L1Loss< InputDataType, OutputDataType > Class Template Reference

The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in the input x and target y. More...

Public Member Functions
	L1Loss (const bool mean=true)
	Create the L1Loss 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...
template < typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the L1 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::L1Loss< InputDataType, OutputDataType >

The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in the input x and target y.

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 33 of file l1_loss.hpp.

Constructor & Destructor Documentation

L1Loss()

L1Loss

(

const bool

mean = true

)

Create the L1Loss object.

Parameters

mean	Reduction type. If true, it returns the mean of the loss. Else, it returns the 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.

Forward()

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

Computes the L1 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 74 of file l1_loss.hpp.

Mean() [2/2]

bool& Mean ( )

inline

Set the value of reduction type.

Definition at line 76 of file l1_loss.hpp.

References L1Loss< InputDataType, OutputDataType >::serialize().

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 69 of file l1_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 71 of file l1_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by L1Loss< 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/l1_loss.hpp