The empty loss does nothing, letting the user calculate the loss outside the model.
More...
#include <empty_loss.hpp>
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| EmptyLoss () |
| Create the EmptyLoss object.
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template<typename PredictionType , typename TargetType > |
double | Forward (const PredictionType &input, const TargetType &target) |
| Computes the Empty loss function. More...
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template<typename PredictionType , typename TargetType , typename LossType > |
void | Backward (const PredictionType &prediction, const TargetType &target, LossType &loss) |
| Ordinary feed backward pass of a neural network. More...
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template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::EmptyLoss< InputDataType, OutputDataType >
The empty loss does nothing, letting the user calculate the loss outside the model.
- Template Parameters
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InputDataType | Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube). |
OutputDataType | Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube). |
◆ Backward()
template<typename InputDataType , typename OutputDataType >
template<typename PredictionType , typename TargetType , typename LossType >
void mlpack::ann::EmptyLoss< InputDataType, OutputDataType >::Backward |
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const PredictionType & |
prediction, |
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const TargetType & |
target, |
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LossType & |
loss |
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Ordinary feed backward pass of a neural network.
- Parameters
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prediction | Prediction used for evaluating the specified loss function. |
target | The target vector. |
loss | The calculated error. |
◆ Forward()
template<typename InputDataType , typename OutputDataType >
template<typename PredictionType , typename TargetType >
double mlpack::ann::EmptyLoss< InputDataType, OutputDataType >::Forward |
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const PredictionType & |
input, |
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const TargetType & |
target |
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Computes the Empty loss function.
- Parameters
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prediction | Prediction used for evaluating the specified loss function. |
target | The target vector. |
The documentation for this class was generated from the following files: