The mean squared logarithmic error performance function measures the network's performance according to the mean of squared logarithmic errors. More...

Public Member Functions
	MeanSquaredLogarithmicError ()
	Create the MeanSquaredLogarithmicError 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 mean squared logarithmic error function. 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::MeanSquaredLogarithmicError< InputDataType, OutputDataType >

The mean squared logarithmic error performance function measures the network's performance according to the mean of squared logarithmic errors.

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 mean_squared_logarithmic_error.hpp.

Constructor & Destructor Documentation

◆ MeanSquaredLogarithmicError()

MeanSquaredLogarithmicError ( )

Create the MeanSquaredLogarithmicError object.

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 mean squared logarithmic error function.

Parameters

prediction	Predictions used for evaluating the specified loss function.
target	The target vector.

◆ OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 66 of file mean_squared_logarithmic_error.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 68 of file mean_squared_logarithmic_error.hpp.

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

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by MeanSquaredLogarithmicError< InputDataType, OutputDataType >::OutputParameter().

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/mean_squared_logarithmic_error.hpp

Public Member Functions