Module init

this module establishes generic random initialization routines for models, params, and tensors. This module works to provide the crate with various initialization methods suitable for machine-learning models.

Modules

distr

Structs

LecunNormal

LecunNormal is a truncated normal distribution centered at 0 with a standard deviation that is calculated as σ = sqrt(1/n_in) where n_in is the number of input units.

TruncatedNormal

A truncated normal distribution is similar to a normal distribution, however, any generated value over two standard deviations from the mean is discarded and re-generated.

XavierNormal

Normal Xavier initializers leverage a normal distribution with a mean of 0 and a standard deviation (σ) computed by the formula: σ = sqrt(2/(d_in + d_out))

XavierUniform

Uniform Xavier initializers use a uniform distribution to initialize the weights of a neural network within a given range.

Traits

Init

A trait for creating custom initialization routines for models or other entities.

InitInplace

This trait enables models to implement custom, in-place initialization methods.

Initialize

This trait provides the base methods required for initializing tensors with random values. The trait is similar to the RandomExt trait provided by the ndarray_rand crate, however, it is designed to be more generic, extensible, and optimized for neural network initialization routines. Initialize is implemented for ArrayBase as well as ParamsBase allowing you to randomly initialize new tensors and parameters.

Functions

randc

Generate a random array of complex numbers with real and imaginary parts in the range [0, 1)

stdnorm

Given a shape, generate a random array using the StandardNormal distribution

stdnorm_from_seed

uniform_from_seed

Creates a random array from a uniform distribution using a given key

Type Aliases

UniformResult