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.
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