Have you ever asked any programmer to explain the code he wrote a year ago? Or have you ever tried reading your own previously written code? It would become very time consuming and tiring if you had to reanalyze every block of code from scratch! The best thing to do is, add a comment.  Comments are not something just to increase the lines of your code but it is the best way to make your code meaningful. Here is a complete guide to help you understand all about Comments in Python.

Before moving ahead, let’s just have a quick walk through all the topics that have been covered in this article –

• What are Comments?
• How to make use of Comments?
• How does Python interpret Comments?
• Types of comments:
  1. Single-line
  2. Multi-line
• Docstrings Comments

What are Comments?

A comment, in general, is an expression of one’s ideas. In programming, comments are programmer-coherent statements, that describe what a block of code means. They get very useful when you are writing large codes. It’s practically inhuman to remember the names of every variable when you have a hundred-page program or so. Therefore, making use of comments will make it very easy for you, or someone else to read as well as modify the code.

Comments are very important, but you will need to know how to make use of them which is exactly what will be discussed in the following topic. 

How to make use of Comments?

Comments can be included anywhere which means inline as well. The best practice is to write relevant comments as and how you proceed with your code.  

 Here are some key points that will help you while commenting your code:

• Comments need to be short and relevant
• They are to be specific to the block of code they are included with
• Make sure to use decent language, as using foul language is unethical
• Don’t comment self-explanatory lines

Now that you know the importance of comments, let’s move ahead and see how to write Comments in Python.

How to write Comments in Python?

Comments in Python start with a # character. However, alternatively at times, commenting is done using docstrings(strings enclosed within triple quotes), which are described further in this article.

Example:

#Comments in Python start like this
print("Comments in Python start with a #")

Output: Comments in Python start with a #

As you can see in the above output, the print statement is executed whereas the comment statement is not present in the output.

 If you have more than one comment line, all of them need to be prefixed by a #.

Example:

#Comments in Python
#start with this character
print("Comments in Python")

Output: Comments in Python

The above output shows that all lines prefixed with a # character are not returned in the output.

Moving forward let’s see how comments are interpreted and why they never appear in the output.

How does Python interpret comments?

When the interpreter encounters a # symbol anywhere, (except inside a string because a # within a string just means #), it omits everything that is present after it until the end of that line. The # tag actually tells the interpreter to stop reading anything present after it.

Types of Comments

Comments can either be

• Single-line or
• Multi-line

Single-line Comments:

They can appear either in an individual line or inline with some other code.

Example:

#multiplying two variables          (this line starts with a #, hence will be ignored till line ends)
a=1
b=2
c=a*b
print(c)     # printing result      (inline comment, whatever is present after # will be ignored)

Output: 2

Multi-line Comments:

Multi-line comments appear in more than one line. All the lines to be commented are to be prefixed by a #. If you don’t do so, you will encounter an error.

Example:

#adding 2 variables
#pinting the result in a new variable
a=2
b=3
c=a+b
print(c)

Output: 5

The above output shows that the first two program lines being prefixed with a # character have been omitted and the rest of the program is executed returning its respective output.

You can also a very good shortcut method to comment multiple lines. All you need to do is hold the ctrl key and left click in every place wherever you want to include a # character and type a # just once. This will comment all the lines where you introduced your cursor.

If you want to remove # from multiple lines you can do the same thing and use the backspace key just once and all the selected # characters will be removed.

However, these multi-line comments look very unpleasant when you’re commenting documentation. The following topic will introduce you to a solution to this.

Docstring Comments:

Docstrings are not actually comments, but, they are documentation strings. These docstrings are within triple quotes. They are not assigned to any variable and therefore, at times, serve the purpose of comments as well.

They are used particularly when you need to affiliate some documentation related to a class or a function, etc.

Example:

"""
Using docstring as a comment.
This code divides 2 numbers
"""
x=8
y=4
z=x/y
print(z)

Output:  2.0

As you can see, the output does not contain the docstring, therefore, it has been omitted as it appears before the code has started.

But if you execute just a docstring without the code below, as shown above, the output will be the string itself.

Example:

"""
Using docstring as a comment.
This code divides 2 numbers
"""

Output: ‘\nUsing docstring as a comment.\nThis code divides 2 numbers\n’

In the above output, the docstring has been printed since it is not followed by any code.

Now, in case it would be present after the code was written, the docstring will still be printed after the result.

Example:

x=8
y=4
z=x/y
print(z)
"""
Using docstring as a comment.
This code divides 2 numbers
"""

Output: 

2.0
'\nUsing docstring as a comment.\nThis code divides 2 numbers\n'

As you can see, the docstring has been printed after the output. So, therefore, as mentioned above, docstring behaves differently at different places depending on where it appears in the code. This brings us to the end of this article. I hope you have enjoyed learning about Comments in Python. Make sure you practice as much as possible and revert your experience.  

Got a question for us? Please mention it in the comments section of this “Comments in Python” blog and we will get back to you as soon as possible.

To get in-depth knowledge on Python along with its various applications, you can enroll for live Python online training with 24/7 support and lifetime access.  

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In my previous article we discussed Azure DevOps and its components. This article would elaborate on one of those components known as Azure Boards. In the process we would explore how it helps to plan, track and also discuss work across different teams.

First, let us start by taking a look at the pointers to be discussed in this Azure Boards article:

1. What is Azure Boards
2. Azure Boards Features
3. Demo: Getting Started with Azure Boards

So let us start by understanding what the term actually means.

What is Azure Boards?

Well, you can think of it as an interface that lets you you track tasks, features, and even bugs that may be associated with your project. Here you are aided by three work item types:

• Epics
• Issues
• Tasks

Here is the depiction in the form of an image(Image Source: Microsoft.com):

Also as the work gets completed the status gets updated stage by stage from:

• To Do 
• Doing 
• Done

Here is the image below depicting the same (Image Source: Microsoft.com):

Each time we create or add an issue, task or an epic, it means we are creating a work item. Every work item we create would represent an object. This object is stored in the work item data store. Every item here will have an identifier, assigned to it. These IDs are unique to a particular project.

If you wish to track significant features or requirements you add epics. Issues on the other hand are used to track user stories, bugs, or other smaller items of work. Likewise tasks are meant for tracking little amounts of work. The tracker can be used for both hourly and daily tracking.

Now let us take a look at some of the features of Azure Boards.

Features Of Azure Boards

Kanban Implementation

It is very easy to implement and use Kanban with Azure Boards. It eases up two important tasks, those are:

• Update the status of issues
• Prioritize Your Backlog of Issues

This means assigning work becomes a lot more easier and you can share information better and prioritize your work with drag and drop features provided.

Collaborate using Azure Boards

• The Discussion section lets you collaborate better with the people in your project
• You can create dashboards and  track status and trends of the accomplished work. Get instant alerts for the issues created, changed or resolved
• You can set notifications to get alerted when an issue is created or changed

Flexibility to work in sprints and to implement Scrum

• It becomes easier to plan sprints with proper assignment
• You can use effort estimates to predict work
• It is possible to assign issues or tasks in bulk

Integrate with GitHub

It is easy to connect to GitHub and in turn it means:

• You can perform pull, push and commit requests with ease
• You can link to GitHub commits and pull requests with ease

Let us now move further and see how can we use Azure Boards to carry out some of the features we discussed above.

Demo: Getting Started with Azure Boards

Step1: Start by opening this link in your browser ‘https://aex.dev.azure.com/signup/boards’. You would be re-directed to this page. Click on Continue.

Step2: Next you would be asked to enter the organisation name and the location where you are operating from. Enter those and click on continue.

Step3: Next step is to create a project. Enter the details below and create a project.

Step4: You would be re-directed to following interface, You have four sections. The first one gives you access to all the major tabs of Azure Boards. Second one lets you add items. Third one is for issues and fourth one lets you manage project and project members.

Step5: Let us create an issue, to do that click on issues drop down on top left corner of your screen and select issue.

Step6: Click on Add Item and enter the name of the issue. Once you hit the enter button, the issue tab would appear as below.

Step7: We can change the state to Doing and your item would shift under the column named Doing.

Step8: You can add tasks to the issue. This bit covers what needs to be done. I have added three tasks and I have crossed one suggesting the task was complete.

So guys this was about adding a simple issue to your Azure boards. There is a lot that can be done with Azure boards. You are free to explore Azure Boards. As far as this article is concerned. I would be resting it here. In case if you like Cloud Computing and DevOps. You may want to check out this AWS Certified DevOps Engineer Training by Edureka which is catered to meet all the AWS and DevOps needs the learners may have.

In case of queries put those in the comment section and we revert at the earliest.

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Most systems are developed under the assumption of normal operating conditions. It is important to ensure that it doesn’t breakdown under heavy loads. Software Testing makes sure that the system would not crash under crunch situations. This Stress Testing using JMeter article will guide you on how to perform stress testing in the following sequence:

• Introduction to Performance Testing
• What is Stress Testing?
• Why do we need Stress Testing?
• Steps to perform Stress Testing
• Stress Testing Types & Tools
• Stress Testing using JMeter

Introduction to Performance Testing

Software testing provides the solution to all our problems about machines behaving the way we want them to. The different types of software testing help the tester determine the right choice of testing for the applications. 

Performance Testing is a type of software testing which ensures that the application is performing well under workload. It is further divided into six different types such as:

This article focuses on Stress Testing and we will have a look at how you can perform stress testing using JMeter.

What is Stress Testing?

Stress Testing is a type of Software Testing that verifies the stability & reliability of the system. This test mainly determines the system on its robustness and error handling under extremely heavy load conditions.

Stress Testing ensures that the system would not crash under crunch situations. It tests beyond the normal operating point and evaluates how the system works under those extreme conditions. It also checks whether the system demonstrates effective error management.

Why do We Need Stress Testing?

It is important to perform Stress Testing on a website whenever there is a spike or a sudden surge in traffic. If you fail to accommodate this sudden traffic, it may result in loss of revenue and repute. Stress Testing is also required for the following reasons:

• To check whether the system works under abnormal conditions.
• Display appropriate error message when the system is under stress.
• System failure under extreme conditions could result in enormous revenue loss.
• Prepare the website for extreme conditions by executing Stress Testing.

Steps to Perform Stress Testing

There are 5 steps that you need to follow while performing stress testing on any website:

1. Plan the Stress Test – In this step, you gather the system data, analyze the system and define the stress test goals.
2. Create Automation Scripts – Here, you need to create the Stress testing automation scripts and generate the test data for the stress scenarios.
3. Script Execution – In the third step, you run the Stress testing automation scripts and store the stress results.
4. Results Analysis – After storing the results, now you need to analyze the Stress Test results and identify bottlenecks.
5. Tweaking and Optimization – In the final step,  you fine-tune the system, change configurations, and optimize the code to meet the desired benchmark.

Now you know about the different steps involved in the process of stress testing. So, let’s move ahead and have a look at the different types of stress testing and the tools required.

Stress Testing Types & Tools

The different types of Stress Testing include:

• Distributed Stress Testing – In distributed client-server systems, you can perform testing across all clients from the server. The role of stress server is to distribute a set of stress tests to all stress clients and track on the status of the client.
• Application Stress Testing – This testing is used for finding defects related to data locking and blocking, network issues and performance bottlenecks in an application.

• Transactional Stress Testing – You need this testing to perform stress testing on a number of transactions between two or more applications. It is used for fine-tuning and optimizing the system.
• Systematic Stress Testing – This testing is integrated and can be tested across multiple systems running on the same server. You can also find defects where one application data blocks another application.
• Exploratory Stress Testing – This is used to test the system with unusual parameters or conditions that are unlikely to occur in a real scenario. You can find the defects in any critical situation.

These were some of the different types of stress testing. Now to perform these tests, you require certain tools. So, let’s have a look at some of the important tools used for stress testing.

Tools used for Stress Testing

Testing tools ensure that your applications are performing well in peak traffic and under extreme stress conditions. Below are some of the most commonly used stress/web testing tools:

• Apache JMeter – Jmeter is an Open Source testing tool. It is a pure Java application for stress and Performance Testing.
• LoadRunner – LoadRunner from HP is a widely-used Testing tool. Stress test results provided by  Loadrunner are considered as a benchmark.
• NeoLoad – This tool is used to simulate thousands of users in order to evaluate the application performance under stress and analyze the response times.

You might get confused because there are a number of tools available for stress testing. But JMeter is one of the most preferred tools for performing stress testing on websites. So, let’s have a look at the steps required in the process of Stress Testing using JMeter.

Stress Testing Using JMeter

I have already discussed about Apache JMeter in detail and also the steps involved in the installation of JMeter in my previous articles. So, let’s get started with the steps involved in performing stress testing.

Step 1 – First, you have to create your own Test Plan in JMeter.

Step 2 – Insert a Thread Group and add an HTTP request with the server name of your website on which you want to perform stress testing.

Step 3 – Next, you need to add a listener inside the thread group and view the test results. It will show the status of the test that has taken place.

Step 4 – Now you need to add a response assertion inside your thread group. It is one of the most important element because it helps you to assert the response of request in your software load test plan.

We will assert for response code 200 using response assertion. Response code 200 represents the success of your HTTP request. If you change the value from 200 to any other number, the status of the test will change to unsuccessful.

Now you can add a number of users or threads and increase the loop count. So this will increase the stress on your server. But You can continue performing the test and view results in order to check the status of your website under a certain amount of stress.

Now that you have understood what is stress testing, check out the Performance Testing Using JMeter Course by Edureka which is designed to introduce you to the complete software testing life-cycle. You will be learning different levels of testing, test environment setup, test case design technique, test data creation, test execution, bug reporting, CI/CD pipeline in DevOps, and other essential concepts of software testing. Got a question for us? Please mention it in the comments section of “Stress Testing using JMeter” and we will get back to you.

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The prime objective of any software project is to get a high-quality output while reducing the cost and the time required for completing the project. To achieve that companies subject software product to mainly four levels of testing. Unit testing is the first level of testing in Software Testing. Throughout this article, we will explore what unit test is in detail. If you are new to software testing, be sure to also read the Beginners’ Guide for Software Testing.

Let’s take a look at topics covered in this article:

• Levels of Software Testing
• What is Unit Testing?
• How do you Perform Unit Testing?
• What are the Benefits of Unit Testing?
• Demo: Writing a Sample Unit Test
• Best Unit Testing Frameworks

Levels of Software Testing

Software Testing is a phase within the software development cycle in which business-critical software is verified for correctness, quality, and performance.

There are four fundamental levels within software testing, each examining the software functionality from a unique vantage point within the development process. The four levels of software testing are as shown in the image below.

This article explains unit testing, the first level of software testing in detail.

What is Unit Testing?

Unit testing is a way of testing the smallest piece of code referred to as a unit that can be logically isolated in a system. It is mainly focused on the functional correctness of standalone modules.

A unit can be almost anything you want it to be – a specific piece of functionality, a program, or a particular method within the application. Smaller the unit, better it is. Smaller tests usually give you a much more granular view of your production, the code is performing. Also, your tests can run faster if they are small. So, it is also micro-level of software testing.

How Do You Perform Unit Testing?

The goal of unit testing to separate each part of the program and test that the individual parts are working correctly and as intended. While performing unit tests, application code functions are executed in a test environment with sample input. The output obtained is then compared with the expected output for that input. If they match the test passes. If not it is a failure. Unit tests are great for confirming the correctness of the code. Let’s take a look at a sample algorithm that illustrates the concept. 

As you can see, performing the unit test is quite simple. You write part of the code and subject it to test. If the test passes then you add it to your test suite and test the next part of the code. Else, you make the necessary changes and retest it again. Repeat the process until all the units of software are tested. This type of basic testing offers a lot of advantages like finding software bugs early, simplifying integration, providing a source of documentation, and many others.

What are the Benefits of Unit Testing?

Conducting regression tests, benefits companies in a number of ways such as:

Makes Coding Agile

Unit testing speeds up the coding process. When you add new features to your application, sometimes you might have to modify the design and code of your software product. However, changing already tested code costs too much money and effort. But with unit tests, you can just test the newly added piece of code instead of testing the entire program. Also, unit tests improve the quality of your code.

Helps Find Software Bugs Early

As unit tests are carried out by developers who test individual code before integration, issues can be found very early in the software testing process. They can be resolved then and there without impacting the other pieces of the code. The advantage of detecting errors early is that you can minimize development risks, and avoid spending too much money and time.

Provides Documentation

In testing, code documentation is often neglected since it requires a lot of time. But unit testing makes documentation a little easier by encouraging better coding practices and also leaving behind pieces of code that describe what your product is doing.

Debugging is Made Easier

Unit testing simplifies the debugging process. When a test fails, only the latest changes made in the code need to be debugged. At higher levels of testing changes made over the span of several days or weeks or months need to be scanned.

Reduces Testing Costs

Since the bugs are found early, the cost of bug fixes is reduced up to some extent. It would cost much more if a bug is found during the later stages of development. You will have to modify the entire code of your project. That sounds really tiring and waste of money. So performing unit testing saves precious time and money as well.

There you go! I hope you are convinced as to why unit testing is important. Moving further, let’s check out a simple demo on how to write unit tests.

Demo: Writing a Sample Unit Test

Unit testing demands that a good test should be:

• Easy to write
• Readable
• Reliable
• Faster & Efficient

Requirements for the demo:

• Java Development Kit(JDK)
• An IDE(In this demo Eclipse is used)
• Unit testing framework(In this demo TestNG is used)

Let’s get started with the demo. So, in this demo, I have two files:

• A Math class with a function to test
• A Test class with methods to perform testing

Take a look at the below code to understand the test case. It’s a Math class with two methods: add, multiply. 

public final class Math {

public static int add(int first, int second) 

{
  return first + second;
}

public static int multiply(int multiplicand, int multiplier) 

{
  return multiplicand * multiplier;
}

}

Next up we have a Test class with methods to test the functionality of the add() function and multiply() function.

import org.testng.annotations.Test;

import static org.testng.Assert.assertEquals;

public class MathTests {

@Test
public void add_TwoPlusTwo_ReturnsFour() {
final int expected = -4;

final int actual = Math.add(-2,-3);

assertEquals(actual, expected);
}
@Test
public void multiple_twonumbers_retursvalue() {
final int expected = -4;

final int actual = Math.multiply(2,2);
assertEquals(actual, expected);

}

}

Unit Test: Checking functionality of add function

Comment the multiply() function in Math class and multiple_twonumbers_retursvalue() function in Test class. Then assign value for the expected variable and call the multiply() function with sample input(consider both positive & negative cases). When you run the test, the expected value is compared with the actual value. If the test is returning the intended results, it means that add() function is working perfectly. I have attached a screenshot of test results when the expected value is -5 and the parameters passed to add() function are -2 and -3.

Simple right? We have tested a unit or part of the entire program. You can do the same with multiply() function. The purpose of this demo was to make you understand what a unit means in unit testing. So, the main objective of here is to verify the internal design and internal logic, internal paths of the software project in small chunks. The unit testing framework that I used in this demo is TestNG. There are various other unit testing frameworks for various programming languages.

Best Unit Testing Frameworks

Some of the popular unit testing frameworks are:

• JUnit: It is an open-source framework for a test-driven development environment, specifically designed for Java programming language.
• NUnit: It is one of the most popular unit-testing frameworks for C#.
• TestNG: Designed for Java programming language, it resembles JUnit and NUnit but offers some new functionalities which make it more powerful and easier to use.
• HtmlUnit: It is one of the most popular frameworks for JavaScript. It is used for testing web applications that are used within frameworks like JUnit and TestNG.
• Unitest: Inspired by the JUnit framework, this framework supports test automation and acts independently of the tests from the reporting environment. It is one of the most popular unit-testing frameworks for Python.

Apart from these, there are a lot of other frameworks. With this, we have reached the end of the blog. Hope the things that you have learned here today will help you as you head out on your software testing journey.

If you found this article relevant, check out the live-online Selenium Certification Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. 

