neural-net-rs

This code was originally forked from neural-net-rs. It has been changed and extended so much that it no longer bears any resemblance to the original code. I would like to thank the original author for their work and for inspiring me to extend this project.

I've merged everything into the main branch now so you can ignore this...

There are two interesting branches

• main This is my "stream of consciousness" code that poured out of me one weekend. It became overly complicated because I added multithreading half way through then tried to back it out. Threading isn't the best way to optimise neural networks. Not immediately anyway.
• mnist This was a new start where I concentrate on getting the code correct first. I'm pretty happy where this code is and I'll use this branch as the basis for future development.

MNIST

This code (currently) implements a neural network to train and test on the MNIST dataset of handwritten digits. The network has three layers: an input layer, a hidden layer, and an output layer. The activation function is the original sigmoid function and a learning rate of 0.01. Here are the most recent training statistics:

"layers": [784, 200, 10],
"activation": "Sigmoid",
"learning_rate": 0.01,
"momentum": 0.5

Confusion Matrix:
           Predicted
Actual     0    1    2    3    4    5    6    7    8    9
      +--------------------------------------------------
  0   |  972    0    0    2    0    2    1    1    2    0
  1   |    0 1126    1    2    0    1    2    1    2    0
  2   |    7    1 1003    1    3    1    1    8    7    0
  3   |    0    0    5  984    0    7    0    7    5    2
  4   |    1    0    4    0  953    1    2    1    2   18
  5   |    4    1    0   10    1  863    4    0    7    2
  6   |    6    3    0    1    2    4  936    2    4    0
  7   |    2    9   13    2    0    0    0  991    0   11
  8   |    5    1    2    2    8    5    3    3  940    5
  9   |    7    5    1    7   11    1    2    6    2  967

Per-digit Metrics:
Digit  | Accuracy | Precision | Recall  | F1 Score
-------|----------|-----------|---------|----------
   0   |  99.2%   |   96.8%   |  99.2%  |   98.0%
   1   |  99.2%   |   98.3%   |  99.2%  |   98.7%
   2   |  97.2%   |   97.5%   |  97.2%  |   97.3%
   3   |  97.4%   |   97.3%   |  97.4%  |   97.4%
   4   |  97.0%   |   97.4%   |  97.0%  |   97.2%
   5   |  96.7%   |   97.5%   |  96.7%  |   97.1%
   6   |  97.7%   |   98.4%   |  97.7%  |   98.1%
   7   |  96.4%   |   97.2%   |  96.4%  |   96.8%
   8   |  96.5%   |   96.8%   |  96.5%  |   96.7%
   9   |  95.8%   |   96.2%   |  95.8%  |   96.0%

Overall Accuracy: 97.35%

I don't know if this is good or bad :) Training took about 20 minutes on the 60,000 images

---

neural-net-rs is a Rust-based neural network framework designed for educational purposes. This project aims to provide a simple yet informative implementation of neural networks in the Rust programming language.

Features

• Educational Focus: neural-net-rs is created with the primary goal of helping users understand the fundamentals of neural networks in Rust.
• Simplicity: The framework prioritizes simplicity to facilitate a smooth learning experience for beginners in deep learning.
• Flexibility: While keeping things simple, neural-net-rs is designed to be flexible, allowing users to experiment with different neural network architectures.

Getting Started

Prerequisites

Install Rust

Installation

git clone https://github.com/your-username/neural-net-rs.git
cd neural-net-rs
cargo build