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...

Public Member Functions
	HuberLoss (const double delta=1.0, const bool mean=true)
	Create the HuberLoss object. 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...
double	Delta () const
	Get the value of delta. More...
double &	Delta ()
	Set the value of delta. More...
template < typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Huber Loss function. More...
bool	Mean () const
	Get the value of reduction type. More...
bool &	Mean ()
	Set the value of reduction type. 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::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 , and linear for large values, with equal values and slopes of the different sections at the two points where .

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 36 of file huber_loss.hpp.

Constructor & Destructor Documentation

HuberLoss()

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()

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 target vector.
loss	The calculated error.

Delta() [1/2]

double Delta ( ) const

inline

Get the value of delta.

Definition at line 78 of file huber_loss.hpp.

Delta() [2/2]

double& Delta ( )

inline

Set the value of delta.

Definition at line 80 of file huber_loss.hpp.

Forward()

PredictionType::elem_type 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.

Mean() [1/2]

bool Mean ( ) const

inline

Get the value of reduction type.

Definition at line 83 of file huber_loss.hpp.

Mean() [2/2]

bool& Mean ( )

inline

Set the value of reduction type.

Definition at line 85 of file huber_loss.hpp.

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

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 73 of file huber_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 75 of file huber_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by HuberLoss< InputDataType, OutputDataType >::Mean().

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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/huber_loss.hpp