12 #ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_LOGARITHMIC_ERROR_HPP 13 #define MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_LOGARITHMIC_ERROR_HPP 30 typename InputDataType = arma::mat,
31 typename OutputDataType = arma::mat
48 template<
typename PredictionType,
typename TargetType>
49 typename PredictionType::elem_type
Forward(
const PredictionType& prediction,
50 const TargetType& target);
60 template<
typename PredictionType,
typename TargetType,
typename LossType>
61 void Backward(
const PredictionType& prediction,
62 const TargetType& target,
73 template<
typename Archive>
74 void serialize(Archive& ar,
const uint32_t );
78 OutputDataType outputParameter;
PredictionType::elem_type Forward(const PredictionType &prediction, const TargetType &target)
Computes the mean squared logarithmic error function.
Definition: mean_squared_logarithmic_error_impl.hpp:31
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1
The core includes that mlpack expects; standard C++ includes and Armadillo.
OutputDataType & OutputParameter()
Modify the output parameter.
Definition: mean_squared_logarithmic_error.hpp:68
void serialize(Archive &ar, const uint32_t)
Serialize the layer.
Definition: mean_squared_logarithmic_error_impl.hpp:52
The mean squared logarithmic error performance function measures the network's performance according ...
Definition: mean_squared_logarithmic_error.hpp:33
OutputDataType & OutputParameter() const
Get the output parameter.
Definition: mean_squared_logarithmic_error.hpp:66
MeanSquaredLogarithmicError()
Create the MeanSquaredLogarithmicError object.
Definition: mean_squared_logarithmic_error_impl.hpp:23
void Backward(const PredictionType &prediction, const TargetType &target, LossType &loss)
Ordinary feed backward pass of a neural network.
Definition: mean_squared_logarithmic_error_impl.hpp:41