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Every business runs on software and all the software needs to be tested before it is released to users. Software testing is vital to release efficient, effective and reliable software at a rapid pace. There are different types of software testing and each of these types has a different purpose and provides a unique value to the software development process. In this article, we will be discussing the two most frequently misunderstood types: smoke testing and sanity testing.

Let’s take a look at topics covered in this article:

• Types of Software Testing
• Smoke Testing
  • What is Smoke Testing?
  • How to Conduct Smoke Testing?
• Sanity Testing
  • What is Sanity Testing?
  • How to Conduct Sanity Testing?
• Smoke Testing vs Sanity Testing

Types of Software Testing

Software Testing is a crucial phase of the software development cycle. There are many different types of software testing. Each of these testing types has its own purpose. The type of software testing that you choose depends on your testing objective, the complexity, and functionality of your software, and your testing team. The image below lists out some of the most common types of software testing used today.

This article will help you figure out what smoke testing and sanity testing are and how different they are from each other. Let’s get started.

Smoke Testing

What is Smoke Testing?

Smoke Testing is a type of software testing which is usually performed on initial software builds to make sure that the critical functionalities of the program are working absolutely fine.

Not sure what software build is? A software build is a process by which source code is converted to a stand-alone form that can be run on any system. It is often referred to as a code build. Coming back to smoke testing, it is executed before any functional or any regression tests are executed in detail on the software build. The main objective here is to reject a badly broken application so that the QA team does not waste time installing and testing the software application. Instead of performing exhaustive testing, we make sure that critical functionalities are working fine.

How to Conduct Smoke Testing?

Smoke tests can be performed manually or they can be automated. To execute smoke tests, you don’t need to write new test cases, instead, you can just pick the necessary test cases that are already defined by programmers. The primary focus here is to test the core application workflow. So, just pick those test cases that define the main functionalities of your software. Let’s try to figure this out with a real-time example.

Let’s say, you are working on an e-commerce site. You have a few initial builds that are ready to be released for testing. First thing you need to do is to check if the core functionalities are working or not. So, you try to access the site and add an item to your cart to place an order. Well, that’s the major workflow of any e-commerce site, right? If this primary workflow works, you can say that the build that you have subjected to testing has passed the smoke test. You can then move on to next rounds of testing.

Now that you are clear with what smoke testing is, let’s get to the next topic of this article, that is sanity testing.

Sanity Testing

What is Sanity Testing?

Smoke Testing is a type of software testing which is conducted after receiving a software build, with minor changes in the code, or functionality. The aim is to make sure that the bugs have been fixed and to confirm that there are no further issues introduced due to the new changes.

Unlike smoke testing, the objective goal of sanity test is not to verify the core functionalities, instead, it is to verify the correctness and rationality of the software. It is usually done near the end of a test cycle, to ascertain if bugs have been fixed and if minor changes to the code are well tolerated. Also, to determine whether the most recent fixes have affected any component functionality. Sanity tests are often unscripted and can be performed manually, or with the help of automated tools.

How to Conduct Sanity Testing?

Similar to smoke testing, you don’t need to write new test cases unless a new feature is introduced. The main objective here is, to ensure that false results or bugs are not present in component processes. Also, to check whether the build is sane enough to move to further stages of software testing cycle. 

Let’s consider the same example that we used for smoke testing. So, you are working on an e-commerce site. A new feature related to user registration is released. Your main goal is to check if the new feature is working correctly or not. Once you are sure that it’s working as it is supposed to, then you move on to the next level of testing. If the sanity test fails, it is not reasonable to attempt more rigorous testing. In the sanity tests, you exercise the smallest subset of application functions needed to determine whether the application logic is functional and correct.

You might have noticed that there is some overlap between smoke testing and sanity testing, especially when it comes to the fact that neither is really designed to be a thorough process. However, there are also obvious and important differences between these two testing types. Let’s check them out.

Smoke Testing vs Sanity Testing

Smoke testing and sanity testing describe very different practices. But people still get them confused, since the distinction is somewhat subtle. The table below lists the key differences between smoke testing and sanity testing.

So, that’s it, guys! With this, we have reached the end of this article. Hopefully, by now, you have the basic knowledge of smoke testing and sanity testing. These are important testing types which ensure the detection of bugs and defects in early stages of the development cycle and I hope you will be using them well.

If you found this article relevant, check out the live-online Selenium Certification Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. 

Got a question for us? Please mention it in the comments section of this article and we will get back to you.

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Most of the projects fail because of poor stakeholder management as stakeholders are one of the key factors which play a huge role in deciding the success or failure of a project. In this article on Project Stakeholder Management, I will be giving you a complete insight into who exactly are these stakeholders and why a separate Knowledge Area has been dedicated for their management along with various process involved in it.

Below are the topics that I would be discussing in this article:

• What is Project Stakeholder Management?
• Need for Stakeholder Management
• Project Stakeholder Management Processes

If you wish to master the concepts of project management and become a certified project manager, you can check out our instructor-led PMP Certification Training where these topics are covered in depth.

What is Project Stakeholder Management?

Project Stakeholder Management is one of the ten Knowledge Areas of the project management framework that exclusively deals with the resources involved in a project. According to PMBOK^® Guide – Sixth Edition,

Project Stakeholder Management includes the processes required to identify the people, groups, or organizations that could impact or be impacted by the project, to analyze stakeholder expectations and their impact on the project, and to develop appropriate management strategies for effectively engaging stakeholders in project decisions and execution.

Project stakeholder management is the process where a project manager needs to form, monitor and maintain productive relationships with the investors involved in the project. This is mostly done by influencing the expectations of the stakeholders regarding the result gained from their initial investment in the project appropriately. It also helps a business towards its defined goals by preserving the existing investor’s satisfaction and furthermore, recruiting new investors as per requirement but in an ethical way.

Before explaining project stakeholder management in further detail, you must have a clear understanding of who a stakeholder is.

In any business, a stakeholder is normally an investor in a company or a project and plays a vital role in companies business decisions. A stakeholder need not be an equity holder if the company but can be a regular employee as well. To put it in simpler terms, stakeholders are of three types:

1. Internal Stakeholders: These are the people who own or work in the organization such as business partners, board members, and employees.
2. External Stakeholders: These are the people who are affected by the performance and the outcomes of a business such as local city government, community residents, nonprofits business sponsors, the trade media, etc.
3. Connected Stakeholders: This group includes people like shareholders, vendors, suppliers, retailers, contractors, customers, wholesalers, sales reps, distributors, etc.

To conclude this, you can say that a stakeholder is a person or a group of people who have an interest in your project and will be influenced by its deliverables/ output. Thus for a project manager, it becomes very important to understand the values and issues that stakeholders have in order to resolve. Addressing stakeholders issues are very necessary for avoiding any possible conflicts and ensuring that everyone remains satisfied until the completion of the project.

I hope this clears who is a stakeholder and what role he plays in an organization. Moving ahead, let’s now see with proper stakeholder management how a project can be benefitted.

Need for Project Stakeholder Management

1. Stakeholder Identification: With better identification and engagement of valuable stakeholders in your project, the implementation of the project will be more aligned to the project managers benefit.
2. Relationship Building: Stakeholder management helps in early relationship building with the stakeholders which leads them to engage earlier in the project. It is really helpful in building rapport with the public.
3. Fewer Surprises and better communication: Since everything is well discussed and communicated with the stakeholders, it reduces the chances of getting caught off guard and in turn reduces the number of iterations or modifications.
4. A better understanding of needs and concerns: With Stakeholder management, you get to communicate with each of the stakeholder involved in your project which helps in getting a clear cut view of their idea about the project and related concerns.
5. Better time and money investment: Stakeholder management keeps you in the loop with the stakeholders which results in continuous input and regular feedback from them. This ensures that the tasks that you are working on hold the highest value to the project.
6. Happier Stakeholders: Since you are keeping your stakeholders in a proper loop and with regular involvement, they will feel happy and satisfied.
7. Improved Reputation: Being a project manager it is very important to hold good rapport in the market. With good stakeholder management skills, you can relate well with people in their projects which in turn will help you in adding stars to your reputation as well.

Now that you are aware of various benefits and need for stakeholder management in a project, let’s now dive deeper to get a better insight on how actually it works internally.

Project Stakeholder Management Processes

Project Stakeholder Management knowledge area consists of four processes. They are:

1. Identify Stakeholders
2. Plan Stakeholder Engagement
3. Manage Stakeholder Management
4. Monitor Stakeholder Engagement

Identify Stakeholders

The initial process of project stakeholder management is Identify Stakeholders. In this process, the project stakeholders are regularly identified, analyzed and various information related to them such as their interests, involvement, interdependencies, influence, and potential impact on project success are documented. It is performed at periodic intervals throughout the project lifecycle which helps the project team in identifying appropriate focus required for the engagement of each stakeholder involved in the project.

Below I have listed down the various inputs, techniques, and outputs involved in this process of project stakeholder management:

Plan Stakeholder Engagement

The second process of project stakeholder management is Plan Stakeholder Engagement. In this process, various approaches are curated in order to involve the stakeholders on the basis of their needs, interests, expectations, and latent impact on the project. It is performed at periodic intervals throughout the project lifecycle and helps in developing a realistic plan which can effectively interact with the stakeholders.

In the below table, I have listed down various inputs, tools, and techniques and outputs involved in this process:

Manage Stakeholder Management 

Next process of this knowledge area is Manage Stakeholder Engagement. In this process, various steps are taken for better communication and maintain working with stakeholders so that by the end of the project all their needs and expectations are met. Along with this their concerns and issues are addressed and appropriate stakeholder involvement is fostered as well. It is performed throughout the project lifecycle and helps a project manager in increasing support and minimizing resistance from the stakeholders.

I have listed down the various inputs, tools & techniques and outputs involved in this process:

Monitor Stakeholder Engagement

Monitor Stakeholder Engagement is the final process of project stakeholder management Knowledge Area. As the name suggests, in this process relationships of project stakeholders are monitored and various strategies are tailored in order to engage the stakeholders using engagement plans and strategies. This process is performed throughout the project lifecycle and helps in maintaining as well as increasing the efficiency and effectiveness of the implied stakeholder engagement activities. This process is very crucial to perform while the project evolves and its development environment varies.

