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

The Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. More...

#include <huber_loss.hpp>

Public Member Functions
	HuberLoss (const double delta=1.0, const bool mean=true)
	Create the HuberLoss object. More...
template<typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Huber 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.
double	Delta () const
	Get the value of delta.
double &	Delta ()
	Set the value of delta.
bool	Mean () const
	Get the value of reduction type.
bool &	Mean ()
	Set the value of reduction type.
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::HuberLoss< InputDataType, OutputDataType >

The Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss.

This function is quadratic for small values of \( y - f(x) \), and linear for large values, with equal values and slopes of the different sections at the two points where \( |y - f(x)| = delta \).

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

HuberLoss()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::HuberLoss< InputDataType, OutputDataType >::HuberLoss	(	const double	delta = 1.0,
		const bool	mean = true
	)

Create the HuberLoss object.

Parameters

delta	The threshold value upto which squared error is followed and after which absolute error is considered.
mean	If true then mean loss is computed otherwise sum.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType , typename LossType >

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

Computes the Huber Loss function.

Parameters

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

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

• src/mlpack/methods/ann/loss_functions/huber_loss.hpp
• src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp