sklears-neural

Latest release: 0.1.1 (April 25, 2026). See the workspace release notes for highlights and upgrade guidance.

Overview

sklears-neural delivers multilayer perceptrons and neural utility blocks that align with scikit-learn’s neural-network module while embracing Rust’s performance story.

Key Features

• Models: MLPClassifier, MLPRegressor, RBMs, autoencoders, and incremental learning variants.
• Optimizers: SGD, Adam, LBFGS, RMSProp, and adaptive learning-rate schedules.
• Hardware Acceleration: SIMD kernels, CUDA/WebGPU execution, and mixed-precision training.
• Integration: Works with sklears pipelines, calibration, inspection, and export utilities.

Quick Start

use sklears_neural::MLPClassifier;
use scirs2_core::ndarray::{array, Array1};

let x = array![
    [0.0, 0.0],
    [0.0, 1.0],
    [1.0, 0.0],
    [1.0, 1.0],
];
let y = Array1::from(vec![0, 1, 1, 0]);

let mlp = MLPClassifier::builder()
    .hidden_layer_sizes(vec![16, 16])
    .activation("relu")
    .solver("adam")
    .max_iter(1000)
    .random_state(Some(42))
    .build();

let fitted = mlp.fit(&x, &y)?;
let probs = fitted.predict_proba(&x)?;

Status

• Exercised via 432 passing crate tests in 0.1.1 (86 skipped).
• Verified against scikit-learn parity tests for convergence and scoring APIs.
• Roadmap items (ONNX export, distillation helpers) documented in this crate’s TODO.md.