Uses of Interface
org.nd4j.linalg.lossfunctions.ILossFunction

Packages that use ILossFunction

Package	Description
org.nd4j.linalg.lossfunctions
org.nd4j.linalg.lossfunctions.impl

Uses of ILossFunction in org.nd4j.linalg.lossfunctions

Methods in org.nd4j.linalg.lossfunctions that return ILossFunction

Modifier and Type	Method and Description
ILossFunction	LossFunctions.LossFunction.getILossFunction()

Uses of ILossFunction in org.nd4j.linalg.lossfunctions.impl

Classes in org.nd4j.linalg.lossfunctions.impl that implement ILossFunction

Modifier and Type	Class and Description
class	LossBinaryXENT Binary cross entropy loss function https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression Labels are assumed to take values 0 or 1
class	LossCosineProximity Created by susaneraly on 9/9/16.
class	LossFMeasure F–measure loss function is a loss function design for training on imbalanced datasets.
class	LossHinge Created by susaneraly on 8/15/16.
class	LossKLD Kullback Leibler Divergence loss function
class	LossL1 L1 loss function: i.e., sum of absolute errors, L = sum_i abs(predicted_i - actual_i) See also LossMAE for a mathematically similar loss function (MAE has division by N, where N is output size)
class	LossL2 L2 loss function: i.e., sum of squared errors, L = sum_i (actual_i - predicted)^2 The L2 loss function is the square of the L2 norm of the difference between actual and predicted.
class	LossMAE Mean absolute error loss function: L = 1/N sum_i abs(predicted_i - actual_i) See also LossL1 for a mathematically similar loss function (LossL1 does not have division by N, where N is output size)
class	LossMAPE Created by susaneraly on 8/15/16.
class	LossMCXENT Multi-Class Cross Entropy loss function: L = sum_i actual_i * log( predicted_i )
class	LossMixtureDensity This is a cost function associated with a mixture-density network.
class	LossMSE Mean Squared Error loss function: L = 1/N sum_i (actual_i - predicted)^2 See also LossL2 for a mathematically similar loss function (LossL2 does not have division by N, where N is output size)
class	LossMSLE Mean Squared Logarithmic Error loss function: L = 1/N sum_i (log(1+predicted_i) - log(1+actual_i))^2
class	LossNegativeLogLikelihood Negative log likelihood loss function
class	LossPoisson Created by susaneraly on 9/9/16.
class	LossSquaredHinge Created by susaneraly on 9/9/16.