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

Computes the hinge loss between \(y_true\) and \(y_pred\). More...

#include <hinge_loss.hpp>

Public Member Functions
	HingeLoss (const bool reduction=true)
	Create HingeLoss object. More...
template<typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Hinge loss 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	Reduction () const
	Get the type of reduction used.
bool &	Reduction ()
	Modify the type of reduction used.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

Detailed Description

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

Computes the hinge loss between \(y_true\) and \(y_pred\).

Expects \(y_true\) to be either -1 or 1. If \(y_true\) is either 0 or 1, a temporary conversion is made to calculate the loss. The hinge loss \(l(y_true, y_pred)\) is defined as \(l(y_true, y_pred) = max(0, 1 - y_true*y_pred)\).

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

HingeLoss()

template<typename InputDataType , typename OutputDataType >

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

(

const bool

reduction = true

)

Create HingeLoss object.

Parameters

reduction

Specifies the reduction to apply to the output. If false, 'mean' reduction is used, where sum of the output will be divided by the number of elements in the output. If true, 'sum' reduction is used and the output will be summed. It is set to true by default.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType , typename LossType >

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

Ordinary feed backward pass of a neural network.

Parameters

prediction	Prediction 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::HingeLoss< InputDataType, OutputDataType >::Forward	(	const PredictionType &	prediction,
		const TargetType &	target
	)

Computes the Hinge loss function.

Parameters

prediction	Prediction 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/hinge_loss.hpp
• src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp