Crate iron_learn 

Re-exports

pub use crate::tensor::Tensor;

pub use crate::neural_network::ActivationFn;

pub use crate::neural_network::ActivationLayer;

pub use crate::neural_network::Layer;

pub use crate::neural_network::LinearLayer;

pub use crate::neural_network::LossFunction;

pub use crate::neural_network::MeanSquaredErrorLoss;

pub use crate::neural_network::NeuralNet;

pub use crate::neural_network::NeuralNetBuilder;

Modules

examples

neural_network

tensor

Tensor Module - Linear Algebra Core

Structs

Complex

A complex number represented by real and imaginary components

CpuTensor

The CpuTensor structure is the backend implementaion of the Tensor trait. The implementation uses CPU for calcualations. Although, Auto Vectorization has been used whereever possible for parallel execeution.

Traits

Numeric

The Numeric trait defines a set of operations that numeric types must support. It includes basic arithmetic operations and the ability to return special values like zero and one.

SignedNumeric

The SignedNumeric defines all the Numeric types that can be signed like i32, i64 etc.

Functions

gradient_descent

Perform a single gradient descent update step.

linear_regression

Train a linear regression model using gradient descent.

logistic_regression

Train a logistic regression model using gradient descent.

normalize_features

Normalize features using provided mean and std per feature.

predict_linear

Predict outputs for x using linear model weights w.

predict_logistic

Predict binary labels for x using logistic model weights w.