org.tribuo.common.sgd

Interface SGDObjective<T>

Type Parameters:

T - The type of the output at training time.

All Superinterfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>

All Known Subinterfaces:

LabelObjective, MultiLabelObjective, RegressionObjective

All Known Implementing Classes:

AbsoluteLoss, BinaryCrossEntropy, Hinge, Hinge, Huber, LogMulticlass, SquaredLoss

public interface SGDObjective<T>
extends com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<com.oracle.labs.mlrg.olcut.provenance.ConfiguredObjectProvenance>

An interface for a loss function that can produce the loss and gradient incurred by a single prediction.

Method Summary

Modifier and Type	Method and Description
com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector>	lossAndGradient(T truth, SGDVector prediction) Scores a prediction, returning the loss and a vector of per output dimension gradients.

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

getProvenance

Method Detail

lossAndGradient

com.oracle.labs.mlrg.olcut.util.Pair<Double,SGDVector> lossAndGradient(T truth,
                                                                       SGDVector prediction)

Scores a prediction, returning the loss and a vector of per output dimension gradients.

Parameters:

truth - The true output.

prediction - The prediction for each dimension.

Returns:

The score and per dimension gradient.