Module traits

This module provides the core traits for the library, such as Backward and Forward

Traits

ApplyGradient

A trait declaring basic gradient-related routines for a neural network

ApplyGradientExt

This trait extends the ApplyGradient trait by allowing for momentum-based optimization

ArrayLike

Backward

Backward propagate a delta through the system;

Biased

Clip

A trait denoting objects capable of being clipped between some minimum and some maximum.

ClipMut

This trait enables tensor clipping; it is implemented for ArrayBase

Codex

Decode

Decode defines a standard interface for decoding data.

DefaultLike

DropOut

[Dropout] randomly zeroizes elements with a given probability (p).

Encode

Encode defines a standard interface for encoding data.

FillLike

Forward

This trait denotes entities capable of performing a single forward step

Gradient

The Gradient trait defines a common interface for all gradients

IntoAxis

The IntoAxis trait is used to define a conversion routine that takes a type and wraps it in an Axis type.

L1Norm

a trait for computing the L1 norm of a tensor or array

L2Norm

a trait for computing the L2 norm of a tensor or array

NdLike

Norm

The Norm trait serves as a unified interface for various normalization routnines. At the moment, the trait provides L1 and L2 techniques.

Numerical

Numerical is a trait for all numerical types; implements a number of core operations

OnesLike

RawTensor

The RawTensor trait defines the base interface for all tensors,

Scalar

The Scalar trait extends the Numerical trait to include additional mathematical operations for the purpose of reducing the number of overall traits required to complete various machine-learning tasks.

ScalarComplex

Tensor

The Tensor trait extends the RawTensor trait to provide additional functionality for tensors, such as creating tensors from shapes, applying functions, and iterating over elements. It is generic over the element type A and the dimension type `D

Weighted

ZerosLike