Advanced Rust ML: Custom Modules with Tch-rs | ML Engineering
Hi All Another day, another Rust ML backend application. Today we're looking at defining custom modules in Rust in the ML backend. We're going to be creating a custom linear layer in Rust using Tch-rs ( Rust PyTorch bindings ). View full source below: Let's break the above code down. Block by block. 1. Imports use tch::{nn, nn::Module, Tensor}; What it does: * tch::nn - Neural network module containing layer definitions. * nn:Module - The trait that all neaural network modules must implement. * Tensor - The Tensor type used throughout tch-rs 2. Struct Definition struct CustomLayer { weight: Tensor, bias: Tensor, } What it does: * weight - A tensor holding a layer's weights (matrix) * bias - A tensor holding the layer's bias (vector) This is essentially a linear layer (fully connected layer) that we're definin...
