Keras Tutorial

In this page, we will learn What is keras?, What makes Keras special?, Keras user experience, How does Keras back up its claim to be multi-platform and multi-backend?, Keras Backend, Advantages of Keras, Disadvantages of Keras, and Prerequisite.

What is keras?

An open-source, Python-based high-level neural network framework called Keras is able to run on Theano, TensorFlow, or CNTK. Francois Chollet, a Google developer, was the one who created it. To enable quicker experimentation with deep neural networks, it has been made user-friendly, expandable, and modular. It supports both Convolutional and Recurrent Networks separately as well as in combination.

keras

It uses the Backend library to resolve low-level computations because it is unable to handle them. As a high-level API wrapper for the low-level API, the backend library enables it to run on TensorFlow, CNTK, or Theano.

When it was launched, it had over 4,800 contributors; as of now, there are over 250,000 developers. Since it has been growing, it has had a 2X growth. The development of Keras has been aggressively supported by large corporations, including Microsoft, Google, NVIDIA, and Amazon. In addition to being used in the development of well-known companies like Netflix, Uber, Google, Expedia, etc., it has tremendous industry interaction.

What makes Keras special?

  • A key component of Keras has always been its emphasis on the user experience.
  • substantial industry adoption.
  • It has many backends and supports multiple platforms, making it easier for all of the encoders to collaborate on coding.
  • Keras's research community and the production community work together remarkably well.
  • Concepts are simple to understand.
  • It facilitates quick prototyping.
  • It runs on both the CPU and GPU simultaneously.
  • It gives designers the flexibility to create any architecture, which is later used as an API for the project.
  • Really, getting started is extremely easy.
  • Keras is unique due to its simple model production.

Keras user experience:

  1. Keras is an API designed for humans
    Keras adheres to best practices to lessen cognitive strain, guarantees that the models are consistent, and makes sure that the corresponding APIs are straightforward.
  2. Not designed for machines
    For the majority of frequent use scenarios, Keras minimizes the number of user steps by providing unambiguous feedback when an error occurs.
  3. Easy to learn and use as well.
  4. Highly Flexible
    By incorporating low-level deep learning languages like TensorFlow or Theano, Keras offers extremely high flexibility to all of its developers and guarantees that anything created in the base language may be implemented in Keras.

How does Keras back up its claim to be multi-platform and multi-backend?

In order to run the code with TensorFlow, Theano, CNTK, or MXNet as needed, Keras can be built in both R and Python. A CPU, an AMD GPU, an NVIDIA GPU, a TPU, etc. can all run Keras. As it fully supports running with TensorFlow serving, GPU acceleration (WebKeras, Keras.js), Android (TF, TF Lite), iOS (Native CoreML), and Raspberry Pi, it ensures that creating models with Keras is incredibly straightforward.

Note: Keras Tutorial

Keras Backend

By providing high-level building blocks, Keras, a model-level library, aids in the creation of deep learning models. Instead of being handled by Keras itself, all of the low-level computations, including convolutions and products of tensors, rely on a specialized tensor manipulation library that has been carefully optimized to act as a backend engine. As a result, Keras enables the ability to plug in many backend engines rather than adopting a single tensor library and carrying out operations specific to that library.

The three backend engines that make up Keras are as follows:

TensorFlow:
One of the most well-known deep learning tools, TensorFlow, a Google product, is widely utilized in the machine learning and deep neural network research fields. On November 9th, 2015, it was released with the Apache License 2.0. It is designed in a way that makes it simple to run on a variety of CPUs, GPUs, and mobile operating systems. It is made up of several wrappers written in different languages, such as Python, C++, or Java.

TensorFlow

Theano:
Theano was developed by the MILA group at the University of Montreal in Canada's Quebec province. It is an open-source Python toolkit that uses the scipy and numpy libraries to perform mathematical operations on multi-dimensional arrays. It effectively computes the gradients by automatically creating symbolic graphs using GPUs for faster processing. It turned out to be a great fit for unstable expressions since it computes them using more reliable algorithms after first observing them numerically.

Theano

CNTK:
The open-source framework for deep learning is called the Microsoft Cognitive Toolkit. It includes every fundamental component needed to create a neural network. Although C++ or Python are used to train the models, C# or Java are also used to load the models and perform prediction operations.

CNTK

Advantages of Keras

Keras has the following benefits, which are listed below:

  • Faster network model deployment is very simple to comprehend and use. Due to the market's overwhelming interest in using it for AI businesses, it has strong community support.
  • According to your needs, you can utilize any one of TensorFlow, CNTK, or Theano with Keras as a backend because it allows multi-backend.
  • Since deployment is simple, cross-platform compatibility is included as well.
  • The following are the gadgets that Keras can be installed on:
    1. iOS with CoreML
    2. Android with TensorFlow Android
    3. Web browser with .js support
    4. Cloud engine
    5. Raspberry pi
  • Because it enables data parallelism, Keras may be trained simultaneously on several GPUs, reducing training time and enabling the processing of massive amounts of data.
Note: Keras Tutorial

Disadvantages of Keras

The only drawback is that Keras has pre-configured layers of its own and won't allow you to create an abstract layer because it can't handle low-level APIs. Only high-level APIs running on top of the backend engine are supported (TensorFlow, Theano, and CNTK).

Prerequisite

This Keras course is designed to clarify the language's core ideas for both newcomers and experts. You will reach a modest level of proficiency after finishing this session, from which you can advance your knowledge.

Audience

You must be familiar with both the fundamentals of neural networks and the Python language because Keras is a high-level deep learning library.

Problem

You may rest assured that this instruction won't present any challenges to you. If you have any questions or see any errors in this lesson, please let us know by posting them in the contact form so that we can continue to refine it.