12 #ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_L1_LOSS_HPP    13 #define MLPACK_METHODS_ANN_LOSS_FUNCTION_L1_LOSS_HPP    30     typename InputDataType = arma::mat,
    31     typename OutputDataType = arma::mat
    42   L1Loss(
const bool mean = 
true);
    51   template<
typename PredictionType, 
typename TargetType>
    52   typename PredictionType::elem_type 
Forward(
const PredictionType& prediction,
    53                                              const TargetType& target);
    63   template<
typename PredictionType, 
typename TargetType, 
typename LossType>
    64   void Backward(
const PredictionType& prediction,
    65                 const TargetType& target,
    74   bool Mean()
 const { 
return mean; }
    76   bool& 
Mean() { 
return mean; }
    81   template<
typename Archive>
    82   void serialize(Archive& ar, 
const uint32_t );
    86   OutputDataType outputParameter;
    96 #include "l1_loss_impl.hpp" void serialize(Archive &ar, const uint32_t)
Serialize the layer. 
 
PredictionType::elem_type Forward(const PredictionType &prediction, const TargetType &target)
Computes the L1 Loss function. 
 
Linear algebra utility functions, generally performed on matrices or vectors. 
 
OutputDataType & OutputParameter()
Modify the output parameter. 
 
The core includes that mlpack expects; standard C++ includes and Armadillo. 
 
OutputDataType & OutputParameter() const
Get the output parameter. 
 
void Backward(const PredictionType &prediction, const TargetType &target, LossType &loss)
Ordinary feed backward pass of a neural network. 
 
bool & Mean()
Set the value of reduction type. 
 
bool Mean() const
Get the value of reduction type. 
 
L1Loss(const bool mean=true)
Create the L1Loss object. 
 
The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in th...