jsat.lossfunctions

Class SoftmaxLoss

java.lang.Object

jsat.lossfunctions.LogisticLoss

jsat.lossfunctions.SoftmaxLoss

All Implemented Interfaces:

Serializable, LossC, LossFunc, LossMC

public class SoftmaxLoss
extends LogisticLoss
implements LossMC

The Softmax loss function is a multi-class generalization of the Logistic loss.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SoftmaxLoss()

Method Summary

Modifier and Type	Method and Description
void	deriv(Vec processed, Vec derivs, int y) Computes the derivatives with respect to each output processed and derivs may be the same object, and will simply have all its values altered if so.
CategoricalResults	getClassification(Vec processed) Given the processed predictions, returns the classification results for said predictions.
double	getLoss(Vec processed, int y) Computes the scalar loss for on the given example
void	process(Vec pred, Vec processed) Given the vector of raw outputs for each class, transform it into a new vector.

Methods inherited from class jsat.lossfunctions.LogisticLoss

classify, clone, deriv, deriv2, getClassification, getDeriv, getDeriv2, getDeriv2Max, getLoss, loss

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.lossfunctions.LossC

clone, getClassification, getDeriv, getDeriv2, getLoss

Methods inherited from interface jsat.lossfunctions.LossFunc

getDeriv2Max

Constructor Detail

SoftmaxLoss

public SoftmaxLoss()

Method Detail

getLoss

public double getLoss(Vec processed,
                      int y)

Description copied from interface: LossMC

Computes the scalar loss for on the given example

Specified by:

getLoss in interface LossMC

Parameters:

processed - the vector of raw predictions.

y - the true class label in [0, k-1] for k classes

Returns:

the loss in [0, Inf)

process

public void process(Vec pred,
                    Vec processed)

Description copied from interface: LossMC

Given the vector of raw outputs for each class, transform it into a new vector.
processed and derivs may be the same object, and will simply have all its values altered if so.

Specified by:

process in interface LossMC

Parameters:

pred - the vector of raw predictions

processed - the location to store the processed predictions.

deriv

public void deriv(Vec processed,
                  Vec derivs,
                  int y)

Description copied from interface: LossMC

Computes the derivatives with respect to each output
processed and derivs may be the same object, and will simply have all its values altered if so.

Specified by:

deriv in interface LossMC

Parameters:

processed - the processed predictions

derivs - the vector to place the derivative of the loss to.

y - the true class label in [0, k-1] for k classes

getClassification

public CategoricalResults getClassification(Vec processed)

Description copied from interface: LossMC

Given the processed predictions, returns the classification results for said predictions.

Specified by:

getClassification in interface LossMC

Parameters:

processed - the processed score/prediction vector

Returns:

the classification results