12 #ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_HUBER_LOSS_HPP 13 #define MLPACK_METHODS_ANN_LOSS_FUNCTION_HUBER_LOSS_HPP 33 typename InputDataType = arma::mat,
34 typename OutputDataType = arma::mat
46 HuberLoss(
const double delta = 1.0,
const bool mean =
true);
54 template<
typename InputType,
typename TargetType>
55 typename InputType::elem_type
Forward(
const InputType& input,
56 const TargetType& target);
65 template<
typename InputType,
typename TargetType,
typename OutputType>
66 void Backward(
const InputType& input,
67 const TargetType& target,
76 double Delta()
const {
return delta; }
78 double&
Delta() {
return delta; }
81 bool Mean()
const {
return mean; }
83 bool&
Mean() {
return mean; }
88 template<
typename Archive>
89 void serialize(Archive& ar,
const unsigned int );
93 OutputDataType outputParameter;
106 #include "huber_loss_impl.hpp" The Huber loss is a loss function used in robust regression, that is less sensitive to outliers in da...
Linear algebra utility functions, generally performed on matrices or vectors.
double Delta() const
Get the value of delta.
The core includes that mlpack expects; standard C++ includes and Armadillo.
bool Mean() const
Get the value of reduction type.
bool & Mean()
Set the value of reduction type.
double & Delta()
Set the value of delta.
OutputDataType & OutputParameter() const
Get the output parameter.
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
HuberLoss(const double delta=1.0, const bool mean=true)
Create the HuberLoss object.
InputType::elem_type Forward(const InputType &input, const TargetType &target)
Computes the Huber Loss function.
void Backward(const InputType &input, const TargetType &target, OutputType &output)
Ordinary feed backward pass of a neural network.
OutputDataType & OutputParameter()
Modify the output parameter.