Various inputs, tools and techniques, and outputs involved in this process have been listed below:

This brings us to the end of this Project Stakeholder Management article. Hope it helped in adding value to your knowledge. If you wish to learn more about project management or project management certifications you can check my other articles as well.

If you found this “Project Stakeholder Management” article relevant, check out the PMP^® Certification Exam Training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. 

Got a question for us? Please mention it in the comments section of this Project Stakeholder Management article and we will get back to you.

The post Learn how to Perform an Effective Project Stakeholder Management appeared first on Edureka.

Software testing, in recent days, has reached the peaks of popularity and the growth of Automation testing using Selenium has added more wings to this transformation. As you all be might be aware that Selenium is the best tool for testing a website. But what is the very basic thing that you need for website testing? Well, Selenium provides few drivers that help you in creating a browser instance and perform testing. In this article, I will give you a brief insight into two of the important drivers which are ChromeDriver and GeckoDriver in Selenium.

Below is the list of topics that I will be covering in this article:

ChromeDriver

• What is ChromeDriver?
• Why do you need ChromeDriver?
• Setting Up ChromeDriver

GeckoDriver

• What is GeckoDriver?
• Why do you need GeckoDriver?
• Setting Up GeckoDriver

You may also go through this recording of ChromeDriver in Selenium by experts where you can understand the topics in a detailed manner with examples.

What is ChromeDriver?

WebDriver is an open source tool for automated testing of web apps across many browsers. It provides capabilities for navigating to web pages, user input, JavaScript execution, and many more. ChromeDriver is a standalone server which implements WebDriver’s wire protocol for Chromium. In order to instantiate the object of ChromeDriver, you can simply create the object with the help of below command.

Webdriver driver = New ChromeDriver();

Now, let’s move further in this ChromeDriver and GeckoDriver in Selenium article and understand why you need a ChromeDriver in Selenium.

Why do you need ChromeDriver?

The main purpose of the ChromeDriver is to launch Google Chrome. Without that, it is not possible to execute Selenium test scripts in Google Chrome as well as automate any web application. This is the main reason why you need ChromeDriver to run test cases on Google Chrome browser. 

Now that you know what is ChromeDriver and why do you need it, let’s move ahead and understand how to set up ChromeDriver in the system.

Setting Up ChromeDriver

Step 1: Navigate to the Selenium official website. Under third-party drivers, you will find all the drivers. Just click on Google ChromeDriver and choose the latest version and download it. Below image depicts the same.

Step 2: Based on your operating system, you can choose the preferred ChromeDriver that suits your operating system as shown in the below image. 

Step 3: Once the zip file is downloaded, you can unzip it in order to retrieve chromedriver.exe. executable file. Below image depicts the executable ChromeDriver application.

Step 4: After configuring ChromeDriver, you need to copy the path where you have saved a ChromeDriver to set the system properties of the driver.

Step 5: Now let’s move further and understand the Selenium script and see how ChromeDriver is useful in launching Google Chrome browser and executing the test cases.

package Edureka;
import java.util.concurrent.TimeUnit;
import org.openqa.selenium.By;
import org.openqa.selenium.chrome.ChromeDriver;
public class Chrome {
public static void main(String[] args) {
System.setProperty("webdriver.chrome.driver", "C:\\Selenium-java edureka\\chromedriver_win32\\chromedriver.exe"); // Setting system properties of ChromeDriver
WebDriver driver = new ChromeDriver(); //Creating an object of ChromeDriver
driver.manage().window().maximize();
driver.manage().deleteAllCookies();
driver.manage().timeouts().pageLoadTimeout(40, TimeUnit.SECONDS);
driver.manage().timeouts().implicitlyWait(30, TimeUnit.SECONDS);
driver.get("https://www.google.com/");
driver.findElement(By.name("q")).sendKeys("Edureka"); //name locator for text box
WebElement searchIcon = driver.findElement(By.name("btnK"));//name locator for google search
searchIcon.click();

In the above code, I have used System.set.property() to set the properties of the ChromeDriver and then created an object of ChromeDriver. This will help us to instantiate the Google Chrome browser and execute the test cases. So, I will start Google Chrome and navigate to google.com. Here, I will try to locate the search box using the name locator. On inspecting the web element you can see that it has an input tag and attributes like class and id. Next, I will copy the name of name locator and paste it in my Selenium script as shown in the above code. On executing the code, it will give you an automated search of Selenium. Basically, this is how it works. But the role of ChromeDriver basically is to launch Google Chrome browser.

This was all about ChromeDriver. I hope this helped you in gaining a few insights about ChromeDriver. Now let’s move further and learn the fundamentals of another driver that is widely used in the market, i.e. GeckoDriver.

What is GeckoDriver?

GeckoDriver is a web browser engine which is used in many applications developed by Mozilla Foundation and the Mozilla Corporation. GeckoDriver is the link between your tests in Selenium and the Firefox browser. GeckoDriver is a proxy for using W3C WebDriver-compatible clients to interact with Gecko-based browsers. In order to instantiate the object of GeckoDriver, you can simply create the object with the help of the below command.

Webdriver driver = New FirefoxDriver();

You may also go through this recording of GeckoDriver in Selenium by experts where you can understand the topics in a detailed manner with examples.

Why do you need GeckoDriver?

For Mozilla Firefox till version 47, we never needed GeckoDriver. But the Mozilla Firefox after version 47, comes with Marionette, which is an automation driver for Mozilla’s.

It can remotely control either the UI or the internal JavaScript of a Gecko platform, such as Firefox. So, you require GeckoDriver for FireFox. If you do not use it, you won’t be able to instantiate the object of GeckoDriver and launch Firefox. That is the reason why you need a GeckoDriver.

Now that you know what is GeckoDriver and why do you need it, let’s dive deeper in this ChromeDriver and GeckoDriver in Selenium article and learn how to set up GeckoDriver in the system.

Setting Up GeckoDriver

Step 1: Navigate to the Selenium official website. Under third-party drivers, you will find all the drivers. Just click on Mozilla  GeckoDriver and choose the latest version and download it. Below image depicts the same.

Step 2: Next, you will be redirected to GitHub where you will find options for GeckoDriver releases as depicted in the below image.

Step 3: Based on your operating system, you can choose the preferred ChromeDriver that suits your operating system as shown in the below image.

Step 4: Once the zip file is downloaded, you can unzip it in order to retrieve geckodriver.exe executable file.

Step 5: After configuring GeckoDriver, you need to copy the path where you have saved GeckoDriver to set the system properties of it.

Step 6: Now let’s move further and understand the Selenium script and see how GeckoDriver is useful in launching the Mozilla Firefox browser and executing the test cases.

package Edureka;
import java.util.concurrent.TimeUnit;
import org.openqa.selenium.By;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.WebElement;
import org.openqa.selenium.firefox.FirefoxDriver;

public class Example {
public static void main(String[] args) {
System.setProperty("webdriver.gecko.driver", "C:\\geckodriver-v0.23.0-win64\\geckodriver.exe");
WebDriver driver = new FirefoxDriver();
driver.manage().window().maximize();
driver.manage().deleteAllCookies();
driver.manage().timeouts().pageLoadTimeout(60, TimeUnit.SECONDS);
driver.manage().timeouts().implicitlyWait(50, TimeUnit.SECONDS);
String baseUrl = "https://www.edureka.co/";
String expectedTitle = "Instructor-Led Online Training with 24X7 Lifetime Support | Edureka";
String actualTitle = "";
// launch firfox and direct it to the Base URL
driver.get(baseUrl);
// get the actual value of the title
actualTitle = driver.getTitle();

/*compare the actual title of the page with the expected one and print
 the result as "Passed" or "Failed"*/

if (actualTitle.contentEquals(expectedTitle)){
System.out.println("Test Passed!");
} else {
System.out.println("Test Failed");
}
}
}

In the above code, I have used System.set.property() to set the properties of the GeckoDriver and then created an object of GeckoDriver. That will help us to instantiate the Mozilla Firefox browser and execute the test cases. So, I will launch the Mozilla Firefox and navigate to edureka.co website. Here, I will check whether the actual title matches with the expected title of the webpage or not manually. On executing the code, GeckoDriver will launch Mozilla Firefox browser and navigate to edureka.co website. In the backend, Selenium will implicitly verify if the actual title matches with the expected title or not. If it matches, then it will print Test Passed. Else, it will print Test Failed. This is how it works.

This was all about GeckoDriver. With this, we come to an end of this article on ChromeDriver and GeckoDriver in Selenium. I hope you understood the concepts and it added value to your knowledge.

Got a question for us? Please mention it in the comments section of ChromeDriver and GeckoDriver in Selenium article and we will get back to you.

The post What are ChromeDriver and GeckoDriver in Selenium? appeared first on Edureka.

With the increasing demand for automation testing, Selenium is one such tool which perfectly fits for Cross Browser Testing of a website. It is very necessary to check the compatibility and performance of the websites on different browsers and operating systems. So, this article on Cross Browser testing using Selenium will help you understand these concepts in depth.

Below are the topics covered in this article:

• What is Cross Browser Testing?
• Why do you need Cross Browser testing?
• How to perform Cross Browser testing?
• Demo using Selenium

What is Cross Browser Testing?

Cross-browser testing is nothing but testing the application in multiple browsers like IE, Chrome, Firefox so that we can test our application effectively. Cross-browser compatibility is the ability of a website or web application to function across different browsers and operating systems. 

For Example– Say you have 20 test cases to execute manually.  You can complete this task in a day or two. But, if the same test cases have to be executed in five browsers, then probably you will take a week to complete it. However, if you automate these 20 test cases and run them, then it will not take more than an hour or two depending on the test case complexity. So that’s where cross-browser testing comes into the picture.

Now, let’s move further and see why do you need Cross Browser Testing in Selenium.

Why do you need Cross Browser Testing?

Every website is comprised of three major technologies i.e. HTML5, CSS3, and JavaScript. However, there are n number of technologies in the backend like Python, Ruby, etc can be used. But, in the front end and in the rendering, only these three technologies are used.

Also, each browser uses a completely different rendering engine to compute these three technologies. For example, Chrome uses Blink, Firefox uses Gecko and IE uses edge HTML and Chakra,  because of which the same website would be rendered completely differently by all these different browsers. And that’s exactly why you need cross-browser testing. That means the website should work perfectly fine, in all the different browser versions and in different operating systems. So to ensure that it works fine, cross-browser testing is required.

Along with that, I have listed a few reasons that depict the need for Cross Browser Testing.

• Browser compatibility with different OS.
• Image orientation.
• Each browser has a different orientation of Javascript which can cause issue sometimes.
• Font size mismatch or not rendered properly.
• Compatibility with the new web framework.

Now let’s move further and understand how to perform Cross Browser Testing.

How to Perform Cross Browser Testing?

Cross-browser testing is basically running the same set of test cases multiple times on different browsers. This type of repeated task is best suited for automation. Thus, it’s more cost and time effective to perform this testing by using tools. Now let’s see how it is performed using selenium web driver.

Step1: If we are using Selenium WebDriver, we can automate test cases using Internet Explorer, FireFox, Chrome, Safari browsers.

Step 2: To execute test cases with different browsers in the same machine at the same time we can integrate TestNG framework with Selenium WebDriver.

Step3: Finally, you can write the test cases and execute the code.

Now, let’s see how to perform cross-browser testing of Edureka website on three different browsers

Demo using Selenium WebDriver

package co.edureka.pages;

import java.util.concurrent.TimeUnit;
import org.openqa.selenium.By;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.WebElement;
import org.openqa.selenium.chrome.ChromeDriver;
import org.openqa.selenium.edge.EdgeDriver;
import org.openqa.selenium.firefox.FirefoxDriver;
import org.testng.annotations.BeforeTest;
import org.testng.annotations.Parameters;
import org.testng.annotations.Test;

public class CrossBrowserScript {

WebDriver driver;

/**
* This function will execute before each Test tag in testng.xml
* @param browser
* @throws Exception
*/
@BeforeTest
@Parameters("browser")
public void setup(String browser) throws Exception{
//Check if parameter passed from TestNG is 'firefox'
if(browser.equalsIgnoreCase("firefox")){
//create firefox instance
System.setProperty("webdriver.gecko.driver", "C:\\geckodriver-v0.23.0-win64\\geckodriver.exe");
driver = new FirefoxDriver();
}

//Check if parameter passed as 'chrome'
else if(browser.equalsIgnoreCase("chrome")){
//set path to chromedriver.exe
System.setProperty("webdriver.chrome.driver", "C:\\Selenium-java-edureka\\New folder\\chromedriver.exe");
driver = new ChromeDriver();

}
else if(browser.equalsIgnoreCase("Edge")){
//set path to Edge.exe
System.setProperty("webdriver.edge.driver","C:\\Selenium-java-edureka\\MicrosoftWebDriver.exe");;span style="font-family: verdana, geneva, sans-serif; font-size: 14px;">//create Edge instance</span>
driver = new EdgeDriver();
}
else{
//If no browser passed throw exception
throw new Exception("Browser is not correct");
}
driver.manage().timeouts().implicitlyWait(10, TimeUnit.SECONDS);
}

@Test
public void testParameterWithXML() throws InterruptedException{
driver.get("https://www.edureka.co/");
WebElement Login = driver.findElement(By.linkText("Log In"));
//Hit login button
Login.click();
Thread.sleep(4000);
WebElement userName = driver.findElement(By.id("si_popup_email"));
//Fill user name
userName.sendKeys("your email id");
Thread.sleep(4000);
//Find password'WebElement password = driver.findElement(By.id("si_popup_passwd"));
//Fill password
password.sendKeys("your password");
Thread.sleep(6000);

WebElement Next = driver.findElement(By.xpath("//button[@class='clik_btn_log btn-block']"));
//Hit search button
Next.click();
Thread.sleep(4000);
WebElement search = driver.findElement(By.cssSelector("#search-inp"));
//Fill search box
search.sendKeys("Selenium");
Thread.sleep(4000);
//Hit search button

WebElement searchbtn = driver.findElement(By.xpath("//span[@class='typeahead__button']"));
searchbtn.click();
}
}

In the above code, I am performing actions on Edureka website like logging in to the website and searching for Selenium course. but, I want to check the cross-browser compatibility on three different browsers i.e Google Chrome, Mozilla Firefox, and Microsoft Edge. That’s why I have set the system properties of all the 3 browsers in my code. After that using locators I am performing actions on the website. So this is all about my class file. Now in order to execute the program, you need a TestNG XML file which contains the dependencies of the above class file. Below code depicts the TestNG file.

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE suite SYSTEM "http://testng.org/testng-1.0.dtd">
<suite name="TestSuite" thread-count="2" parallel="tests" >
<test name="ChromeTest">
<parameter name="browser" value="Chrome"/>
<classes>
<class name="co.edureka.pages.CrossBrowserScript">
</class>
</classes>
</test>
<test name="FirefoxTest">
<parameter name="browser" value="Firefox" />
<classes>
<class name="co.edureka.pages.CrossBrowserScript">
</class>
</classes>
</test>
<test name="EdgeTest">
<parameter name="browser" value="Edge" />
<classes>
<class name="co.edureka.pages.CrossBrowserScript">
</class>
</classes>
</test>
</suite>

In the above XML file, I am specifying different classes for the drives so that it will help us in instantiating the browsers to execute the test cases on the website. That’s how it works.

With this, we come to an end of this article on Cross Browser Testing using Selenium Webdriver. I hope you understood the concepts and it added value to your knowledge.

Got a question for us? Please mention it in the comments section of Cross Browser Testing using Selenium article and we will get back to you.

The post Know How to Perform Cross Browser Testing Using Selenium appeared first on Edureka.

Python language is one of the most popular programming languages. While learning python is seemingly easy, there are certain core concepts that must be mastered before moving on with various applications of python. Operators in python is one of the core fundamental concept in python. This blog will help you understand the different types of operators in python. Following are the topics covered in this blog:

• What Is An Operator?
• Types Of Operators
  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Membership Operators
  • Identity Operators 
  • Bitwise Operators

What Is An Operator?

Operators in python are used for operations between two values or variables. The output varies according to the type of operator used in the operation. We can call operators as special symbols or constructs to manipulate the values of the operands. Suppose if you want to perform addition of two variables or values, you can use the addition operator for this operation. The values in the operands can be  variable or any data type that we have in python.

Depending upon the type of operations there are 7 types of operators in python programming language.

Types of Operators

1. Arithmetic operators
2. Assignment operators 
3. Comparison operators
4. Logical operators
5. Membership operators
6. Identity operators
7. Bitwise operators

Arithmetic operators

Arithmetic operators are used to perform arithmetic calculations in python. Below are the arithmetic operators with names and their symbols. These are the symbols that we use while doing an arithmetic operation in python.

x = 10
y = 15
#addition
x + y
#subtraction
x - y
#multiplication
x * y
#division
x / y
#floor division
x // y
#modulus
x % y
#exponentiation
x ** y

Assignment operators

Assignment operators are used to assign values to the variables or any other object in python. Following are the assignment operators that we have in python.

x = 10
x += 5
#it is same as x = x + 5
x -= 5
x *= 5
x /= 5
#similarly we can write all assignment operators like this.

Comparison operators

Comparison operators are used to compare two values. Following are the comparison operators that we have in python.

x = 5
y = 3

#equal
x == 5

#not equal
x != 5

#greater than
x > y

#less than
x < y #greater than or equal to x >= y

#less than or equal to
x <= y

Logical operators

Logical operators are used to compare two conditional statements. Following are the logical operators that we have in python.

#logical and
5 > 3 and 5 > 4
#it will return true, since both statements are true.
5 > 3 or 5 < 2
#it will return true, since one of the statements is true.
not(5 < 2 and 5 > 3)
#it will return true, even when logical and will return false.

Identity operators

Identity operators compare two objects. Following are the identity operators that we have in python.

a = [10,20,30]
b = [10,20,30]
x = b
z = a
# is operator
x is a
#this will return false
x is z
#this will return true.
a is b
#this will return false, even though both have the same items in the list.
a is not b
#this will return true, since both are not same objects.

Membership operators

Membership operators are used to check if a sequence is present in an object. Following are the membership operators that we have in python.

a = [10,20,30,'edureka']
#in operator
'edureka' in a
#this will return true, since the item is present in the object.
'python' in a
#this will return false, since it is not present in a.
10 not in a
#this will return false, because it is there.
50 not in a
#this will return true, since there is no 50 in a.

Bitwise operators

Bitwise operators compare the binary values. Following are the bitwise operators that we have in python.

#bitwise AND
10 & 12
#this will return 8
#bitwise OR
10 | 12
#this will return 14
#bitwise XOR
10 ^ 12
#this will return 6
#bitwise NOT
~ (10 & 12)
#this will return -9
#left shift
10 << 2 #this will return 40 #right shift 10 >> 2
#this will return 2

To understand how we got the result using the bitwise operators lets take a look at the binary equivalent of 10 and 12.

10 in binary is 1010 and 12 in binary is 1100. When doing an AND operation between 1010 and 1100, the bit will be 1 if both the bits are 1. Therefore, the resultant binary equivalent will be 1000 which is 8 when we convert it to decimal.

Bitwise OR operator will set each bit to 1 if one of the bits is 1, bitwise XOR will set each bit to 1 if only one of the bits is 1 and bitwise not will invert all bits.

When doing a left shift or a right shift, the bits will shift left 2 places in our example. Therefore 1010 will become 101000 which is 40. Similarly, when doing right shift 1010 will become 10, which is 2.

In this blog, we have discussed different types of operators in python. This topic is a fundamental concept for learning python programming language. It is a core python concept that is necessary while moving to various other domains in python. If you are looking for a structured learning approach towards python programming, you can enroll for Edureka’s Python Certification Program to kick-start your learning.

If you have any queries, mention them in the comments section. We will get back to you. 

The post Operators In Python – All You Need To Know appeared first on Edureka.

