absolute loss More...
#include <shark/ObjectiveFunctions/Loss/AbsoluteLoss.h>
Public Types | |
typedef base_type::BatchLabelType | BatchLabelType |
typedef base_type::BatchOutputType | BatchOutputType |
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typedef VectorType | OutputType |
typedef VectorType | LabelType |
typedef VectorMatrixTraits< OutputType >::DenseMatrixType | MatrixType |
typedef Batch< OutputType >::type | BatchOutputType |
typedef Batch< LabelType >::type | BatchLabelType |
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enum | Feature |
list of features a cost function can have More... | |
typedef VectorType | OutputType |
typedef VectorType | LabelType |
typedef Batch< OutputType >::type | BatchOutputType |
typedef Batch< LabelType >::type | BatchLabelType |
typedef TypedFlags< Feature > | Features |
typedef TypedFeatureNotAvailableException< Feature > | FeatureNotAvailableException |
Public Member Functions | |
AbsoluteLoss () | |
constructor More... | |
std::string | name () const |
From INameable: return the class name. More... | |
double | eval (BatchLabelType const &labels, BatchOutputType const &predictions) const |
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AbstractLoss () | |
virtual double | eval (LabelType const &target, OutputType const &prediction) const |
evaluate the loss for a target and a prediction More... | |
double | eval (Data< LabelType > const &targets, Data< OutputType > const &predictions) const |
virtual double | evalDerivative (LabelType const &target, OutputType const &prediction, OutputType &gradient) const |
evaluate the loss and its derivative for a target and a prediction More... | |
virtual double | evalDerivative (LabelType const &target, OutputType const &prediction, OutputType &gradient, MatrixType &hessian) const |
evaluate the loss and its first and second derivative for a target and a prediction More... | |
virtual double | evalDerivative (BatchLabelType const &target, BatchOutputType const &prediction, BatchOutputType &gradient) const |
evaluate the loss and the derivative w.r.t. the prediction More... | |
double | operator() (LabelType const &target, OutputType const &prediction) const |
evaluate the loss for a target and a prediction More... | |
double | operator() (BatchLabelType const &target, BatchOutputType const &prediction) const |
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virtual | ~AbstractCost () |
const Features & | features () const |
virtual void | updateFeatures () |
bool | hasFirstDerivative () const |
returns true when the first parameter derivative is implemented More... | |
bool | isLossFunction () const |
returns true when the cost function is in fact a loss function More... | |
double | operator() (Data< LabelType > const &targets, Data< OutputType > const &predictions) const |
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virtual | ~INameable () |
Additional Inherited Members | |
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Features | m_features |
absolute loss
The absolute loss is usually defined in a single dimension as the absolute value of the difference between labels and predictions. Here we generalize to multiple dimensions by returning the norm.
Definition at line 50 of file AbsoluteLoss.h.
typedef base_type::BatchLabelType shark::AbsoluteLoss< VectorType >::BatchLabelType |
Definition at line 54 of file AbsoluteLoss.h.
typedef base_type::BatchOutputType shark::AbsoluteLoss< VectorType >::BatchOutputType |
Definition at line 55 of file AbsoluteLoss.h.
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inline |
constructor
Definition at line 58 of file AbsoluteLoss.h.
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inlinevirtual |
evaluate the loss \( \| labels - predictions \| \), which is a slight generalization of the absolute value of the difference.
Implements shark::AbstractLoss< VectorType, VectorType >.
Definition at line 71 of file AbsoluteLoss.h.
References shark::blas::distance(), shark::blas::row(), and SIZE_CHECK.
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inlinevirtual |
From INameable: return the class name.
Reimplemented from shark::INameable.
Definition at line 63 of file AbsoluteLoss.h.
References shark::AbstractLoss< VectorType, VectorType >::eval().