A day with .Net

My day to day experince in .net

Micro Services Architecture – Design Authentication with IdentityServer4, SQL Server and ASP.NET Core Part-2

Posted by vivekcek on January 20, 2018

This is the continuation of my first post about “Setting up IdentityServer4 for token based authentication in Microservices architecture”

Please read the first part.
Micro Services Architecture – Design Authentication with IdentityServer4, SQL Server and ASP.NET Core Part-1

In this part we will create a Web Api. To access this Web Api first we need to get a valid token from our Identity Server. After getting the token we can call the Web Api.

Please note that we are using ASP.NET Core 1.1. The steps to do the same in ASP.NET Core 2.0 is little bit different.

1.Create a Web Api project named Protected Api.

2.Select Web Api template.

3.Now add nuget package named “IdentityServer4.AccessTokenValidation”.

4.Now update your Configure method in Startup.cs

 public void Configure(IApplicationBuilder app, IHostingEnvironment env, ILoggerFactory loggerFactory)
        {
            app.UseIdentityServerAuthentication(new IdentityServerAuthenticationOptions
            {
                Authority = "http://localhost:5000",
                RequireHttpsMetadata = false,

                ApiName = "api1"
            });

            app.UseMvc();
        }

5.Now create a controller as below with Authorize attribute.

 [Route("api/[controller]")]
    public class ValuesController : Controller
    {
        [Authorize]
        [HttpGet]
        public IActionResult Get()
        {
            return new JsonResult(User.Identity.IsAuthenticated);
        }

    }

6.Now first start our IdentityService. Then get a token as explained in the Part 1.
7.Now use that token with Postman to call our protected Api.

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Posted in Microservices | Tagged: , , , | Leave a Comment »

Micro Services Architecture – Design Authentication with IdentityServer4, SQL Server and ASP.NET Core Part-1

Posted by vivekcek on January 20, 2018

Micro Services architecture is one of the hot topic in developer community.

I recommend micro services architecture if you face the below scenarios.

1. Use it when you are going to build next Amazon, Facebook, Uber etc.
2. You need to build a large system with a set of people with different technology stack.
3. You are building for high availability and fault tolerance.
4. You want to reduce the time to market.

To succeed with this architecture. You need below thing

1. A team of good architects who understand the architecture and business domain very well.
2. Everyday refine the architecture, if you find any flaws.
3. Should be able to define the boundaries of each micro services.
4. First day onward design the architecture for availability, Security, resilience.
5. Agile is good but execute it with creative people, who know how to execute it better.

In micro services architecture the first thing we can do is design our authentication system.

Here I am going to setup a Token Based authentication system with Identity Server 4. This is how it looks.

These are my Tools.

1.Visual Studio 2017(15.0)
2.ASP.NET Core 1.1
3.SQL Server 2014
4.Post Man

Please note that the steps will be different for ASP.NET Core 2.0.

1.Create an ASP.NET Core 1.1 project.

2.Select empty template.

3.Now add the IdentityServer 4 nuget package (We are using 1.5.2 version , 1.x versions are for ASP.NET Core 1.1).

4.We want our users to be signed with Username and Password.
5.Now add a class named Config.cs in your solution and paste below code.

using IdentityServer4.Models;
using IdentityServer4.Test;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

namespace IdentityServices
{
    public class Config
    {

        public static IEnumerable<ApiResource> GetApiResources()
        {
            return new List<ApiResource>
            {
                new ApiResource("api1", "My API")
            };
        }

        public static IEnumerable<Client> GetClients()
        {
            return new List<Client>
            {
              
                new Client
                {
                    ClientId = "client",
                    AllowedGrantTypes = GrantTypes.ResourceOwnerPassword,

                    ClientSecrets =
                    {
                        new Secret("secret".Sha256())
                    },
                    AllowedScopes = { "api1" }
                }
            };
        }


        public static List<TestUser> GetUsers()
        {
            return new List<TestUser>
            {
                new TestUser
                {
                    SubjectId = "1",
                    Username = "vivek",
                    Password = "password"
                }
               
            };
        }

    }

   
}

6.Now update your Startup.cs as below.

