mlpack::ann::KLDivergence< InputDataType, OutputDataType > Class Template Reference

The Kullback–Leibler divergence is often used for continuous distributions (direct regression). More...

#include <kl_divergence.hpp>

Public Member Functions
	KLDivergence (const bool takeMean=false)
	Create the Kullback–Leibler Divergence object with the specified parameters. More...
template<typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Kullback–Leibler divergence error function. More...
template<typename PredictionType , typename TargetType , typename LossType >
void	Backward (const PredictionType &prediction, const TargetType &target, LossType &loss)
	Ordinary feed backward pass of a neural network. More...
OutputDataType &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
bool	TakeMean () const
	Get the value of takeMean.
bool &	TakeMean ()
	Modify the value of takeMean.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the loss function.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::KLDivergence< InputDataType, OutputDataType >

The Kullback–Leibler divergence is often used for continuous distributions (direct regression).

For more information, see the following paper.

article{Kullback1951,
  title   = {On Information and Sufficiency},
  author  = {S. Kullback, R.A. Leibler},
  journal = {The Annals of Mathematical Statistics},
  year    = {1951}
}

Template Parameters

InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Constructor & Destructor Documentation

KLDivergence()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::KLDivergence< InputDataType, OutputDataType >::KLDivergence

(

const bool

takeMean = false

)

Create the Kullback–Leibler Divergence object with the specified parameters.

Parameters

takeMean

Boolean variable to specify whether to take mean or not.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType , typename LossType >

void mlpack::ann::KLDivergence< InputDataType, OutputDataType >::Backward	(	const PredictionType &	prediction,
		const TargetType &	target,
		LossType &	loss
	)

Ordinary feed backward pass of a neural network.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	The target vector.
loss	The calculated error.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType >

PredictionType::elem_type mlpack::ann::KLDivergence< InputDataType, OutputDataType >::Forward	(	const PredictionType &	prediction,
		const TargetType &	target
	)

Computes the Kullback–Leibler divergence error function.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	Target data to compare with.

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

• src/mlpack/methods/ann/loss_functions/kl_divergence.hpp
• src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp