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 double Forward(
const InputType&& input,
const TargetType&& target);
58 template<
typename InputType,
typename TargetType,
typename OutputType>
59 void Backward(
const InputType&& input,
60 const TargetType&& target,
71 template<
typename Archive>
72 void serialize(Archive& ar,
const unsigned int );
76 OutputDataType outputParameter;
83 #include "mean_squared_error_impl.hpp"
double Forward(const InputType &&input, 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.
MeanSquaredError()
Create the MeanSquaredError object.
void Backward(const InputType &&input, const TargetType &&target, OutputType &&output)
Ordinary feed backward pass of a neural network.
The mean squared error performance function measures the network's performance according to the mean ...
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
OutputDataType & OutputParameter()
Modify the output parameter.