MeanSquaredLogarithmicError< InputDataType, OutputDataType > Class Template Reference

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 InputType , typename TargetType , typename OutputType >
void	Backward (const InputType &input, const TargetType &target, OutputType &output)
	Ordinary feed backward pass of a neural network. More...
template<typename InputType , typename TargetType >
InputType::elem_type	Forward (const InputType &input, 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 unsigned int)
	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

(

)

Create the MeanSquaredLogarithmicError object.

Member Function Documentation

void Backward	(	const InputType &	input,
		const TargetType &	target,
		OutputType &	output
	)

Ordinary feed backward pass of a neural network.

Parameters

input	The propagated input activation.
target	The target vector.
output	The calculated error.

InputType::elem_type Forward	(	const InputType &	input,
		const TargetType &	target
	)

Computes the mean squared logarithmic error function.

Parameters

input	Input data used for evaluating the specified function.
target	The target vector.

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 64 of file mean_squared_logarithmic_error.hpp.

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 66 of file mean_squared_logarithmic_error.hpp.

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

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

• src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp