[WIP] Snail NN - smol neural network library

Minimalistic CPU based neural network library with backpropagation and parallelized stochastic gradient descent.

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

Todos

• Wgpu compute shaders