Implements Gradient Centralization in TensorFlow with Python Package

Gradient Centralization TensorFlow 

This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique for Deep Neural Networks as suggested by Yong et al. in the paper Gradient Centralization: A New Optimization Technique for Deep Neural Networks. It can both speedup training process and improve the final generalization performance of DNNs.

Installation

Run the following to install:

pip install gradient-centralization-tf

About the Examples

gctf_mnist.ipynb

This notebook shows the the process of using the gradient-centralization-tf Python package to train on the Fashion MNIST dataset availaible from tf.keras.datasets. It further also compares using gctf and performance without using gctf.

gctf_horses_v_humans.ipynb

 

This notebook shows the the process of using the gradient-centralization-tf Python package to train on the Horses vs Humans dataset by Laurence Moroney. It further also compares using gctf and performance without using gctf.

Usage

gctf.centralized_gradients_for_optimizer

Create a centralized gradients functions for a specified optimizer.

Arguments:

• optimizer: a tf.keras.optimizers.Optimizer object. The optimizer you are using.

Example:

>>> opt = tf.keras.optimizers.Adam(learning_rate=0.1)
>>> opt.get_gradients = gctf.centralized_gradients_for_optimizer(opt)
>>> model.compile(optimizer = opt, ...)

Returns:

A tf.keras.optimizers.Optimizer object.

gctf.get_centralized_gradients

Computes the centralized gradients.

This function is ideally not meant to be used directly unless you are building a custom optimizer, in which case you could point get_gradients to this function. This is a modified version of tf.keras.optimizers.Optimizer.get_gradients.

Arguments:

• optimizer: a tf.keras.optimizers.Optimizer object. The optimizer you are using.
• loss: Scalar tensor to minimize.
• params: List of variables.

Returns:

A gradients tensor.

gctf.optimizers

Pre built updated optimizers implementing GC.

This module is speciially built for testing out GC and in most cases you would be using gctf.centralized_gradients_for_optimizer though this module implements gctf.centralized_gradients_for_optimizer. You can directly use all optimizers with tf.keras.optimizers updated for GC.

Example:

>>> model.compile(optimizer = gctf.optimizers.adam(learning_rate = 0.01), ...)
>>> model.compile(optimizer = gctf.optimizers.rmsprop(learning_rate = 0.01, rho = 0.91), ...)
>>> model.compile(optimizer = gctf.optimizers.sgd(), ...)

Returns:

A tf.keras.optimizers.Optimizer object.

Developing gctf

To install gradient-centralization-tf, along with tools you need to develop and test, run the following in your virtualenv:

git clone https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
# or clone your own fork

pip install -e .[dev]

Download Details:
Author: Rishit-dagli
Source Code: https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
License: Apache-2.0 License

#tensorflow #python 

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Top 20 Most Useful Python Modules or Packages

 March 25, 2021  Deepak@321  0 Comments

Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.

Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:

1. Web Development
2. Data Science
3. Machine Learning
4. AI and graphical user interfaces.

Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.

#python #packages or libraries #python 20 modules #python 20 most usefull modules #python intersting modules #top 20 python libraries #top 20 python modules #top 20 python packages

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Difference Between Python Module and Python Package?

Difference between python module and python package?

What’s the difference between a Python module and a Python package?

Module:  It is a simple Python file that contains collections of functions and global variables and has a “.py”  extension file. It’s an executable file and we have something called a “Package” in Python to organize all these modules.

Package:  It is a simple directory which has collections of modules, i.e., a package is a directory of Python modules containing an additional init.py  file. It is the init.py  which maintains the distinction between a package and a directory that contains a bunch of Python scripts. A Package simply is a namespace. A package can also contain sub-packages.

When we import a module or a package, Python creates a corresponding object which is always of type module . This means that the dissimilarity is just at the file system level between module and package.

#technology #python #what's the difference between a python module and a python package? #python package #python module

How to Install Pyenv on Ubuntu 18.04

What is Pyenv?
Pyenv is a fantastic tool for installing and managing multiple Python versions. It enables a developer to quickly gain access to newer versions of Python and keeps the system clean and free of unnecessary package bloat. It also offers the ability to quickly switch from one version of Python to another, as well as specify the version of Python a given project uses and can automatically switch to that version. This tutorial covers how to install pyenv on Ubuntu 18.04.

#tutorials #apt #debian #environment #git #github #linux #package #package management #package manager #personal package archive #ppa #pyenv #python #python 3 #python support #python-pip #repository #smb #software #source install #ubuntu #ubuntu 18.04 #venv #virtualenv #web application development

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services