Linux users and administrators can’t really live by the GUI alone. By only learning how to work with your tool, can you get the most out of Linux. Hence, we’ve brought together a list of useful Linux commands into this convenient guide, which will be of help no matter which Linux Curriculum you choose to learn from. 

So, I’ve categorized these commands into the following segments;

• Linux Basic Commands 
• Commands for Working with Files
• Commands for Working with Directories
• Commands for Working with User Permissions
• Commands for Working with Zipped Files
• Working with Secure Shell For Remote Machine Access

Linux Commands: Basic Commands 

Linux provides a CLI (Command Line Interface) to communicate with the OS. Here are the most basic of the Linux Commands.

1. pwd

This command Displays the current working directory of the terminal.

syntax:

$ pwd

2. echo

This command writes its arguments to standard output.

syntax:

$ echo "<text>"

3. su

This command is used to switch to root-user so that superuser permissions can be used to execute commands.

syntax:

$ su

4. su <username>

This command is used to switch to a different user whose name is passed as the argument.

syntax:

$ su <username>

5. sudo

This command executes only that command with root/ superuser privileges.

syntax:

$ sudo <command>

6. clear

This command is used to clear the terminal screen. Contents will not actually be deleted in this case, only scrolled down. You can also clear the screen by pressing Ctrl+L on the keyboard.

syntax:

$ clear

Linux Commands: Working with Files

7. cp 

This command copies files and directories. A copy of the file/directory copied, still remains in the working directory.

syntax:

$ cp <flag> {filename} /pathname/

8. mv

This command moves files and directories from one directory to another. The file/directory once moved, is deleted from the working directory. 

syntax:

$ mv <flag> {filename} /pathname/

9. rm

This command removes files from a directory. By default, the rm command does not remove directories. Once removed, the contents of a file cannot be recovered.

syntax:

$ rm <flag> {filename} 

10. grep

This command is used to search for a particular string/ word in a text file. This is similar to “Ctrl+F”, but executed via a CLI.

syntax:

$ grep <flag or element_to_search> {filename}

11. cat

This command can read, modify or concatenate text files. It also displays file contents.

syntax:

$ cat <flag> {filename}

Linux Commands: Working with Directories

12. ls

This command lists all the contents in the current working directory.

syntax:

$ ls <flag>


13. cd

This command is used to change the current working directory of the user.

syntax:

$ cd /pathname/

14. sort 

This command sorts the results of a search either alphabetically or numerically. Files, file contents and directories can be sorted using this command.

syntax:

$ sort <flag> {filename}

15. mkdir

This command is used to create a new directory.

syntax:

$ mkdir <flag> {directoryname} /pathname/

16. rmdir

This command is used to remove a specified directory. Although by default, it can only remove an empty directory, there are flags which can be deployed to delete the non-empty directories as well.

syntax:

$ rmdir <flag> {directoryname} 

Linux Commands: Working with User Permissions

17. chmod

This command is used to change the access permissions of files and directories. Consider the example below.

On trying to run the newly created file named chmodtest.sh, an error is thrown. After modifying the permissions of the file using the said Linux command, it turns executable.

syntax:

$ chmod <permissions of user,group,others> {filename}

The permissions associated with each digit is as follows.

Linux Commands: Installing Packages

Stable versions of most software’s will already be available in Linux repositories. Here are the Linux Commands to install them.

18. install packages

For an RHEL based system;

syntax:

$ sudo yum install package-name

For a Debian based system;

syntax:

$ sudo apt-get install package-name

For a Fedora based system;

syntax:

$ sudo dnf install package-name

Linux Commands: Working with Zipped Files

When you download a package from the internet, the downloaded file comes in compressed form. Here are a few commands to decompress and compress files in Linux.

19. tar

The following command is used to zip files of .tar format.

syntax:

$ tar –cvf tar-filename source-folder-name

The following command is used to unzip files of .tar format.

syntax:

$ tar –xvf tar-file-name

Linux Commands: Working with Secure Shell For Remote Machine Access

20. ssh

This command refers to a cryptographic network protocol for operating network services securely over an unsecured network. Typical use-cases include remote command-line execution, but any network service can be secured with SSH.

The following command, on running at the slave node, will give remote access to the master.

syntax:

$ ssh <master's ip>

The following command, on running at the master, will give remote access to the slave node.

syntax:

$ ssh <slave's ip>

So, there you have it. All the Linux commands you’re sure to use in your day-to-day IT-life.

Want to know more about the Commands in Linux? You could log on to www.edureka.co/linux-admin. Edureka’s Linux Administration Certification training is curated to shape your career as a Linux professional & help you to run applications, perform desired functions on your system and networks, create a network configuration, and maintain security administration.

The post 20 Linux Commands You’ll Actually Use In Your Life appeared first on Edureka.

Image Classification a task which even a baby can do in seconds, but for a machine, it has been a tough task until the recent advancements in Artificial Intelligence and Deep Learning. Self-driving cars can detect objects and take required action in real-time and most of this is possible because of TensorFlow Image Classification. In this article, I’ll guide you through the following topics:

• What is TensorFlow?
• What is Image Classification?
• TensorFlow Image Classification: Fashion MNIST
• CIFAR 10: CNN

What is TensorFlow?

TensorFlow is Google’s Open Source Machine Learning Framework for dataflow programming across a range of tasks. Nodes in the graph represent mathematical operations, while the graph edges represent the multi-dimensional data arrays communicated between them.

Tensors are just multidimensional arrays, an extension of 2-dimensional tables to data with a higher dimension. There are many features of Tensorflow which makes it appropriate for Deep Learning and it’s core open source library helps you develop and train ML models.

What is Image Classification?

The intent of Image Classification is to categorize all pixels in a digital image into one of several land cover classes or themes.  This categorized data may then be used to produce thematic maps of the land cover present in an image.

Now Depending on the interaction between the analyst and the computer during classification, there are two types of classification:

• Supervised &
• Unsupervised

So, without wasting any time let’s jump into TensorFlow Image Classification. I have 2 examples: easy and difficult. Let’s proceed with the easy one.

TensorFlow Image Classification: Fashion MNIST

Importing Fashion MNIST

Here we are going to use Fashion MNIST Dataset, which contains 70,000 grayscale images in 10 categories. We will use 60000 for training and the rest 10000 for testing purposes. You can access the Fashion MNIST directly from TensorFlow, just import and load the data.

• Let’s import the libraries first

from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras

import tensorflow as tf
from tensorflow import keras

# Helper libraries

import numpy as np
import matplotlib.pyplot as plt

• Let’s load the data

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

• Next, we are going to map the images into classes

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat','Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

• Exploring the data

train_images.shape
#Each Label is between 0-9

train_labels
test_images.shape

• Now, it’s time to pre-process the data.

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
#If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255.

• We have to scale the images from 0-1 to feed it into the Neural Network

train_images = train_images / 255.0

test_images = test_images / 255.0

• Let’s display some images.

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

• Setup the layers

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

• Compile the Model

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

• Model Training

model.fit(train_images, train_labels, epochs=10)

• Evaluating Accuracy

test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)

• Making Predictions

predictions = model.predict(test_images)

predictions[0]

• A prediction is an array of 10 numbers. These describe the “confidence” of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value.

np.argmax(predictions[0])
#Model is most confident that it's an ankle boot. Let's see if it's correct

Output: 9

test_labels[0]

Output: 9

• Now, it’s time to look at the full set of 10 channels

def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img, cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'green'
  else:
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array[i], true_label[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('green')

• Let’s look at the 0th and 10th image first

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()

i = 10
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()

• Now, let’s plot several images and their predictions. Correct ones are green, while the incorrect ones are red.

num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions, test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions, test_labels)
plt.show()

• Finally, we will use the trained model to make a prediction about a single image.

[]

# Grab an image from the test dataset
img = test_images[0]

print(img.shape)

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

predictions_single = model.predict(img) 
print(predictions_single)

plot_value_array(0, predictions_single, test_labels)
plt.xticks(range(10), class_names, rotation=45)
plt.show()

• As you can see the prediction for our only image in batch.

prediction_result = np.argmax(predictions_single[0])

Output: 9

CIFAR-10: CNN

The CIFAR-10 dataset consists of airplanes, dogs, cats, and other objects. You’ll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. This use-case will surely clear your doubts about TensorFlow Image Classification.

• Downloading the Data

from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm 
import tarfile

cifar10_dataset_folder_path = 'cifar-10-batches-py'

class DownloadProgress(tqdm):
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

""" 
    check if the data (zip) file is already downloaded
    if not, download it from "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" and save as cifar-10-python.tar.gz
"""
if not isfile('cifar-10-python.tar.gz'):
    with DownloadProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
        urlretrieve(
            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
            'cifar-10-python.tar.gz',
            pbar.hook)

if not isdir(cifar10_dataset_folder_path):
    with tarfile.open('cifar-10-python.tar.gz') as tar:
        tar.extractall()
        tar.close()

• Importing Necessary Libraries

import pickle
import numpy as np
import matplotlib.pyplot as plt

• Understanding the Data

The original batch of Data is 10000×3072 tensor expressed in a numpy array, where 10000 is the number of sample data. The image is colored and of size 32×32. Feeding can be done either in a format of (width x height x num_channel) or (num_channel x width x height). Let’s define the labels.

def load_label_names():
    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

• Reshaping the Data

We are going to reshape the data in two stages

Firstly, divide the row vector (3072) into 3 pieces. Each piece corresponds to each channel. This results in (3 x 1024) dimension of a tensor. Then Divide the resulting tensor from the previous step with 32.  32 here means the width of an image. This results in (3x32x32).

