keras is the most easy to use the library for machine learning for beginners. Being simple helps it to bring machine learning from imaginations to reality. It provides an infrastructure that can be learned in very less time. Using Keras, you will be able to stack layers like experts.

2. Pythonic Nature

Python is the most popular library for machine learning and Data Science. The compatibility with python allows Keras to have many useful features. Writing less code, easy to debug, easy to deploy, extensibility is due to the support of Keras with python 2.7 and python 3.6.

3. Strong Backend Support

Keras being a high-level API provides support for multiple popular and powerful backend frameworks. Tensorflow, theano, CNTK are very dominant for backend computations and Keras supports all of them.

4. Base for Innovations

The importance of Keras leads to many other innovative tools to explore deep learning. These tools are built on top of Keras making Keras as the base. The following tools are:

• Deepjazz: This is deep learning-driven jazz built using Keras and theano, available on github.
• Eclipse Picasso: It is a visualization tool that works with Keras checkpoints.
• Auto Keras: It is built upon Keras and used for machine learning model automation.

Reasons to Learn and Use Keras

• Keras allows us to switch between the backends as per the requirement of our applications. It acts as a wrapper that gives us the privilege to use either TensorFlow, theano, or any other framework.
• Keras is very easy and enjoyable to use. It uses great guiding principles like extensibility, python nativeness, and modularity.
• The ability of Keras to create the state of the art implementations of common deep neural networks. These are fast and it is easy to get them running using Keras.
• Being Keras user, you will be more faster and productive, you will have the ability to try more ideas.
• Keras provides Multi-GPU and strong distributed support. We can run our deep learning models on large GPU clusters.
• We can deploy Keras deep learning models on multiple platforms. For example, We can deploy in the browser using tensorflow.js, on the server using either TensorFlow serving or using Node.js runtime. On mobile devices i.e in android or IOS, we can deploy using TensorFlow Lite.
• Keras has a large ecosystem of products to support your deep learning development. Some of the popular products are Tensorflow Cloud, Keras Tuner, Tensorflow Lite,Tensorflow.js, and Tensorflow Model Optimizatio

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Keras Modules - Types and Examples

Keras modules provide various predefined classes and functions for deep learning algorithms. In this Keras tutorial, we will learn various modules in Keras. We will study the features and few of the applications of these modules.

Keras Modules

Various Modules available in keras are:

• Backend
• Utils
• Image Processing
• Sequence Processing
• Text Processing
• Callback

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Keras Ecosystem - Keras Open Source Frameworks

Keras, other than being a high-level deep learning API also has some other initiatives for machine learning workflow. There is a wide range of machine learning frameworks whose development is based on Keras. In this article, we will discuss Keras Ecosystem. This ecosystem of frameworks tries to ease and optimize the current approach of training and deploying ML models.

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Keras Ecosystem

Some of the frameworks for Keras Ecosystem are:

1. Auto Keras

This framework was built at the DATA lab with an ambition of making machine learning accessible to everyone.

It is a simple interface to perform many machine learning tasks. The supported tasks in auto Keras are image classifier, image regression, text classification, text regression, structured data classification, and structured data regression.

An example of image classification task using auto Keras:

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