In this blog, we will build a simple neural network from scratch in Rust. We'll start by setting up our project, then implement the core components of a neural network, and finally train it on a basic dataset.

Project Setup

First, let's set up a new Rust project. Open your terminal and run:

cargo new neural_network
cd neural_network

Next, we'll add the ndarray crate for numerical operations and rand crate for random number generation. Update your Cargo.toml file to include these dependencies:

[dependencies]
ndarray = "0.15"
rand = "0.8"

Implementing the Neural Network

We'll start by creating a network.rs file in the src directory to hold our neural network implementation.

Defining the Network Structure
Create a Network struct that will hold our weights and biases:

// src/network.rs
use ndarray::{Array1, Array2, Axis};
use rand::thread_rng;
use rand::Rng;

pub struct Network {
    weights1: Array2<f64>,
    biases1: Array1<f64>,
    weights2: Array2<f64>,
    biases2: Array1<f64>,
}

impl Network {
    pub fn new(input_size: usize, hidden_size: usize, output_size: usize) -> Self {
        let mut rng = thread_rng();

        let weights1 = Array2::from_shape_fn((hidden_size, input_size), |_| rng.gen_range(-1.0..1.0));
        let biases1 = Array1::from_shape_fn(hidden_size, |_| rng.gen_range(-1.0..1.0));
        let weights2 = Array2::from_shape_fn((output_size, hidden_size), |_| rng.gen_range(-1.0..1.0));
        let biases2 = Array1::from_shape_fn(output_size, |_| rng.gen_range(-1.0..1.0));

        Network {
            weights1,
            biases1,
            weights2,
            biases2,
        }
    }
}

Forward Pass
Implement the forward pass of the network, which involves calculating the activations for each layer:

impl Network {
    fn sigmoid(x: &Array1<f64>) -> Array1<f64> {
        x.mapv(|x| 1.0 / (1.0 + (-x).exp()))
    }

    fn sigmoid_derivative(x: &Array1<f64>) -> Array1<f64> {
        x * &(1.0 - x)
    }

    pub fn forward(&self, input: &Array1<f64>) -> (Array1<f64>, Array1<f64>, Array1<f64>) {
        let hidden_input = self.weights1.dot(input) + &self.biases1;
        let hidden_output = Self::sigmoid(&hidden_input);
        let final_input = self.weights2.dot(&hidden_output) + &self.biases2;
        let final_output = Self::sigmoid(&final_input);

        (hidden_output, final_input, final_output)
    }
}

Read the full article here!

Conclusion

In this blog, we built a simple neural network from scratch in Rust. We covered the core components, including initialization, forward pass, and backpropagation. This example can be expanded to more complex networks and datasets, providing a solid foundation for neural network implementation in Rust.

Feel free to experiment with different architectures, activation functions, and learning rates to see how they affect the network's performance.

Building a Neural Network from Scratch in Rust

Project Setup

Implementing the Neural Network

Read the full article here!

Conclusion