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
48 template<
typename InputType,
typename TargetType>
49 typename InputType::elem_type
Forward(
const InputType& input,
50 const TargetType& target);
59 template<
typename InputType,
typename TargetType,
typename OutputType>
61 const TargetType& target,
72 template<
typename Archive>
77 OutputDataType outputParameter;
84#include "mean_squared_error_impl.hpp"
The mean squared error performance function measures the network's performance according to the mean ...
MeanSquaredError()
Create the MeanSquaredError object.
OutputDataType & OutputParameter() const
Get the output parameter.
void Backward(const InputType &input, const TargetType &target, OutputType &output)
Ordinary feed backward pass of a neural network.
InputType::elem_type Forward(const InputType &input, const TargetType &target)
Computes the mean squared error function.
OutputDataType & OutputParameter()
Modify the output parameter.
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
Linear algebra utility functions, generally performed on matrices or vectors.
The core includes that mlpack expects; standard C++ includes and Armadillo.