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...

 
template
<
typename
InputType
,
typename
TargetType
>
double Forward (const InputType &&input, 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 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
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 InputType &&  input,
const TargetType &&  target,
OutputType &&  output 
)

Ordinary feed backward pass of a neural network.

Parameters
inputThe propagated input activation.
targetThe target vector.
outputThe calculated error.

◆ Forward()

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

Computes the mean squared error function.

Parameters
inputInput data used for evaluating the specified function.
targetThe target vector.

◆ OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 63 of file mean_squared_error.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 65 of file mean_squared_error.hpp.

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

◆ serialize()

void serialize ( Archive &  ar,
const unsigned  int 
)

The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-3.2.1/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp