Rust DNN

Create Modular Deep Neural Networks in Rust easy

In progress

If literally anyone stars this project I will add convolutional layers, more activations, and deconv layers. If this project get 20 stars I add everything

Installation

After running

cargo add Rust_Simple_DNN

Then you must put these in your rust code

use Rust_Simple_DNN::rdnn::layers::*;
use Rust_Simple_DNN::rdnn::*;

Mini tutorial

This is how you make a neural network that looks like this

Use this code to make it:

//FC layers are dense layers.
//Sig layers are sigmoid activation
let mut net = Net::new(
        vec![
            FC::new(3, 4), //input 3, output 4
            Sig::new(4), //sigmoid, input 4 output 4

            FC::new(4, 4),
            Sig::new(4), //sigmoid

            FC::new(4, 1),// input 4 output 1
            Sig::new(1), //sigmoid
        ],
        1, //batch size
        0.1, //learning rate
    );
    //"net" is the variable representing your entire network

net.forward_data(&vec![1.0, 0.0, -69.0]); //returns the output vector

After propagating some data through, you can then also backpropagate some like this:

 net.backward_data(&vec![0.0]); //a vector of what you want the nn to output

The network will automatically store and apply the gradients, so to train the network, all you need to do is repeatedly forward and backpropagate your data

let mut x = 0;

    while x < 5000 {
        net.forward_data(&vec![1.0, 0.0, 0.0]);
        net.backward_data(&vec![1.0]);

        net.forward_data(&vec![1.0, 1.0, 0.0]);
        net.backward_data(&vec![0.0]);

        net.forward_data(&vec![0.0, 1.0, 0.0]);
        net.backward_data(&vec![1.0]);

        net.forward_data(&vec![0.0, 0.0, 0.0]);
        net.backward_data(&vec![0.0]);
        x += 1;
    }

//at this point its trained

This is Pytorch if it wasn't needlessly complicated be like hahahaha