Crate concision_traits 

Core traits defining fundamental abstractions and operations useful for neural networks.

Modules

math

Mathematically oriented operators and functions useful in machine learning contexts.

ops

composable operators for tensor manipulations and transformations, neural networks, and more

tensor

Traits

Abs

Affine

apply an affine transformation to a tensor; affine transformation is defined as mul * self + add

Apply

Apply is a composable binary operator generally used to apply some object or function onto the caller to produce some output.

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

ApplyMut

ApplyMut provides an interface for mutable containers that can apply a function onto their elements, modifying them in place.

ApplyOnce

The ApplyOnce trait consumes the container and applies the given function to every element before returning a new container with the results.

ArrayLike

AsBiasDim

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

AsComplex

AsComplex defines an interface for converting a reference of some numerical type into a complex number.

Backward

The Backward trait establishes a common interface for completing a single backward step in a neural network or machine learning model.

BackwardStep

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

Conjugate

Cos

Cosh

CrossEntropy

A trait for computing the cross-entropy loss of a tensor or array

Cubed

Decode

Decode defines a standard interface for decoding data.

Decrement

Decrement is a chainable trait that defines a decrement method, effectively removing a single unit from the original object to create another

DecrementAxis

The DecrementAxis is used as a unary operator for removing a single axis from a multidimensional array or tensor-like structure.

DecrementMut

The DecrementMut trait defines a decrement method that operates in place, modifying the original object.

DefaultLike

Dim

the Dim 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.

DimConst

Encode

Encode defines a standard interface for encoding data.

Exp

FillLike

FloorDiv

Forward

The Forward trait describes a common interface for objects designated to perform a single forward step in a neural network or machine learning model.

ForwardMut

ForwardOnce

A consuming implementation of forward propagation

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.

Increment

The Increment

IncrementAxis

The IncrementAxis trait defines a method enabling an axis to increment itself, effectively adding a new axis to the array.

IncrementMut

InitWith

InitWith enables a container to

Initialize

Initialize provides a mechanism for initializing some object using a value of type T to produce another object.

IntoAxis

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

IntoComplex

Trait for converting a type into a complex number.

Inverse

The Inverse trait generically establishes an interface for computing the inverse of a type, regardless of if its a tensor, scalar, or some other compatible type.

IsSquare

IsSquare is a trait for checking if the layout, or dimensionality, of a tensor is square.

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

Loss

The Loss trait defines a common interface for any custom loss function implementations. This trait requires the implementor to define their algorithm for calculating the loss between two values, lhs and rhs, which can be of different types, X and Y respectively. These terms are used generically to allow for flexibility in the allowed types, such as tensors, scalars, or other data structures while clearly defining the “order” in which the operations are performed. It is most common to expect the lhs to be the predicted output and the rhs to be the actual output, but this is not a strict requirement. The trait also defines an associated type Output, which represents the type of the loss value returned by the loss method. This allows for different loss functions to return different types of loss values, such as scalars or tensors, depending on the specific implementation of the loss function.

MapInto

MapInto defines an interface for containers that can consume themselves to apply a given function onto each of their elements.

MapTo

MapTo establishes an interface for containers capable of applying a given function onto each of their elements, by reference.

MaskFill

This trait is used to fill an array with a value based on a mask. The mask is a boolean array of the same shape as the array.

MatMul

The MatMul trait defines an interface for matrix multiplication.

MatPow

The MatPow trait defines an interface for computing the power of some matrix

MeanAbsoluteError

A trait for computing the mean absolute error of a tensor or array

MeanSquaredError

A trait for computing the mean squared error of a tensor or array

NdGradient

NdLike

NdTensor

Norm

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

OnesLike

PercentChange

The PercentChange trait establishes a binary operator for computing the percent change between two values where the caller is considered the original value.

PercentDiff

Compute the percentage difference between two values. The percentage difference is defined as:

Predict

The Predict trait is designed as a model-specific interface for making predictions. In the future, we may consider opening the trait up allowing for an alternative implementation of the trait, but for now, it is simply implemented for all implementors of the Forward trait.

PredictWithConfidence

The PredictWithConfidence trait is an extension of the Predict trait, providing an additional method to obtain predictions along with a confidence score.

RawStore

The RawStore trait is used to define an interface for key-value stores like hash-maps, dictionaries, and similar data structures.

RawStoreMut

RawStoreMut extends the RawStore trait by introducing various mutable operations and accessors for elements within the store.

RawTensor

RawTensorData

Root

The Root trait provides methods for computing the nth root of a number.

RoundTo

ScalarTensorData

A marker trait used to denote tensors that represent scalar values; more specifically, we consider any type implementing the RawTensorData type where the Elem associated type is the implementor itself a scalar value.

Sine

Sinh

SquareRoot

Squared

Store

The Store trait is a more robust interface for key-value stores, building upon both RawStore and RawStoreMut traits by introducing an entry method for in-place manipulation of key-value pairs.

StoreEntry

The StoreEntry trait establishes a common interface for all entries within a key-value store. These types enable in-place manipulation of key-value pairs by allowing for keys to point to empty or vacant slots within the store.

SummaryStatistics

This trait describes the fundamental methods of summary statistics. These include the mean, standard deviation, variance, and more.

Tan

Tanh

TensorBase

Train

This trait defines the training process for the network

Transpose

The Transpose trait generically establishes an interface for transposing a type

Unsqueeze

The Unsqueeze trait establishes an interface for a routine that unsqueezes an array, by inserting a new axis at a specified position. This is useful for reshaping arrays to meet specific dimensional requirements.

ZerosLike