MeanSquaredError< InputDataType, OutputDataType > Class Template Reference

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 InputType , typename TargetType , typename OutputType >
void	Backward (const InputType &&input, const TargetType &&target, OutputType &&output)
	Ordinary feed backward pass of a neural network. More...
OutputDataType &	Delta () const
	Get the delta. More...
OutputDataType &	Delta ()
	Modify the delta. More...
template < typename InputType , typename TargetType >
double	Forward (const InputType &&input, const TargetType &&target)
InputDataType &	InputParameter () const
	Get the input parameter. More...
InputDataType &	InputParameter ()
	Modify the input parameter. 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::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 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.

Delta() [1/2]

OutputDataType& Delta ( ) const

inline

Get the delta.

Definition at line 73 of file mean_squared_error.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 75 of file mean_squared_error.hpp.

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

Forward()

double Forward	(	const InputType &&	input,
		const TargetType &&	target
	)

InputParameter() [1/2]

InputDataType& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 63 of file mean_squared_error.hpp.

InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 65 of file mean_squared_error.hpp.

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 68 of file mean_squared_error.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 70 of file mean_squared_error.hpp.

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

Referenced by MeanSquaredError< InputDataType, OutputDataType >::Delta().

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

• src/mlpack/methods/ann/layer/mean_squared_error.hpp