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Builder;
using Microsoft.AspNetCore.Hosting;
using Microsoft.AspNetCore.Http;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Logging;

namespace IdentityServices
{
    public class Startup
    {
        
        public void ConfigureServices(IServiceCollection services)
        {
            services.AddIdentityServer()
                .AddTemporarySigningCredential()
                .AddInMemoryApiResources(Config.GetApiResources())
                .AddInMemoryClients(Config.GetClients())
                .AddTestUsers(Config.GetUsers());
        }

       
        public void Configure(IApplicationBuilder app, IHostingEnvironment env, ILoggerFactory loggerFactory)
        {

            loggerFactory.AddConsole();

            if (env.IsDevelopment())
            {
                app.UseDeveloperExceptionPage();
            }

            app.UseIdentityServer();

        }
    }
}

7.Now open your project properties and in Debug tab change profile from IISExpress to your project, then update you app url to http://localhost:5000/

8.Now run the app. Select IdentityServices.

9.The app will run as a console application.

10.Now open post man and try this.

In the next post we will move the users to SQL Server.

Posted in Microservices | Tagged: , , | Leave a Comment »

Resilient HTTP call with retry and exponential back-off – Micro-services architecture.

Posted by vivekcek on January 11, 2018

During the designing of a microservice architecture based application, i came across a scenario in which i need to make sure the http call to other services need to retry for ensuring resilence.

The approach i tried is retry the http call when request exception occur. And each retry is performed after a particular time interwell, which is exponenetial in nature.

To implement this i used a nuget package named polly.

This is the Resilient HTTP class we wrote, You can use an interface and dependency injection for productions app.

public interface IHttpClient
{
Task<string> GetStringAsync(string uri, string authorizationToken = null,
string authorizationMethod = "Bearer");
Task<HttpResponseMessage> PostAsync<T>(string uri, T item,
string authorizationToken = null, string requestId = null,
string authorizationMethod = "Bearer");
Task<HttpResponseMessage> DeleteAsync(string uri,
string authorizationToken = null, string requestId = null,
string authorizationMethod = "Bearer");
}
public class ResilientHttpClient : IHttpClient
{
private HttpClient _client;
private PolicyWrap _policyWrapper;
private ILogger<ResilientHttpClient> _logger;
public ResilientHttpClient(Policy[] policies,
ILogger<ResilientHttpClient> logger)
{
_client = new HttpClient();
_logger = logger;
// Add Policies to be applied
_policyWrapper = Policy.WrapAsync(policies);
}
private Task<T> HttpInvoker<T>(Func<Task<T>> action)
{
// Executes the action applying all
// the policies defined in the wrapper
return _policyWrapper.ExecuteAsync(() => action());
}
public Task<string> GetStringAsync(string uri,
string authorizationToken = null,
string authorizationMethod = "Bearer")
{
return HttpInvoker(async () =>
{
var requestMessage = new HttpRequestMessage(HttpMethod.Get, uri);
var response = await _client.SendAsync(requestMessage);
return await response.Content.ReadAsStringAsync();
});
}
}

Now in your WebApi’s Startup.cs write below code.

// Startup.cs class
if (Configuration.GetValue<string>("UseResilientHttp") == bool.TrueString)
{
services.AddTransient<IResilientHttpClientFactory,
ResilientHttpClientFactory>();
services.AddSingleton<IHttpClient,
ResilientHttpClient>(sp =>
sp.GetService<IResilientHttpClientFactory>().
CreateResilientHttpClient());
}
else
{
services.AddSingleton<IHttpClient, StandardHttpClient>();
}

public ResilientHttpClient CreateResilientHttpClient()
=> new ResilientHttpClient(CreatePolicies(), _logger);
// Other code
private Policy[] CreatePolicies()
=> new Policy[]
{
Policy.Handle<HttpRequestException>()
.WaitAndRetryAsync(
// number of retries
6,
// exponential backoff
retryAttempt => TimeSpan.FromSeconds(Math.Pow(2, retryAttempt)),
// on retry
(exception, timeSpan, retryCount, context) =>
{
var msg = $"Retry {retryCount} implemented with Pollys
RetryPolicy " +
$"of {context.PolicyKey} " +
$"at {context.ExecutionKey}, " +
$"due to: {exception}.";
_logger.LogWarning(msg);
_logger.LogDebug(msg);
}),
}

Posted in Microservices | Leave a Comment »

Location Permission in Android Version 6 (android 6.0 marshmallow) With API Level 23

Posted by vivekcek on January 2, 2018

I was developing an android taxi app with gps location. Initially i targeted the app for API level 26 and later i decided for API level 23. In the app i need to get location permission at run time. I placed required attributes in android manifest as below.

