MarginRankingLoss< InputDataType, OutputDataType > Class Template Reference

Margin ranking loss measures the loss given inputs and a label vector with values of 1 or -1. More...

Public Member Functions
	MarginRankingLoss (const double margin=1.0)
	Create the MarginRankingLoss object with Hyperparameter margin. 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...
template < typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Margin Ranking Loss function. More...
double	Margin () const
	Get the margin parameter. More...
double &	Margin ()
	Modify the margin parameter. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Detailed Description

template

<

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

class mlpack::ann::MarginRankingLoss< InputDataType, OutputDataType >

Margin ranking loss measures the loss given inputs and a label vector with values of 1 or -1.

If the label is 1 then the first input should be ranked higher than the second input at a distance larger than a margin, and vice- versa if the label is -1.

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).

Definition at line 35 of file margin_ranking_loss.hpp.

Constructor & Destructor Documentation

MarginRankingLoss()

MarginRankingLoss

(

const double

margin = 1.0

)

Create the MarginRankingLoss object with Hyperparameter margin.

Hyperparameter margin defines a minimum distance between correctly ranked samples.

Member Function Documentation

Backward()

void 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 label vector which contains -1 or 1 values.
loss	The calculated error.

Forward()

PredictionType::elem_type Forward	(	const PredictionType &	prediction,
		const TargetType &	target
	)

Computes the Margin Ranking Loss function.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	The label vector which contains values of -1 or 1.

Margin() [1/2]

double Margin ( ) const

inline

Get the margin parameter.

Definition at line 79 of file margin_ranking_loss.hpp.

Margin() [2/2]

double& Margin ( )

inline

Modify the margin parameter.

Definition at line 81 of file margin_ranking_loss.hpp.

References MarginRankingLoss< InputDataType, OutputDataType >::serialize().

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 74 of file margin_ranking_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 76 of file margin_ranking_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by MarginRankingLoss< InputDataType, OutputDataType >::Margin().

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp