Guna  Rakulan

Guna Rakulan

1608279569

Scheduling Learning Rate in Keras

In this video, you will learn about learning rate schedules and decay using Keras. You’ll learn how to use Keras’ standard learning rate decay along with step-based, linear, and polynomial learning rate schedules.

Code generated in the video can be downloaded from here:

Subscribe : https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w

#keras #python #machine-learning

What is GEEK

Buddha Community

Scheduling Learning Rate in Keras

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.

Keras Tutorial

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.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

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

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

#keras tutorials #importance of keras #keras features #learn keras #deep learning

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Deval Desai

Deval Desai

1601694581

Adding and Customizing Learning Rate Schedule in Machine Learning Model

One of the painful things about training a neural network is the sheer number of hyperparameters we have to deal with. For example

  • Learning rate
  • Momentum or the hyperparameters for Adam optimization algorithm
  • Number of layers
  • Number of hidden units
  • Mini-batch size
  • Activation function
  • etc

Among them, the most important parameter is the learning rate. If your learning rate is set to low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.

Image for post

Image created by author using https://excalidraw.com/

When training a neural network, it is often useful to reduce the learning rate as the training progresses. This can be done by using learning rate schedules or adaptive learning rate. In this article, we will focus on adding and customizing learning rate schedule in our machine learning model and look at examples of how we do them in practice with Keras and TensorFlow 2.0

Learning Rate Schedules

Learning Rate Schedules seek to adjust the learning rate during training by reducing the learning rate according to a pre-defined schedule. The popular learning rate schedules include

  1. Constant learning rate
  2. Time-based decay
  3. Step decay
  4. Exponential decay

For the demonstration purpose, we will build an image classifier to tackle Fashion MNIST, which is a dataset that has 70,000 grayscale images of 28-by-28 pixels with 10 classes.

Please check out my Github repo for source code.

#machine-learning #tensorflow #deep-learning #keras

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert