12#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_RECONSTRUCTION_LOSS_HPP
13#define MLPACK_METHODS_ANN_LOSS_FUNCTION_RECONSTRUCTION_LOSS_HPP
33 typename InputDataType = arma::mat,
34 typename OutputDataType = arma::mat,
35 typename DistType = BernoulliDistribution<InputDataType>
51 template<
typename InputType,
typename TargetType>
52 typename InputType::elem_type
Forward(
const InputType& input,
53 const TargetType& target);
62 template<
typename InputType,
typename TargetType,
typename OutputType>
64 const TargetType& target,
75 template<
typename Archive>
83 OutputDataType outputParameter;
90#include "reconstruction_loss_impl.hpp"
The reconstruction loss performance function measures the network's performance equal to the negative...
ReconstructionLoss()
Create the ReconstructionLoss 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 reconstruction loss.
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.