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.
Top comments (0)