[WIP] Snail NN - smol neural network library

fully functional neural network libary with backpropagation and parallelized stochastic gradient descent implementation.

Examples

Storing images inside the neural network, upscaling and interpolate between them.

cargo run --example imagepol --release

The mandatory xor example

cargo run --example xor --release

Example Code:

use snail_nn::prelude::*;

fn main(){
    let mut nn = Model::new(&[2, 3, 1]);
    nn.set_activation(Activation::Sigmoid)

    let mut batch = TrainingBatch::empty(2, 1);
    let rate = 1.0;

    // AND - training data
    batch.add(&[0.0, 0.0], &[0.0]);
    batch.add(&[1.0, 0.0], &[0.0]);
    batch.add(&[0.0, 1.0], &[0.0]);
    batch.add(&[1.0, 1.0], &[1.0]);

    for _ in 0..10000 {
        let (w_gradient, b_gradient) = nn.gradient(&batch.random_chunk(2));
        nn.learn(w_gradient, b_gradient, rate);
    }

    println!("ouput {:?} expected: 0.0", nn.forward(&[0.0, 0.0]));
    println!("ouput {:?} expected: 0.0", nn.forward(&[1.0, 0.0]));
    println!("ouput {:?} expected: 0.0", nn.forward(&[0.0, 1.0]));
    println!("ouput {:?} expected: 1.0", nn.forward(&[1.0, 1.0]));
}

Features

• Sigmoid, Tanh & Relu activation functions
• Parallelized stochastic gradient descent
• It works on my machine ¯\(ツ)/¯
• Will gobble up most of your cpu

Todo

• more examples
• better documentation
• compute shaders with wgpu