12 #ifndef MLPACK_METHODS_ANN_LAYER_MEAN_SQUARED_ERROR_HPP 13 #define MLPACK_METHODS_ANN_LAYER_MEAN_SQUARED_ERROR_HPP 31 typename InputDataType = arma::mat,
32 typename OutputDataType = arma::mat
49 double Forward(
const arma::Mat<eT>&& input,
const arma::Mat<eT>&& target);
58 void Backward(
const arma::Mat<eT>&& input,
59 const arma::Mat<eT>&& target,
60 arma::Mat<eT>&& output);
80 template<
typename Archive>
81 void Serialize(Archive& ar,
const unsigned int );
98 #include "mean_squared_error_impl.hpp" Linear algebra utility functions, generally performed on matrices or vectors.
OutputDataType & OutputParameter() const
Get the output parameter.
double Forward(const arma::Mat< eT > &&input, const arma::Mat< eT > &&target)
The core includes that mlpack expects; standard C++ includes and Armadillo.
InputDataType inputParameter
Locally-stored input parameter object.
InputDataType & InputParameter()
Modify the input parameter.
OutputDataType & Delta()
Modify the delta.
OutputDataType & OutputParameter()
Modify the output parameter.
The mean squared error performance function measures the network's performance according to the mean ...
OutputDataType outputParameter
Locally-stored output parameter object.
InputDataType & InputParameter() const
Get the input parameter.
OutputDataType delta
Locally-stored delta object.
MeanSquaredError()
Create the MeanSquaredError object.
void Serialize(Archive &ar, const unsigned int)
Serialize the layer.
OutputDataType & Delta() const
Get the delta.
void Backward(const arma::Mat< eT > &&input, const arma::Mat< eT > &&target, arma::Mat< eT > &&output)
Ordinary feed backward pass of a neural network.