12 #ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_ERROR_HPP    13 #define MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_ERROR_HPP    31     typename InputDataType = arma::mat,
    32     typename OutputDataType = arma::mat
    49   template<
typename PredictionType, 
typename TargetType>
    50   typename PredictionType::elem_type 
Forward(
const PredictionType& prediction,
    51                                              const TargetType& target);
    61   template<
typename PredictionType, 
typename TargetType, 
typename LossType>
    62   void Backward(
const PredictionType& prediction,
    63                 const TargetType& target,
    74   template<
typename Archive>
    75   void serialize(Archive& ar, 
const uint32_t );
    79   OutputDataType outputParameter;
    86 #include "mean_squared_error_impl.hpp" void serialize(Archive &ar, const uint32_t)
Serialize the layer. 
 
Linear algebra utility functions, generally performed on matrices or vectors. 
 
PredictionType::elem_type Forward(const PredictionType &prediction, const TargetType &target)
Computes the mean squared error function. 
 
The core includes that mlpack expects; standard C++ includes and Armadillo. 
 
OutputDataType & OutputParameter() const
Get the output parameter. 
 
void Backward(const PredictionType &prediction, const TargetType &target, LossType &loss)
Ordinary feed backward pass of a neural network. 
 
MeanSquaredError()
Create the MeanSquaredError object. 
 
The mean squared error performance function measures the network's performance according to the mean ...
 
OutputDataType & OutputParameter()
Modify the output parameter.