 <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION" />
 <uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION" />
 <uses-permission android:name="android.permission.INTERNET" />
 <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />

Then i asked for permission using the code below. Unfortunately after deploying the app in a marshmallow, the app never asked for permission. Then i went to App manager and gave permission manually.

  if (ActivityCompat.checkSelfPermission(this, android.Manifest.permission.ACCESS_FINE_LOCATION) != PackageManager.PERMISSION_GRANTED && ActivityCompat.checkSelfPermission(this, android.Manifest.permission.ACCESS_COARSE_LOCATION) != PackageManager.PERMISSION_GRANTED) {

            return;
        }

After some research i learn that to get run-time location permission in marshmallow, you have to try below steps in your Activity.

1. Override below method.

final int LOCATION_REQUEST_CODE = 1;
    @Override
    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {
        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
        switch (requestCode) {
            case LOCATION_REQUEST_CODE: {
                if (grantResults.length > 0 && grantResults[0] == PackageManager.PERMISSION_GRANTED) {
                    mapFragment.getMapAsync(this);
                } else {
                    Toast.makeText(getApplicationContext(), "Please provide the permission", Toast.LENGTH_LONG).show();
                }
                break;
            }
        }
    }

2. Request permission using below code.

if (ActivityCompat.checkSelfPermission(this, android.Manifest.permission.ACCESS_FINE_LOCATION) != PackageManager.PERMISSION_GRANTED && ActivityCompat.checkSelfPermission(this, android.Manifest.permission.ACCESS_COARSE_LOCATION) != PackageManager.PERMISSION_GRANTED) {
            ActivityCompat.requestPermissions(DriverMapActivity.this, new String[]{android.Manifest.permission.ACCESS_FINE_LOCATION}, LOCATION_REQUEST_CODE);
        }else{
            mapFragment.getMapAsync(this);
        }

Posted in Android c# | Tagged: , , , , | Leave a Comment »

Communication between Angular 4 components through RxJs and Observables

Posted by vivekcek on September 27, 2017

Hi Guys this is a quick post about how we can implement communication between Angular 4 components via RxJs and Observable. This example can be done with other component communication mechanisms like @Input() and Services.

But observable based communication will be useful in large Angular 4 applications.
I am not going to explain the concept in detail. Please google it.

Back to our example.

I have two nested components, AppComponent and HelloCoponent. AppCoponent is the parent/root.
AppComponent display a list of dynamically created items. There is a button inside HelloComponent, When clicking on it new items are added and these new items are reflected in AppComponent.

Have a look at the structure.

Have a look at the code.

AppComponent

import { Component,OnInit} from '@angular/core';
import {MessageService} from './message.service'
import {Subscription} from 'rxjs/Subscription'
@Component({
  selector: 'my-app',
  templateUrl: './app.component.html',
  styleUrls: [ './app.component.css' ]
})
export class AppComponent implements OnInit  {
  
  names:string[];

  constructor(private msgServce:MessageService){

  }

  ngOnInit():void{
    this.msgServce.msgPublisher$.subscribe(data=>{this.names=data.names;
    //this.changeDetectorRef.detectChanges();
    
    });
  }
}

AppComponent.html

<hello ></hello>
<ul>
       <li *ngFor="let name of names">
          {{name}}
       </li>
</ul>

HelloCoponent

import { Component, Input } from '@angular/core';
import {MessageService} from './message.service'
@Component({
  selector: 'hello',
  template: `<button (click)="onAdd()">Add</button>`,
  styles: [`h1 { font-family: Lato; }`]
})
export class HelloComponent  {
  constructor(private msgService:MessageService){

  }

  onAdd(){
    this.msgService.addName("Test");
  }
}

MessageService

import {Injectable} from '@angular/core'
import {Subject} from 'rxjs/Subject'

@Injectable()
export class MessageService{
  constructor(){}
  names:string[]=[];
  private messageSource=new Subject<any>();
  msgPublisher$=this.messageSource.asObservable();

  addName(name:string){
    this.names.push(name)
    this.messageSource.next({names:this.names})
  }
}

Posted in Angular2 | Tagged: , | Leave a Comment »

Predicting linear regression with Tensorflow and Azure Machine Learning Studio (Comparison ,Gradient descent)

Posted by vivekcek on September 20, 2017

In this post I am trying to evaluate the prediction done by Tensorflow and Azure Machine Learning Studio.
I am using a dataset obtained from Courseera machine learning tutorial. I will provide the dataset at the end of this post.
Here is the predicted value I got from Tensorflow and Azure Machine Learning for the input “8.5172”

Azure

Tensorflow

I used linear regression with Gradient descent optimizer, and below are the values used for epoch and learning rate in Tensorflow and azure ML.
Epoch=1000
Learning rate=0.01

Azure Model and Algorithm settings are given below

Tensorflow code is given below.

import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf

# Parameters
display_step = 50
learning_rate = 0.01
training_epochs = 1000

data = pd.read_csv('ex1data1.txt', names=['population', 'profit'])

X_data = data[['population']]
Y_data = data[['profit']]

n_samples = X_data.shape[0]  # Number of rows

# tf Graph Input
X = tf.placeholder('float', shape=X_data.shape)
Y = tf.placeholder('float', shape=Y_data.shape)

# Set model weights
W = tf.Variable(tf.zeros([1, 1]), name='weight')
b = tf.Variable(tf.zeros(1), name='bias')

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
# cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
cost = tf.reduce_mean(tf.square(pred-Y)) / 2.0

# Gradient descent
# may try other optimizers like AdadeltaOptimizer, AdagradOptimizer, AdamOptimizer, FtrlOptimizer or RMSPropOptimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    cost_value, w_value, b_value = (0.0, 0.0, 0.0)
    for epoch in range(training_epochs):
        # Fit all training data
        _, cost_value, w_value, b_value = sess.run((optimizer, cost, W, b), feed_dict={X: X_data, Y: Y_data})

        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            print('Epoch:', '%04d' % (epoch+1), 'cost=', '{:.9f}'.format(cost_value), \
                'W=', w_value, 'b=', b_value)

    print ('Optimization Finished!')
    print ('Training cost=', cost_value, 'W=', w_value, 'b=', b_value, '\n')
    print('Evaluation')
    print(w_value*8.5172+b_value)
    # Graphic display
    plt.plot(X_data, Y_data, 'ro', label='Original data')
    plt.plot(X_data, w_value * X_data + b_value, label='Fitted line')
    plt.legend()
    plt.show()

Please use below dataset.

6.1101,17.592
5.5277,9.1302
8.5186,13.662
7.0032,11.854
5.8598,6.8233
8.3829,11.886
7.4764,4.3483
8.5781,12
6.4862,6.5987
5.0546,3.8166
5.7107,3.2522
14.164,15.505
5.734,3.1551
8.4084,7.2258
5.6407,0.71618
5.3794,3.5129
6.3654,5.3048
5.1301,0.56077
6.4296,3.6518
7.0708,5.3893
6.1891,3.1386
20.27,21.767
5.4901,4.263
6.3261,5.1875
5.5649,3.0825
18.945,22.638
12.828,13.501
10.957,7.0467
13.176,14.692
22.203,24.147
5.2524,-1.22
6.5894,5.9966
9.2482,12.134
5.8918,1.8495
8.2111,6.5426
7.9334,4.5623
8.0959,4.1164
5.6063,3.3928
12.836,10.117
6.3534,5.4974
5.4069,0.55657
6.8825,3.9115
11.708,5.3854
5.7737,2.4406
7.8247,6.7318
7.0931,1.0463
5.0702,5.1337
5.8014,1.844
11.7,8.0043
5.5416,1.0179
7.5402,6.7504
5.3077,1.8396
7.4239,4.2885
7.6031,4.9981
6.3328,1.4233
6.3589,-1.4211
6.2742,2.4756
5.6397,4.6042
9.3102,3.9624
9.4536,5.4141
8.8254,5.1694
5.1793,-0.74279
21.279,17.929
14.908,12.054
18.959,17.054
7.2182,4.8852
8.2951,5.7442
10.236,7.7754
5.4994,1.0173
20.341,20.992
10.136,6.6799
7.3345,4.0259
6.0062,1.2784
7.2259,3.3411
5.0269,-2.6807
6.5479,0.29678
7.5386,3.8845
5.0365,5.7014
10.274,6.7526
5.1077,2.0576
5.7292,0.47953
5.1884,0.20421
6.3557,0.67861
9.7687,7.5435
6.5159,5.3436
8.5172,4.2415
9.1802,6.7981
6.002,0.92695
5.5204,0.152
5.0594,2.8214
5.7077,1.8451
7.6366,4.2959
5.8707,7.2029
5.3054,1.9869
8.2934,0.14454
13.394,9.0551
5.4369,0.61705

Posted in Azure, Machine Learning, Tensorflow | Tagged: , | Leave a Comment »

Linear Regression With TensorFlow – Part1

Posted by vivekcek on September 18, 2017

Hi Guys in this blog post i am trying to explain, how we can implement simple linear regression with tensorflow. Simple linear regression means regression with single input and single output.

In future posts i will explain about.

1. Linear regression with multiple features.
2. Polynomial regression.
3. Regularized/Normalized linear regression.
4. Linear regression with external data.

Today anyway i want keep the problem simple, so i am using some inline data for analysis.

I hope you have some good knowledge about Machine Learning. If not please take a training from course-era. Course-era has a good training in Machine Learning by Andrew Ng.

Do we really need tensorflow to do linear regression? We can implement it in Octave, MATLAB, Python, Scikit-learn etc…

Understanding the Math’s and statics behind linear regression is more important than the tool we are going to use.

So how we will approach this problem?

First we need to ensure the data available with us is linearly dependent. For that we need to plot it. You can use the below code to plot your data.

import matplotlib.pyplot as plt
import numpy 

train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

From the image it is clear that the data is linearly dependent and we can use simple linear regression with it.

Next we are going to define our hypothesis, Cost function and Optimizer. Hope the reader is aware of what you mean by cost, how to minimize the cost etc..

For linear regression the hypothesis we are going to use is the equation of a straight line.

hypothesis (prediction)=WX+b(Where W is the slope and b is the y intercept and X is our input).
In tensorflow we say them as Weight (W) and bias (b).

Next what is cost, cost is actually the difference from the actual to predicted. We need to minimize this cost to find a best W and b.

The cost function for linear regression is given below.

To minimize the cost we are going to use gradient descent algorithm.

The full code is given below.

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random


learning_rate = 0.01
training_epochs = 1000
display_step = 50


train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]


X = tf.placeholder("float")
Y = tf.placeholder("float")


W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")


hypothesis = tf.add(tf.multiply(X, W), b)


cost = tf.reduce_mean(tf.square(hypothesis - Y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)


init = tf.global_variables_initializer()


with tf.Session() as sess:


    sess.run(init)
    for epoch in range(training_epochs):
        sess.run(optimizer, feed_dict={X: train_X, Y: train_Y})


        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

    # Testing example, as requested (Issue #2)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        cost,
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

Posted in Machine Learning, Tensorflow | Leave a Comment »

C# Versions 8 to 6 Fetaures

Posted by vivekcek on September 14, 2017

In this post you can find the new features available in C# versions 8, 7.1, 7, 6.

Posted in c#.net | Tagged: , , , , | Leave a Comment »

Visualise Computational Graphs with Tensorboard and Tensorflow

Posted by vivekcek on August 6, 2017

In this post i will show , how you can visualise computational graphs created by tensorflow bu using tensorboard.

I am using visual studio for tensorflow development in windows. Refer my previous blog to setup tensor flow in windows Installing tensorflow in windows

Hope the reader has some basic understanding of tensorflow. Tensorflow execute every operation by building computational graphs. To visualize such graph we use tensorboard.

Tensorboard is a visualisation tool that will be installed as a part of tensorflow installation.

Write below code in visual studio. In this code we declare four constants, then we multiply ‘a’ and b’ after that we divide ‘c’ and ‘d’ then result of both were added to produce the final output.

import tensorflow as tf

a=tf.constant(6,name="a")
b=tf.constant(3,name="b")
c=tf.constant(10,name="c")
d=tf.constant(5,name="d")

mul=tf.multiply(a,b,name="mul")
div=tf.div(c,d,name="div")

addn=tf.add_n([mul,div],name="addn")

sess=tf.Session()
output =sess.run(addn)
print(output)
writer=tf.summary.FileWriter('./visual',sess.graph)
writer.close()
sess.close()

These line actually help us to create graphs for above computation. Here “visual” is a folder created in your running directory.

writer=tf.summary.FileWriter('./visual',sess.graph)
writer.close()

Now go to your running directory and execute below command.

tensorboard --logdir="visual"

Now browse to the link and see the graph.

Posted in Tensorflow | Tagged: , , , , | Leave a Comment »

UiPath Automation Video Tutorial – Data Entry In Windows Application

Posted by vivekcek on August 4, 2017

This is my first attempt to create some YouTube video tutorials. In this video i will demonstrate how you can automate a windows application via UiPath.

UiPath is one of the leading RPA provider and you don’t need much technical expertise to learn it.
Also it is easier to learn than test automation.

Posted in UiPath(Robotic Process Automation) | Tagged: , , | 2 Comments »