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A Neural Network based AI, trained on MNIST dataset that predicts numerical digits written on the Canvas.

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NavjotSRakhra/NumericalDigitPredictor

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Digit Predictor - A Neural Network based A.I.

Introduction

This project is based on the Neural Network I implemented. The neural network is trained on MNIST dataset. Its dimensions are input layer: 784, hidden layer: 800, output layer: 10. The accuracy of neural network is above 97%

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Project setup

The project is built upon OpenJDK 20 and should be compatible with Java 20+.

Project usage

To try this yourself, go to the directory that has pom.xml in console and:

  • Execute mvn clean package to run the code.

  • A dialog box pops up and asks you to select the trained model, you can either provide your own trained model (io.github.NavjotSRakhra.NeuralNetwork) or use the ones which I have trained that are present in the resources folder with the extension .nnn

  • You have to click/drag to paint the canvas/drawing area.

  • To clean the canvas press the keyboard key R.

To use the Neural network in your own project copy the following into pom.xml

<dependency>
    <groupId>io.github.NavjotSRakhra</groupId>
    <artifactId>NeuralNetwork</artifactId>
    <version>1.0.2</version>
</dependency>

Or install the library manually by cloning the project: Neural Network

License

MIT License

Copyright (c) 2023 Navjot Singh Rakhra

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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A Neural Network based AI, trained on MNIST dataset that predicts numerical digits written on the Canvas.

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