Getting Started with Keras - tpointtech/Keras GitHub Wiki

Introduction to Keras Tutorial

Keras Tutorial

Keras is an open source high-level Neural Network library, which is written in Python is able to be used with Theano, TensorFlow, or CNTK. It was designed with the help of Google engineer, Francois Chollet. It's accessible, flexible, and modular to facilitate more rapid exploration of deep neural networks. It is not limited to Convolutional Networks and Recurrent Networks as a pair, but also in combination.

It's not able to handle low-level calculations and relies on The Backend library to handle the issue. The Backend library functions as an API wrapper that is high-level for the low-level API that allows it to use TensorFlow, CNTK, or Theano.

In the beginning, it had 4800 contributors when it was first launched it has now grown into 250,000 users. Keras has seen a growth rate of 2x each year since it has expanded. Large companies such as Microsoft, Google, NVIDIA and Amazon have all actively participated in the growth of Keras. Keras is a marvellous interactions with the industry, and can be used to develop the products of well-known companies like Netflix, Uber, Google, Expedia, etc.

What is it that makes Keras different?

The focus on the user experience has been an integral part of Keras.

A large number of companies are adopting the technology. It's a multi backend and is compatible with multiple platforms which allows all encoders to work together in the process of coding. The research community that is present for Keras is extremely cooperative in conjunction with the production community.

Easy to comprehend all concepts.

  • It allows for rapid prototyping.
  • It is able to run on both CPUs and GPU.
  • It allows you to design any architectural structure and later used as an API for the project.
  • It's actually very easy to begin.
  • The ease of production of models creates Keras distinctive.

Keras user experience

Keras API is an API specifically designed for humans. Keras follows the best practices Keras to reduce cognitive burden and ensure that models remain in line and that the APIs are easy to use. Not designed to be used on machines. Keras gives clear information on the event of any error, which reduces the amount of user actions required for the majority of common scenarios of use. Simple to learn and make use of.

Highly Flexible

Keras offer high flexibility to all its developers, by integrating low-level deep-learning languages such for TensorFlow or Theano This ensures that everything created in the basic language is able to be implemented using Keras.

What does Keras can support its claim that it is Multi-Backend, multi-platform and multi-platform?

Keras can be created in R and Python so that the code is able to be executed with TensorFlow, Theano, CNTK or MXNet according to the requirements. Keras can be executed on CPUs, NVIDIA GPU, AMD GPU, TPU, and other. It makes sure that creating models using Keras is a breeze because it is fully compatible to run on TensorFlow providing, GPU acceleration (WebKeras, Keras.js), Android (TF Lite, and TF Lite), iOS (Native CoreML) and Raspberry Pi.

Keras Backend

Keras is a model-level software library that aids in the creation of deep learning models by providing high-level components. The lower-level computations like those of convolutions, Tensors and others. are not managed by Keras itself, instead they require a specially designed Tensor manipulator library that has been designed to function as an engine behind the scenes. Keras has done the task so well that instead of integrating a only tensor library and carrying out operations that are related to this particular library it allows integration of various backend engines to Keras.

Keras comprise three engines that are used as backends. They are as they are:

TensorFlow

TensorFlow is an Google product that is among the most well-known deep-learning tools used extensively in the field of research on machine learning as well as deep neural networks. It was released on November 9, 2015, with the Apache License 2.0. It is designed in a manner that it is able to run on multiple GPUs and CPUs as well as smartphones running operating systems. It comprises a variety of wrappers for different languages, such as Java, C++ or Python.

Theano

Theano was designed in Theano was developed at the University of Montreal, Quebec, Canada, by the MILA group. It is an open-source Python library that is extensively employed to perform mathematical calculations on multi-dimensional arrays, by incorporating numpy and scipy. It makes use of GPUs to speed up computation, and is able to efficiently calculate the gradients using symbolic graphs that are constructed automatically. It's been proven to be extremely suitable for weak expressions since it first analyzes them numerically, and then calculates the equations using more robust algorithms.

CNTK

Microsoft Cognitive Toolkit is deep learning's open-source framework. It comprises all of the essential building blocks that are essential to build an neural network. The models are developed using C++ or Python and it is incorporated with C# or Java to load the model in order to make predictions.

Advantages of Keras

Keras offers the following benefits that are listed below:

  • It is easy to comprehend and integrate the speedier deployment of models for networks.
  • It is a hugely popular community on the market and a lot of the AI businesses are keen on making use of it.
  • It has a multi-backend support This means that you can utilize any of TensorFlow, CNTK, and Theano using Keras for a backup, according to your needs.
  • It is a simple deployment, it also offers the ability to work with cross-platforms.

The following are the devices that Keras can be used:

  • iOS with CoreML CoreML
  • Android and TensorFlow Android. TensorFlow Android
  • Web browser that has .js support
  • Cloud engine
  • Raspberry pi

It can support Data parallelism that means that Keras is able to be trained using multiple GPUs in a single instance, speeding up the time for training and processing huge amounts of data.

Disadvantages of Keras

  • The only drawback is the fact that Keras comes with its own pre-configured layers.
  • If you're looking to build layers that are abstract, the program will not let you do it since it can't handle APIs that are low-level.
  • It can only support high-level APIs that are running on base of the engine (TensorFlow, Theano, and CNTK).