triton 🦎

A self sustaining growing neural net that can repair itself until reaching a desired accuracy

Installation

Use the package manager cargo to add triton to your rust project.

cargo add triton_grow

or add the dependency directly in your cargo.toml file

[dependencies]
triton_grow = "{version}"

Usage

Triton acts as a typical neural network implementation, but allows for a more dynamic way of solving problems you may not know how to solve. Acting as a 'brute force' approach to the world of deep learning, after n epochs in the training process triton will evaluate the specific error of each neuron and column, deciding whether to add a neuron to a column, add a new column entirely, remove a neuron or remove a column.

Triton will train and grow a desirable neural network until a specific accuracy is matched, returning the finished model

use triton_grow::network::{network::Network, activations};

async fn main() -> Result<(),Error>{
    let new_net = Network::new(vec![1,2,3,2], activations::SIGMOID, 0.1);

    let newer_net = Network::from(&new_net, 2, 4);
}

TODO

Currently, triton is in a very beta stage, the following features are still in development:

• Mutating a neural network (1/4)
  • Adding a new layer with n neurons into any point of an existent network
  • Removing a layer from an existent network
  • Adding a single neuron to a layer
  • Removing a single neuron from a layer
• Back propegation only affecting a single column (allows for a newly added layer to 'catch up')
• Analysis mode during back propegation allowing for all individual errors to be recorded
• Updated training function
  • Input desired success rate
  • Dynamic error analysis to allow for choosing if the network should grow or shrink
  • Acceptable threshold of +/- in the errors to allow for a less punishing learning process especially when a new neuron layer has been added
• Model serialization (serde)

License

MIT