Secondly, we have to transpose the data from (num_channel, width, height) to (width, height, num_channel).  For that, we are going to use the transpose function.

def load_cfar10_batch(cifar10_dataset_folder_path, batch_id):
    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:
        # note the encoding type is 'latin1'
        batch = pickle.load(file, encoding='latin1')
        
    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    labels = batch['labels']
        
    return features, label

• Exploring the Data

def display_stats(cifar10_dataset_folder_path, batch_id, sample_id):
    features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id)
    
    if not (0 <= sample_id < len(features)):
        print('{} samples in batch {}.  {} is out of range.'.format(len(features), batch_id, sample_id))
        return None

    print('\nStats of batch #{}:'.format(batch_id))
    print('# of Samples: {}\n'.format(len(features)))
    
    label_names = load_label_names()
    label_counts = dict(zip(*np.unique(labels, return_counts=True)))
    for key, value in label_counts.items():
        print('Label Counts of [{}]({}) : {}'.format(key, label_names[key].upper(), value))
    
    sample_image = features[sample_id]
    sample_label = labels[sample_id]
    
    print('\nExample of Image {}:'.format(sample_id))
    print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))
    print('Image - Shape: {}'.format(sample_image.shape))
    print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label]))
    
    plt.imshow(sample_image)

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import numpy as np

# Explore the dataset
batch_id = 3
sample_id = 7000
display_stats(cifar10_dataset_folder_path, batch_id, sample_id)

• Implementing Preprocessing Functions

We are going to Normalize the data via Min-Max Normalization. This simply makes all x values to range between 0 and 1.
y = (x-min) / (max-min)

def normalize(x):
    """
        argument
            - x: input image data in numpy array [32, 32, 3]
        return
            - normalized x 
    """
    min_val = np.min(x)
    max_val = np.max(x)
    x = (x-min_val) / (max_val-min_val)
    return x

• One-Hot Encode

def one_hot_encode(x):
    """
        argument
            - x: a list of labels
        return
            - one hot encoding matrix (number of labels, number of class)
    """
    encoded = np.zeros((len(x), 10))
    
    for idx, val in enumerate(x):
        encoded[idx][val] = 1
    
    return encoded

• Preprocess and Save the Data

def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename):
    features = normalize(features)
    labels = one_hot_encode(labels)

    pickle.dump((features, labels), open(filename, 'wb'))


def preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode):
    n_batches = 5
    valid_features = []
    valid_labels = []

    for batch_i in range(1, n_batches + 1):
        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)
        
        # find index to be the point as validation data in the whole dataset of the batch (10%)
        index_of_validation = int(len(features) * 0.1)

        # preprocess the 90% of the whole dataset of the batch
        # - normalize the features
        # - one_hot_encode the lables
        # - save in a new file named, "preprocess_batch_" + batch_number
        # - each file for each batch
        _preprocess_and_save(normalize, one_hot_encode,
                             features[:-index_of_validation], labels[:-index_of_validation], 
                             'preprocess_batch_' + str(batch_i) + '.p')

        # unlike the training dataset, validation dataset will be added through all batch dataset
        # - take 10% of the whold dataset of the batch
        # - add them into a list of
        #   - valid_features
        #   - valid_labels
        valid_features.extend(features[-index_of_validation:])
        valid_labels.extend(labels[-index_of_validation:])

    # preprocess the all stacked validation dataset
    _preprocess_and_save(normalize, one_hot_encode,
                         np.array(valid_features), np.array(valid_labels),
                         'preprocess_validation.p')

    # load the test dataset
    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:
        batch = pickle.load(file, encoding='latin1')

    # preprocess the testing data
    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
    test_labels = batch['labels']

    # Preprocess and Save all testing data
    _preprocess_and_save(normalize, one_hot_encode,
                         np.array(test_features), np.array(test_labels),
                         'preprocess_training.p')

preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)

• Checkpoint

import pickle

valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))

• Building the Network

The entire model consists of 14 layers in total.

import tensorflow as tf

def conv_net(x, keep_prob):
    conv1_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], mean=0, stddev=0.08))
    conv2_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 128], mean=0, stddev=0.08))
    conv3_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 128, 256], mean=0, stddev=0.08))
    conv4_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 256, 512], mean=0, stddev=0.08))

    # 1, 2
    conv1 = tf.nn.conv2d(x, conv1_filter, strides=[1,1,1,1], padding='SAME')
    conv1 = tf.nn.relu(conv1)
    conv1_pool = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    conv1_bn = tf.layers.batch_normalization(conv1_pool)

    # 3, 4
    conv2 = tf.nn.conv2d(conv1_bn, conv2_filter, strides=[1,1,1,1], padding='SAME')
    conv2 = tf.nn.relu(conv2)
    conv2_pool = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')    
    conv2_bn = tf.layers.batch_normalization(conv2_pool)
  
    # 5, 6
    conv3 = tf.nn.conv2d(conv2_bn, conv3_filter, strides=[1,1,1,1], padding='SAME')
    conv3 = tf.nn.relu(conv3)
    conv3_pool = tf.nn.max_pool(conv3, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')  
    conv3_bn = tf.layers.batch_normalization(conv3_pool)
    
    # 7, 8
    conv4 = tf.nn.conv2d(conv3_bn, conv4_filter, strides=[1,1,1,1], padding='SAME')
    conv4 = tf.nn.relu(conv4)
    conv4_pool = tf.nn.max_pool(conv4, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    conv4_bn = tf.layers.batch_normalization(conv4_pool)
    
    # 9
    flat = tf.contrib.layers.flatten(conv4_bn)  

    # 10
    full1 = tf.contrib.layers.fully_connected(inputs=flat, num_outputs=128, activation_fn=tf.nn.relu)
    full1 = tf.nn.dropout(full1, keep_prob)
    full1 = tf.layers.batch_normalization(full1)
    
    # 11
    full2 = tf.contrib.layers.fully_connected(inputs=full1, num_outputs=256, activation_fn=tf.nn.relu)
    full2 = tf.nn.dropout(full2, keep_prob)
    full2 = tf.layers.batch_normalization(full2)
    
    # 12
    full3 = tf.contrib.layers.fully_connected(inputs=full2, num_outputs=512, activation_fn=tf.nn.relu)
    full3 = tf.nn.dropout(full3, keep_prob)
    full3 = tf.layers.batch_normalization(full3)    
    
    # 13
    full4 = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=1024, activation_fn=tf.nn.relu)
    full4 = tf.nn.dropout(full4, keep_prob)
    full4 = tf.layers.batch_normalization(full4)        
    
    # 14
    out = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=10, activation_fn=None)
    return out

• Hyperparameters

epochs = 10
batch_size = 128
keep_probability = 0.7
learning_rate = 0.001

logits = conv_net(x, keep_prob)
model = tf.identity(logits, name='logits') # Name logits Tensor, so that can be loaded from disk after training

# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')

• Train the Neural Network

#Single Optimization
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
    session.run(optimizer, 
                feed_dict={
                    x: feature_batch,
                    y: label_batch,
                    keep_prob: keep_probability
                })

#Showing Stats
def print_stats(session, feature_batch, label_batch, cost, accuracy):
    loss = sess.run(cost, 
                    feed_dict={
                        x: feature_batch,
                        y: label_batch,
                        keep_prob: 1.
                    })
    valid_acc = sess.run(accuracy, 
                         feed_dict={
                             x: valid_features,
                             y: valid_labels,
                             keep_prob: 1.
                         })
    
    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))

• Fully Training and Saving the Model

def batch_features_labels(features, labels, batch_size):
    """
    Split features and labels into batches
    """
    for start in range(0, len(features), batch_size):
        end = min(start + batch_size, len(features))
        yield features[start:end], labels[start:end]

def load_preprocess_training_batch(batch_id, batch_size):
    """
    Load the Preprocessed Training data and return them in batches of <batch_size> or less
    """
    filename = 'preprocess_batch_' + str(batch_id) + '.p'
    features, labels = pickle.load(open(filename, mode='rb'))

    # Return the training data in batches of size <batch_size> or less
    return batch_features_labels(features, labels, batch_size)

#Saving Model and Path
save_model_path = './image_classification'

print('Training...')
with tf.Session() as sess:
    # Initializing the variables
    sess.run(tf.global_variables_initializer())
    
    # Training cycle
    for epoch in range(epochs):
        # Loop over all batches
        n_batches = 5
        for batch_i in range(1, n_batches + 1):
            for batch_features, batch_labels in load_preprocess_training_batch(batch_i, batch_size):
                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
                
            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')
            print_stats(sess, batch_features, batch_labels, cost, accuracy)
            
    # Save Model
    saver = tf.train.Saver()
    save_path = saver.save(sess, save_model_path)

Now, the important part of Tensorflow Image Classification is done. Now, it’s time to test the model.

• Testing the Model

import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer

def batch_features_labels(features, labels, batch_size):
    """
    Split features and labels into batches
    """
    for start in range(0, len(features), batch_size):
        end = min(start + batch_size, len(features))
        yield features[start:end], labels[start:end]

def display_image_predictions(features, labels, predictions, top_n_predictions):
    n_classes = 10
    label_names = load_label_names()
    label_binarizer = LabelBinarizer()
    label_binarizer.fit(range(n_classes))
    label_ids = label_binarizer.inverse_transform(np.array(labels))

    fig, axies = plt.subplots(nrows=top_n_predictions, ncols=2, figsize=(20, 10))
    fig.tight_layout()
    fig.suptitle('Softmax Predictions', fontsize=20, y=1.1)

    n_predictions = 3
    margin = 0.05
    ind = np.arange(n_predictions)
    width = (1. - 2. * margin) / n_predictions
   
    for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)):
        if (image_i < top_n_predictions):
            pred_names = [label_names[pred_i] for pred_i in pred_indicies]
            correct_name = label_names[label_id]
            
            axies[image_i][0].imshow((feature*255).astype(np.int32, copy=False))
            axies[image_i][0].set_title(correct_name)
            axies[image_i][0].set_axis_off()

            axies[image_i][1].barh(ind + margin, pred_values[:3], width)
            axies[image_i][1].set_yticks(ind + margin)
            axies[image_i][1].set_yticklabels(pred_names[::-1])
            axies[image_i][1].set_xticks([0, 0.5, 1.0])

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import tensorflow as tf
import pickle
import random

save_model_path = './image_classification'
batch_size = 64
n_samples = 10
top_n_predictions = 5

def test_model():
    test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
    loaded_graph = tf.Graph()

    with tf.Session(graph=loaded_graph) as sess:
        # Load model
        loader = tf.train.import_meta_graph(save_model_path + '.meta')
        loader.restore(sess, save_model_path)

        # Get Tensors from loaded model
        loaded_x = loaded_graph.get_tensor_by_name('input_x:0')
        loaded_y = loaded_graph.get_tensor_by_name('output_y:0')
        loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
        
        # Get accuracy in batches for memory limitations
        test_batch_acc_total = 0
        test_batch_count = 0
        
        for train_feature_batch, train_label_batch in batch_features_labels(test_features, test_labels, batch_size):
            test_batch_acc_total += sess.run(
                loaded_acc,
                feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})
            test_batch_count += 1

        print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))

        # Print Random Samples
        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
        random_test_predictions = sess.run(
            tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
            feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
        display_image_predictions(random_test_features, random_test_labels, random_test_predictions, top_n_predictions)


test_model()

Output: Testing Accuracy: 0.5882762738853503

Now, if you train your neural network for more epochs or change the activation function, you might get a different result that might have better accuracy.

