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

AsBiasDim

The AsBiasDim trait is used to define a type that can be used to get the bias dimension of the parameters.

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

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 the gradient of a function, which is a function that takes an input and returns a delta, which is the change in the output with respect to the input.

IntoAxis

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

NdLike

OnesLike

RawDimension

the RawDimension trait is used to define a type that can be used as a raw dimension. This trait is primarily used to provide abstracted, generic interpretations of the dimensions of the ndarray crate to ensure long-term compatibility.

Weighted

ZerosLike