🧠 Neural Network Visualizer — Java Library

A lightweight, self-contained neural network library written in Java, designed for learning, experimentation, and visualization.
It provides a clean API for building and training fully connected neural networks, along with a real-time Swing-based visualizer for inspecting layers, neurons, activations, and weights.

The project is distributed as a reusable JAR library, making it easy to integrate into other Java applications.

✨ Key Features

• Fully connected feedforward neural network
• Forward propagation
• Backpropagation training
• ReLU activation function
• Adjustable learning rate
• Real-time network visualization
• Interactive zoom
• Visual representation of:
  • Neuron activations
  • Layer structure
  • Weight magnitude and sign
• Model saving and loading via serialization
• No external machine learning libraries required

📦 Using This Project as a Library

1. Download the JAR

• Go to the Releases section of this repository
• Download the latest .jar file

2. Add the JAR to Your Project

IntelliJ IDEA

1. Open Project Structure
2. Navigate to Libraries
3. Click + → Java
4. Select the downloaded JAR
5. Apply and close

Command Line

javac -cp neural-network.jar YourProject.java
java -cp neural-network.jar;. YourProject

🚀 Quick Start

Create a Network

import io.arsh.Network;

Network network = new Network(3, 5, 2);
network.setLearningRate(0.1);

Forward Propagation

double[] output = network.forward(
    new double[]{1.0, 0.5, 0.2}
);

Training (Backpropagation)

network.train(
    new double[]{1.0, 0.5, 0.2},
    new double[]{0.0, 1.0}
);

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👁️ Visualizing the Network

The library includes a built-in visualizer for inspecting the network in real time:

network.show();

Visualizer Details

• Each circle represents a neuron
• Color intensity reflects activation value
• Connection thickness represents weight magnitude
• Color differences indicate positive vs negative weights
• Mouse wheel → zoom
• Click & drag → move around

💾 Saving and Loading Models

Networks can be serialized and reused:

network.save("model.nn");

Network loaded = Network.load("model.nn");

🧩 Architecture Overview

• Network — core neural network logic
• Layer — manages neurons per layer
• Neuron — stores weights, bias, value, and gradient
• utils — activation functions (ReLU, Sigmoid)
• visualizer — Swing-based real-time renderer

The design prioritizes clarity, modularity, and educational value.

🎯 Intended Use Cases

This library is well-suited for:

• Educational projects
• Learning neural network fundamentals
• Visual demonstrations
• Java-based simulations
• Custom tools requiring lightweight neural networks

This project is not a production-level framework and very inefficient but great for learning how neural network works.

🔮 Planned Improvements

• Better optimization options (GPU Integration)
• Training performance graphs
• Loss function visualization
• More control

📜 License

This project is open-source and free to use for learning, experimentation, and personal projects.