So, with this, we come to an end of this TensorFlow Image Classification article. I’m sure you can now use the same to classify any sort of images and you’re not a beginner to image classification.

Edureka’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM), Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

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What is Automation? How to automate tasks using RPA Tools? What are the Automation Anywhere Examples?

I believe you might have wondered the answers to these questions. Well, with UiPath, Blue Prism or Automation Anywhere, you can easily automate multiple tasks with few action commands. In this article on Automation Anywhere Examples, I am going to discuss the top automation examples that will help you automate tedious tasks. This will help you upskill your career as an RPA Certified Professional and standout in today’s market.

The following topics will be covered in this article:

• • What is Automation Anywhere?
  • Automation Anywhere Features
  • Examples of Automation Anywhere
  • Basic Automation Anywhere Commands
    
    • Windows Actions
    • Mouse Clicks
    • String Operations
    • Files & Folders
    • Web Recorders
    • Optical Character Recognition (OCR)
    • KeyStrokes
    • Rest Web Services
  • Complex Examples
    
    • Excel Automation
    • PDF Automation

So let’s get started!

What is Automation Anywhere?

Automation Anywhere is an RPA tool which aims to provide its users with scalable, secure and resilient services. It offers better performance as it has the ability to integrate into multiple platforms and also scale simultaneously. You can also summarize this tool as a summation of Robotic Process Automation, Cognitive Analytics, and Workforce Analytics. Refer to the image below.

Apart from this, Automation Anywhere has recently launched a Community Edition / Free Trial version which is free for lifetime and offers an Enterprise Edition which you can use with a 30 Day Free Trial.

Now, next in this article on Automation Anywhere Examples, let us look into the features of Automation Anywhere.

Automation Anywhere Features

The features of Automation Anywhere are as you can refer from the below image:

Now, that you know what is Automation Anywhere, let us next look into the various automation examples.

Examples of Automation Anywhere

For your better understanding, I have divided the examples to be shown in the following two sections:

• Basic Automation Anywhere Commands
• Complex Examples
  

Basic Automation Anywhere Commands

This section of the article on Automation Anywhere Examples will consist of the basic commands and different kinds of automation which will help you learn the tool better.

So, let us get started.

Windows Actions

The Windows Actions are used to automate tasks such as opening/closing/minimizing/maximizing a window and even getting the Active Window Title.

Problem Statement:

To automate the action of getting the title of an active window.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag the Get Active Window Title command into your workspace.

Step 2: Assign a variable. Here, I will assign the Clipboard system variable. Click on Save.

Step 3: Drag a Message Box and mention the Clipboard variable to display the output. Click on Save. Refer to the below image.

Step 4: Save and Execute the task.

When you execute the task, you will see that the active window title will be displayed. Here the output is Run Time Window as you can see below.

Now, this was just one command guys, you can use the other commands to open/close/resize a window by dragging your mouse over the window.

Mouse Clicks

The Mouse Clicks are used to automate the tasks which are related to clicking at a specific position on the application or a window

Problem Statement:

To automate the action of closing a notepad window.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag the Insert Mouse Click command into your workspace.

Step 2: Select the window on which you want to click. Here I want to click on the Notepad window.

Step 3: Now click on Capture and hover your mouse over the area where you want to click. Here I want to click on the Close symbol.

Step 4: Now, click on Save. Refer to the below image.

Step 5: Save and Execute the task.

When you execute the task, you will see that the notepad window is closed automatically.

String Operations

The String operations like in any other programming knowledge are used to perform various operations such as comparing two strings, replacing a string, finding a length of the string, reversing a string, splitting a string and so on.

Problem Statement:

To automate the task of replacing a few characters from a string.

Solution:

Step 1: Open the Automation Anywhere Workbench and assign the source strings, find and replace values to three different variables.

Step 2: Now, drag the Replace command from the String Operation section.

Step 3: In the source string section mention the initial string or the variable assigned to store the initial string.

Step 4: In the find and the replace section mention the find and the replace strings or the variables assigned to it.

Step 5: Assign the output variable. Here I have assigned the output to Clipboard variable. Now, click on Save. Refer to the below image.

NOTE: Here our source string is edureka. Find value will be ‘reka’ and we have to replace it with ‘tech’.

Step 6: Drag a Message Box and mention the Clipboard variable to display the output. Click on Save.

Step 7: Save and Execute your task.

When you execute the task, you will see that the string is replaced as below.

Now, this was just one command guys, you can use the other commands to compare/ find the length/ find a sub-string/ trim and do many other operations on the strings.

Files & Folders

The files and folders command deals with the various actions which you can perform with the files or folders like copying/deleting/renaming/moving/zipping/ printing and so on.

Problem Statement:

To automate the task of copying the files from a source folder to the destination folder.

Solution:

Step 1: Open the Automation Anywhere Workbench and go to the Files/Folders activity. Now, choose the Copy Files action and drag it to your workspace.

Step 2: Over here choose the option Folder since we wish to move a folder from a source path to the destination path.

Step 3: Then mention the source path in the Source File section and the Destination path in the Destination path section. Refer below.

Step 4: Save and Execute your task.

Once you execute the task you will see that files present in a folder in the source path will be moved to the destination path.

Web Recorders

The web recorders are used in Automation Anywhere to extract text from the web, find broken links, navigate URL, manage web controls, download file and so on.

Problem Statement:

To automate the task of extracting a table from a webpage.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag the Extract Table command.

Step 2: Now, mention the URL and click on Launch. This will open your website.

Step 3: Next, click on Capture Table and hover your mouse over the table you wish to extract. Refer below.

Step 4: Next, extract the table data to a CSV file by mentioning the path of the CSV file. 

Step 5: You can also click on view extracted table by clicking on the View Extracted Table option. Then, click on Save. Refer below.

Step 7: Save and Execute your task.

When you execute your task, you will observe that the table is extracted and stored into a CSV file as below.

Optical Character Recognition(OCR)

The optical character recognition aka OCR is used to capture an area, window or capture an image by path or URL.

Problem Statement:

To automate the task of extracting a text from a window and displaying the output.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag the Capture Area command from the OCR section.

Step 2: Now, select the window from where you wish to extract text and choose the OCR Engine. By default, TESSERACT is chosen.

Step 3: Next, click on Capture Area and drag your mouse over the area from which you wish to extract data. Refer below.

Step 4: You can also view the Captured Text by clicking on View Captured Text. Then click on Save. Refer below.

Step 5: Assign the output to a variable. Here, I am assigning the variable to the Clipboard variable.

Step 6: Now,  drag and drop a Message Box and mention the output variable to display the output.

Step 7: Save and Execute the task.

When you execute the task, you will see that the text is being extracted from the Notepad and is displayed on the Message Box as below.

KeyStrokes

Keystrokes are used to automate the tasks of writing text into an application. With the help of keystrokes, you can make sure you enter the text in any manner that you wish.

Problem Statement:

To automate the task of writing text into a notepad file.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag and drop the Insert Keystrokes command.

Step 2: Type in your text that you want to display on a notepad using the keystrokes. In the below snapshot I have used ENTER, CAPS LOCK keystroke.

Step 3: Save and Execute the task.

Once you execute the task you will see the text is automatically mentioned below.

Rest Web Services

The Rest Web Services are used to automate the task of fetching data from JSON APIs’ and storing it.

Problem Statement:

To automate the task of extracting the data from a JSON file and displaying the output in a Message Box.

Solution:

Step 1: Open the Automation Anywhere Workbench and drag and drop the REST Web Service command.

Step 2: Now mention the URL of the JSON API and choose the method as GET.

Step 3: Now, go to the Response section save the response to a variable.

Step 4: Drag a Message Box and mention the variable. Refer below.

Step 5: Save and Execute the task.

Once you execute the task, you will see the data being extracted from the JSON file and is being displayed on the Message Box. Refer below.

Now, with this, we come to an end of this section of this article on Automation Anywhere Examples. Next, in this article, I will show you some complex automation like Excel and PDF.

Complex Examples

In this section of the article on Automation Anywhere Examples, I will discuss the Excel and PDF automation.

So, let us get started guys!

Excel Automation

Excel Automation is used to tasks related to Excel such as opening/closing a spreadsheet, activating cells, going to a specific cell and so on.

Problem Statement

To automate the task of extracting the data from an Excel File according to some condition and storing the extracted data in another File.

Solution

Step 1: Open the Automation Anywhere Workbench.

Before I move forward with the steps, let me show the file from which we are going to extract data and the output file.

Step 2: Now, your next step is to open both the spreadsheets. To do that, drag the Open Spreadsheets command from the Excel section.

Step 3: Mention the path of the first spreadsheet( from where you have to extract data), check in the box of Contains Header and click on Save. Refer below.

Step 4: Repeat the above two steps for the second spreadsheet and change the session name to Default1 so that it doesn’t clash with the session name of the first spreadsheet. Refer below.

Step 5: Now, drag the Get Cell command and choose the  Get All Cells option. Then, mention the session name to be Default(which is the session name for the first spreadsheet). Then click on Save. Refer below.

Step 6: Drag the Get All Cells action in between the actions of opening both the spreadsheets. This step will help you get the data from all the cells in the first spreadsheet.

Your task pane should look like the below image: