KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #ai-agents #developer-tools #codex #claude-code #mcp #rust #deepseek-harness #typescript #ai-agent #cli

MNISTTensorCNTK

★ 118 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# MNIST for ML Beginners using C#
  
This sample is a very simple WPF application which recognizes hand-written digits using the pre-built MNIST convolutional model. The MNIST problem is Machine Learning's Hello World program.

MNIST is a simple computer vision dataset which consists of hand written digits like as shown below.

This tutorial comes with a pre-built CNTK model which is trained to look at these hand-written digits and predict what these digits are.

CNTK is a deep learning library in which this model is built in. For now, we will just basically load this model as a resource into your .NET application and evaluate on it.  Follow the steps below to get going.

# Get Started
## Step 1: Download and Install
If you are new to .NET, go ahead and download [Visual Studio 2017](https://www.visualstudio.com/thank-you-downloading-visual-studio/?sku=Community&rel=15) and select only the '.NET desktop development' section as shown below.

## Step 2: Download MNIST tutorial code
Clone or [download](https://github.com/dotnet-architecture/MNISTTensorCNTK/archive/master.zip) this github repo. Open up the MNIST solution (MNIST.sln) file in Visual Studio 2017. 
                         
 <img src=https://msdnshared.blob.core.windows.net/media/2017/11/sln.png alt="workload install" width="800" hieght="270"/>

## Step 3: Understand Solution Structure 
This solution consits of two projects 'Digitz' (C# project building a windows app using the CNTK pre-built MNIST model) and 
'Training' (Python project, which generates the MNIST model using CNTK). Don't worry about the Python project for now.

<img src=https://msdnshared.blob.core.windows.net/media/2017/11/slnfragmentation.png alt="workload install" width="411" hieght="441"/>
 
## Step 4: Configure and build your Project for Launch
Set the start-up project to be 'Digitz' project as shown below and then go ahead and build your application by using the right-click 'build' option.

## Step 5: Launch MNIST application 
Launch the app by clicking the green start button at the top or F5 on your keyboard. Draw out a hand-written digit and click 'Recognize' to see if this works for you!

Congrats! you have just completed the helloworld program for Machine Learning.

# TL;DR
The sample provides you an example of how you can use Machine Learning and AI in your .NET apps today. The sample basically loads the pre-built model 'digit.model' and takes the bitmap image that user draws out, converts that into an optimized multi-dimensional exchange type Tensor<T> and then calls CNTK evaluate method to evaluate on the model. 
  
The evaluate method from CNTK returns a list of floats (0 - 9) predicting the confidence for each of these digits. The highest confidence digit is then displayed in the app.

This sample also introduces Tensor<T>, [Tensor<T>](https://blogs.msdn.microsoft.com/dotnet/2017/11/15/introducing-tensor-for-machine-learning-and-ai-libraries) is an exchange type for homogenous multi-dimensional data for 1 to N dimensions. The motivation behind introducing Tensor<T> is to make it easy for Machine Learning library vendors like CNTK, Tensorflow, Caffe, Scikit-Learn to port their libraries over to .NET with minimal dependencies in place.  Tensor<T> is designed to provide the following characteristics.

To learn more about [Tensor<T> follow our blog post](https://blogs.msdn.microsoft.com/dotnet/2017/11/15/introducing-tensor-for-multi-dimensional-machine-learning-and-ai-data/) and [GitHub repo](https://github.com/dotnet/corefxlab/tree/master/src/System.Numerics.Tensors). 
  
To follow a video tutorial on this app being used you can also follow this video. 
https://channel9.msdn.com/Events/Connect/2017/t126

MNISTTensorCNTK

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README