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
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
ActivationFunctionActivation function used for the embedding layer.
InputDataTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataTypeType 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()

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
predictionPredictions used for evaluating the specified loss function
targetThe target vector.
lossThe calculated error.

◆ Forward()

PredictionType::elem_type Forward ( const PredictionType &  prediction,
const TargetType &  target 
)

Computes the mean squared error function.

Parameters
predictionPredictions used for evaluating the specified loss function.
targetThe 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   
)

The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp