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
InputDataTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataTypeType 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
meanReduction 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
predictionPredictions used for evaluating the specified loss function.
targetThe target vector.
lossThe calculated error.

◆ Forward()

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

Computes the L1 Loss function.

Parameters
predictionPredictions used for evaluating the specified loss function.
targetThe 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().


The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/l1_loss.hpp