https://github.com/noodlesreturn/lua-love2d-v11.5-1-neural-network

PS: This code is mostly written by various AI models including (Llima 3.1, GPT4o and Gemini 1.5 pro)
https://github.com/noodlesreturn/lua-love2d-v11.5-1-neural-network

ai love2d lua nueral-network

Last synced: 15 days ago
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PS: This code is mostly written by various AI models including (Llima 3.1, GPT4o and Gemini 1.5 pro)

• Host: GitHub
• URL: https://github.com/noodlesreturn/lua-love2d-v11.5-1-neural-network
• Owner: NoodlesReturn
• Created: 2024-09-10T14:08:44.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T17:39:09.000Z (2 months ago)
• Last Synced: 2024-10-16T12:12:57.341Z (30 days ago)
• Topics: ai, love2d, lua, nueral-network
• Homepage:
• Size: 16.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Neural Network in Lua (LÖVE 2D) - README

## Overview

This project implements a simple neural network in Lua using the LÖVE 2D framework. The network can learn from data through training and provides a visual representation of the network's nodes and connections. It supports a single hidden layer with dynamic nodes, allowing you to modify the network structure easily.

## Features

- **Feedforward Neural Network**: Processes input data through a single hidden layer to generate output.

- **Backpropagation Training**: Learns by adjusting weights and biases based on error.

- **Visualization**: Displays nodes, connections, and weight strengths in real-time.

## Prerequisites

To run this project, you need the following:

- [LÖVE 2D framework](https://love2d.org/) (version 11.3 or later)

- Lua (built-in with LÖVE 2D)

## Getting Started

1. **Clone the repository**:

   ```bash

   git clone 

   cd 

   ```

2. **Run the project**:

   Open a terminal in the project directory and type:

   ```bash

   love .

   ```

This will open the LÖVE 2D window, displaying the neural network.

## Customization

- **Modify the Network Structure**: You can change the number of input, hidden, and output nodes by editing the variables in the `love.load()` function.

- **Training the Network**: Use the `train` function to teach the network with data by adjusting weights to reduce error over time.

## How it Works

- **Feedforward Process**: Inputs are passed through the network, multiplied by weights, added to biases, and processed by an activation function.

- **Backpropagation**: The network adjusts its weights based on the error between its prediction and the target output.

## License

This project is open-source and available under the MIT License.

---

Feel free to explore the code, experiment with different node structures, and tweak the training parameters!