mlpack::ann::LogCoshLoss< InputDataType, OutputDataType > Class Template Reference

The Log-Hyperbolic-Cosine loss function is often used to improve variational auto encoder. More...

#include <log_cosh_loss.hpp>

Public Member Functions
	LogCoshLoss (const double a=1.0)
	Create the Log-Hyperbolic-Cosine object with the specified parameters. More...
template<typename PredictionType , typename TargetType >
PredictionType::elem_type	Forward (const PredictionType &prediction, const TargetType &target)
	Computes the Log-Hyperbolic-Cosine loss function. 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...
OutputDataType &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
double	A () const
	Get the value of hyperparameter a.
double &	A ()
	Modify the value of hyperparameter a.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the loss function.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::LogCoshLoss< InputDataType, OutputDataType >

The Log-Hyperbolic-Cosine loss function is often used to improve variational auto encoder.

This function is the log of hyperbolic cosine of difference between true values and predicted values.

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).

Constructor & Destructor Documentation

LogCoshLoss()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::LogCoshLoss< InputDataType, OutputDataType >::LogCoshLoss

(

const double

a = 1.0

)

Create the Log-Hyperbolic-Cosine object with the specified parameters.

Parameters

a	A double type value for smoothening loss function. It must be positive a real number, Sharpness of loss function is directly proportional to a. It can also act as a scaling factor hence making the loss function more sensitive to small losses around the origin. Default value = 1.0.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType , typename LossType >

void mlpack::ann::LogCoshLoss< InputDataType, OutputDataType >::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()

template<typename InputDataType , typename OutputDataType >

template<typename PredictionType , typename TargetType >

PredictionType::elem_type mlpack::ann::LogCoshLoss< InputDataType, OutputDataType >::Forward	(	const PredictionType &	prediction,
		const TargetType &	target
	)

Computes the Log-Hyperbolic-Cosine loss function.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	Target data to compare with.

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The documentation for this class was generated from the following files:

• src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp
• src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp