Implementing an Extended Convolutional Neural Network in Rust

Introduction

Rust is known for its performance, safety, and concurrency. While not as popular as Python for machine learning, Rust is gaining traction due to its powerful features. In this blog, we will walk through implementing a simple Convolutional Neural Network (CNN) in Rust, using the ndarray and ndarray-rand crates for numerical operations. We will then extend the basic CNN to include additional layers like pooling and dropout, and handle more complex data.

Prerequisites

Before diving in, ensure you have Rust installed on your machine. If not, you can install it from here. You will also need the following crates:

• ndarray
• ndarray-rand
• rand
• blas-src

Add these dependencies to your Cargo.toml file:

[dependencies]
ndarray
ndarray-rand
rand
blas-src = { features = ["openblas"] }

Setting Up the Project

First, create a new Rust project:

cargo new cnn_rust
cd cnn_rust

Next, edit your Cargo.toml to include the dependencies mentioned above.

Implementing the CNN