FlexNN::Layers::BatchNorm1D Class Reference

#include <BatchNorm1D.hpp>

Public Member Functions
	BatchNorm1D (BatchNormParams p, Activations::Activation act=Activations::Activation::None)
	BatchNorm1D (int numFeatures, float epsilon=1e-5f, Activations::Activation act=Activations::Activation::None)
LayerType	type () const noexcept
Activations::Activation	activation () const noexcept
const BatchNormParams &	params () const noexcept
const Eigen::VectorXd &	gamma () const noexcept
const Eigen::VectorXd &	beta () const noexcept
const Eigen::VectorXd &	runningMean () const noexcept
const Eigen::VectorXd &	runningVar () const noexcept
void	setGamma (const Eigen::VectorXd &g)
void	setBeta (const Eigen::VectorXd &b)
void	setRunningMean (const Eigen::VectorXd &m)
void	setRunningVar (const Eigen::VectorXd &v)
std::pair< Eigen::MatrixXd, Eigen::MatrixXd >	forward (const Eigen::MatrixXd &input) const
	Forward with batch stats (training) — updates running stats.
std::pair< Eigen::MatrixXd, Eigen::MatrixXd >	forward (const Eigen::MatrixXd &input, bool training) const
	Forward with explicit training flag.
Eigen::MatrixXd	backward (const Eigen::MatrixXd &upstream, const Eigen::MatrixXd &currZ) const
Eigen::MatrixXd	backward (const Eigen::MatrixXd &nextW, const Eigen::MatrixXd &nextdZ, const Eigen::MatrixXd &currZ) const
Eigen::MatrixXd	propagate (const Eigen::MatrixXd &dZ) const
std::pair< Eigen::MatrixXd, Eigen::VectorXd >	grad (const Eigen::MatrixXd &dZ, const Eigen::MatrixXd &input) const
void	update (const Eigen::MatrixXd &dW, const Eigen::VectorXd &db, double lr) noexcept
void	updateWeights (const Eigen::MatrixXd &dW, const Eigen::VectorXd &db, double lr) noexcept

Constructor & Destructor Documentation

BatchNorm1D() [1/2]

FlexNN::Layers::BatchNorm1D::BatchNorm1D ( BatchNormParams  p, Activations::Activation  act = Activations::Activation::None  )

explicit

BatchNorm1D() [2/2]

FlexNN::Layers::BatchNorm1D::BatchNorm1D ( int  numFeatures, float  epsilon = 1e-5f, Activations::Activation  act = Activations::Activation::None  )

explicit

Member Function Documentation

activation()

Activations::Activation FlexNN::Layers::BatchNorm1D::activation ( ) const

inlinenoexcept

backward() [1/2]

Eigen::MatrixXd FlexNN::Layers::BatchNorm1D::backward	(	const Eigen::MatrixXd &	nextW,
		const Eigen::MatrixXd &	nextdZ,
		const Eigen::MatrixXd &	currZ
	)		const

backward() [2/2]

Eigen::MatrixXd FlexNN::Layers::BatchNorm1D::backward	(	const Eigen::MatrixXd &	upstream,
		const Eigen::MatrixXd &	currZ
	)		const

beta()

const Eigen::VectorXd & FlexNN::Layers::BatchNorm1D::beta ( ) const

inlinenoexcept

forward() [1/2]

std::pair< Eigen::MatrixXd, Eigen::MatrixXd > FlexNN::Layers::BatchNorm1D::forward

(

const Eigen::MatrixXd &

input

)

const

Forward with batch stats (training) — updates running stats.

Uses batch mean/var per feature (row) across batch*L_out? For 1D case with flattened [F*L × batch] input, we treat each row as a feature and compute mean/var over cols (batch). This matches the flattened Dense/BatchNorm stack where each feature is a separate row.

Parameters

input

[F × batch] or [F*L × batch] (each row is a feature)

Returns

Pair (Z, A) where Z is normalized y and A = act(Z) (None)

forward() [2/2]

std::pair< Eigen::MatrixXd, Eigen::MatrixXd > FlexNN::Layers::BatchNorm1D::forward	(	const Eigen::MatrixXd &	input,
		bool	training
	)		const

Forward with explicit training flag.

When training==false, uses runningMean/Var (for predict).

gamma()

const Eigen::VectorXd & FlexNN::Layers::BatchNorm1D::gamma ( ) const

inlinenoexcept

grad()

std::pair< Eigen::MatrixXd, Eigen::VectorXd > FlexNN::Layers::BatchNorm1D::grad	(	const Eigen::MatrixXd &	dZ,
		const Eigen::MatrixXd &	input
	)		const

params()

const BatchNormParams & FlexNN::Layers::BatchNorm1D::params ( ) const

inlinenoexcept

propagate()

Eigen::MatrixXd FlexNN::Layers::BatchNorm1D::propagate

(

const Eigen::MatrixXd &

dZ

)

const

runningMean()

const Eigen::VectorXd & FlexNN::Layers::BatchNorm1D::runningMean ( ) const

inlinenoexcept

runningVar()

const Eigen::VectorXd & FlexNN::Layers::BatchNorm1D::runningVar ( ) const

inlinenoexcept

setBeta()

void FlexNN::Layers::BatchNorm1D::setBeta ( const Eigen::VectorXd &  b )

inline

setGamma()

void FlexNN::Layers::BatchNorm1D::setGamma ( const Eigen::VectorXd &  g )

inline

setRunningMean()

void FlexNN::Layers::BatchNorm1D::setRunningMean ( const Eigen::VectorXd &  m )

inline

setRunningVar()

void FlexNN::Layers::BatchNorm1D::setRunningVar ( const Eigen::VectorXd &  v )

inline

type()

LayerType FlexNN::Layers::BatchNorm1D::type ( ) const

inlinenoexcept

update()

void FlexNN::Layers::BatchNorm1D::update ( const Eigen::MatrixXd &  dW, const Eigen::VectorXd &  db, double  lr  )

noexcept

updateWeights()

void FlexNN::Layers::BatchNorm1D::updateWeights ( const Eigen::MatrixXd &  dW, const Eigen::VectorXd &  db, double  lr  )

inlinenoexcept

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The documentation for this class was generated from the following files:

• include/layers/BatchNorm1D.hpp
• lib/layers/BatchNorm1D.cpp