ANNdotNET


ANNdotNET – deep learning tool on .NET Platform

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Blog posts related to ANNdotNET

  1. ANNdotNET v1.0 has been released (October, 2018)
  2. Visual Neural Network Designer in ANNdotNET(September, 2018)
  3. Introduction to ANNdotNET v1.0 deep learning tool on .NET Platform(September, 2018)
  4. Tutorial how to train, and evaluate Iris model with ANNdotNET. (April, 2018)
  5. Announcement of ANNdotNET v1.0 and GPdotNET v5 (March, 2018)

Introduction

ANNdotNET –  is an open source project for deep learning written in C# for developing and training deep learning models. The project is based on Microsoft CNTK (CogNitive ToolKit) Microsoft open source library for deep learning. It is supposed to be higher API for deep learning in .NET, but also provides, data preparation and transformation from rawDataSet  into mlready dataset, monitoring the training process with additional evaluation functions, capability of early stopping during training, model evaluation and validation, exporting and deployment options.
ANNdotNET v1.0 Start Page
The process of creating, training, evaluating and exporting models is provided from the GUI Application and does not require knowledge for supported programming languages. The ANNdotNET is ideal in several scenarios when user want:
  • more focus on neural network development and training process using on classic desktop approach, instead of focusing on coding,
  • less time spending on debugging source code and peripheral tasks like installing and updating packages, debugging errors in the code, and more focusing on different configuration and parameter variants,
  • to model and is not familiar with supported programming languages,
In case the problem requires more advanced custom models, or training process, ANNdotNET CMD provides high level of API for such implementation. All ml configurations developed with GUI tool, can be handled with CMD tool and vice versa. To get quick introduction to the tool, there are dozens of pre-calculated projects included in the installer which can be opened from the Start page as well as from CMD tool. The projects are based on famous datasets freely distributed on repositories from several categories: regression, binary and multi-class classification problems, image classifications, times series, etc.

Software requirements for building and running ANNdotNET solution

In order to run and develop ANNdotNET solution the following system requirements must be met:
  • Windows 8 x64 or higher,
  • Visual Studio 2017 (Community, Professional or Enterprise),
  • .NET Framework 4.7.2 and newer,
  • .NET Core 2.0 and newer
  • Git source control tool.

How to build and run ANNdotNET solution

In order to compile and build ANNdotNET solution, the following actions must be performed:
    • Clone solution by typing: git clone https://github.com/bhrnjica/anndotnet
    • Run Visual Studio 2017 and open anndotnet.sln solution file.
    • Set anndotnet.wnd– as startup project
    • Change system architecture of the solution (all projects) from Any CPU to x64

  • Once the previous actions are performed the solution window looks like:

  • Restore Nuget packages for the whole solution and press F5.
  • Once the F5 is pressed the ANNdotNET should be shown.

How to install  ANNdotNET GUI Tool

This option is handy in case you don’t have installed Visual Studio or you want to use the application without source code. Before installation, three prerequisites software components  must be installed: Once the prerequisites are installed, the ANNdotNET GUI Tool  can be installed:
  • Download binaries from the release section at: https://github.com/bhrnjica/anndotnet/releases,
  • Unzip the binaries on your machine and run anndotnet.wnd.exe exe file,
  • Once the application is run, select one of many pre-calculated projects placed on Start Page,
  • The following image shows Iris Flower Identification project opened in ANNdotNET GUI Tool

Tutorials

The following three short videos quickly show how to create, train and evaluate regression, binary and multi class classification models.
  1. Training regression model. Data set is Concrete Slump Test  is downloaded from the  UCI ML Repository and loaded into ANNdotNET without any modification, since the data preparation module can prepare it.
  1. Training and evaluation binary classifier model. Data represent Titanic data set downloaded from the public repository.
  1. Training and evaluation multi class classification models. Data represents Iris data set downloaded from the same page as above.
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