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

Public Member Functions
	MeanSquaredError ()
	Create the MeanSquaredError 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 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::MeanSquaredError< InputDataType, OutputDataType >

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

Template Parameters

ActivationFunction	Activation function used for the embedding layer.
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 34 of file mean_squared_error.hpp.

Constructor & Destructor Documentation

◆ MeanSquaredError()

MeanSquaredError ( )

Create the MeanSquaredError 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 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 67 of file mean_squared_error.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 69 of file mean_squared_error.hpp.

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

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by MeanSquaredError< 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_error.hpp

Public Member Functions