Module: aprender::nn

Public module of the aprender-core crate.

Source

crates/aprender-core/src/nn.rs or directory.

Example

use aprender::nn::{Sequential, Linear, ReLU};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::nn is the neural-network toolkit: layers, activations, containers, initializers, normalization, RNNs, transformer building blocks, dropout variants, and a Module trait that ties everything together. The design mirrors torch.nn — you compose layers in a Sequential container, apply forward passes, and let autograd handle backprop.

Key types

The submodule nn::transformer exposes attention + transformer-block types; nn::optim mirrors PyTorch's optimizer API for module-level training loops; nn::scheduler exposes learning-rate schedulers; nn::quantization and nn::ssm are specialized.

Usage patterns

Pattern 1: An MLP via Sequential

use aprender::nn::{Sequential, Linear, ReLU};

// Compose: 4 inputs → 8 hidden (ReLU) → 2 outputs
let mut model = Sequential::new();
model.add(Linear::new(4, 8));
model.add(ReLU::new());
model.add(Linear::new(8, 2));

println!("layers: {}", model.len());

Pattern 2: Use RMSNorm (transformer-style)

use aprender::nn::{Linear, RMSNorm};
use aprender::nn::module::Module;

let norm = RMSNorm::new(64, 1e-6);
let proj = Linear::new(64, 64);

// In a transformer block you'd run: norm -> attention -> residual.
println!("RMSNorm has {} params", norm.parameters().len());

See also

• autograd — Tensor and backward-pass machinery used by Module
• loss — losses to drive backprop
• optim — top-level stochastic optimizers (also re-exported as nn::optim)
• models — full transformer models built from these primitives
• regularization — non-dropout regularizers (Mixup, CutMix, label smoothing)

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.