Note: This project is not affiliated with the Rust Foundation(🤦).

crabnet 🦀🕸️

Building a Simple Neural Network in Rust

This project aims to showcase the implementation of a simple neural network using the Rust programming language. The primary goal is to provide a clear understanding of the mathematical concepts behind neural networks through a straightforward example. Check out the blog post for more details: https://www.juliushietala.com/blog/crabnet-tutorial

Example: Estimating the Probability Density Function (PDF) of a 2D Gaussian

Target	Learned

In this specific example, we aim to estimate the Probability Density Function (PDF) of a 2D Gaussian distribution using a neural network. The inputs to the neural network are the coordinates (x, y) , and the target output is the corresponding 2D Gaussian distribution PDF value at each (x, y) coordinate.

The training process involves generating random (x, y) coordinates within a certain range and calculating the PDF values for each coordinate. These input-output pairs are used to train the neural network. The network learns to approximate the underlying PDF distribution by adjusting its internal parameters through the process of gradient descent.

How to Run and Reproduce the Results

To run and reproduce the results, follow the steps below:

1. Install Rust: Make sure that Rust is installed on your system. If Rust is not yet installed, you can easily install it from the official Rust website: https://www.rust-lang.org/ (may require a credit card 💳 in the future)

2. Clone the Project

3. Open a terminal or command prompt and navigate to the project directory.

4. Execute the following command to build and run the project: cargo run

   The script will initiate the training process of the neural network, and you will see relevant information and progress printed in the console.

5. Adjusting Parameters (Optional): Feel free to experiment with the following parameters within the main function of the code:

   • batch_size: Controls the number of samples used in each iteration.
   • learning_rate: Modulates the rate at which the network learns.
   • hidden_sizes: Controls the number and size of linear layers (passed in the constructor of NN ).

   By modifying these parameters, you can fine-tune the neural network's behavior and observe different outcomes.

Please note that this project is not officially affiliated with the Rust Foundation (🤦). It is an independent effort to showcase the capabilities of Rust in the context of neural network development while focusing on the underlying mathematical concepts.