NeuralNetwork Using Java with ND4J

Welcome to the NeuralNetwork Using Java with ND4J repository! This project demonstrates how to implement a Convolutional Neural Network (NeuralNetwork) in Java using the ND4J (Numerical Computing for Java) library. ND4J provides high-performance tensor computation, making it a powerful choice for machine learning and deep learning tasks in Java.

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Table of Contents

1. Overview
2. Features
3. Technologies Used
4. Setup Instructions
5. Usage
6. NeuralNetwork Architecture
7. Dataset
8. Contributing

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Overview

This project is a hands-on implementation of a Convolutional Neural Network (NeuralNetwork) in Java using ND4J. CNNs are powerful neural networks widely used in computer vision tasks such as image classification, object detection, and segmentation. The repository demonstrates:

• Preprocessing data for training CNNs.
• Defining and training a NeuralNetwork using ND4J.
• Evaluating model performance on a test dataset.

This project is a great resource for Java developers interested in machine learning and deep learning.

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Features

• Custom NeuralNetwork Implementation: Build a NeuralNetwork model step by step in Java.
• Flexible Architecture: Modify layers, activation functions, and optimizers.
• Dataset Preprocessing: Load and normalize image data.
• Training & Evaluation: Train the NeuralNetwork on a sample dataset and evaluate its accuracy.

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Technologies Used

• Java: Core programming language.
• ND4J: Numerical computation library for deep learning in Java.
• Maven: Build automation tools.

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Setup Instructions

Follow these steps to set up and run the project:

Prerequisites

1. Java Development Kit (JDK)
   Ensure JDK 11 or higher is installed.
   Download JDK

2. Maven
   Install Maven for dependency management.
   Maven Installation Guide

3. Clone the Repository

   git clone https://github.com/Se00n00/NeuralNetwork-Using-Java-ND4J.git
   cd NeuralNetwork-Using-Java-ND4J

Usage

Step 1: Get all the dependecies

• Reload the pom.xml to resolve all the required dependencies
• In case of artifact nor found error, visit maven repository to get the info of correct version

Step 2: Clear Resources and Mention your Dataset Directory

• If You wish to work on your own dataset, clear the resources first (any thing iniside it)
• Inside Main.java change the already mention path of your dataset to your dataset in local

Step 3: Train the Model

• Run the Main.java file to start the training process:

  mvn exec:java -Dexec.mainClass="com.example.Main"

• It will first check if the preprocessed data exists in resources or not if not, then would prepare the dataset
• Then the actual training of model would start

NeuralNetwork Architecture

Example Architecture Definition : METHOD : 1

NeuralNetwork NN = new NeuralNetwork()
// Add Layer To It
NN.add(new Conv2D(10,7,0,4)0;        // (Number of Filters, FIlter Shape, Padding, Strides )
NN.add(new MaxPool2D(2,1));          // (Window Shape, Strides)
NN.add(new Flatten());               // Flatten Defore Dense Layer
NN.add(new Dense(512, "RELU"));      // (Number of Neurons, Activation Function
NN.add(new Dense(10, "SOFTMAX"));

Example Architecture Definition : METHOD : 2

NeuralNetwork NN = new NeuralNetwork(new ArrayList<>(Arrays.asList(
                new Conv2D(10,7,0,4),
                new MaxPool2D(2,1),
                new Flatten(),
                new Dense(512, "RELU"),
                new Dense(64, "RELU"),
                new Dense(10,"SOFTMAX")
 )));

Training And Evaluation

// Train the Model
NN.fit(Train_X, Train_Y, 0.001,10);    // (Training_X, Training_Y, Learning Rate, Epoch)

// Evalute the Model
System.out.println("Final Accuracy :: "+NN.Accuracy(Test_X, Test_Y)+"%");

Modify the architecture in Main.java to suit your requirements Using any Method.

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Contributing

Contributions are welcome! If you'd like to contribute to this project, follow these steps:

1. Fork the Repository: Click on the "Fork" button at the top right of this page.
2. Clone Your Fork:

   git clone https://github.com/Se00n00/NeuralNetwork-Using-Java-ND4J.git