Class  Description 

LossBinaryXENT 
Binary cross entropy loss function
https://en.wikipedia.org/wiki/Cross_entropy#Crossentropy_error_function_and_logistic_regression
Labels are assumed to take values 0 or 1

LossCosineProximity 
Created by susaneraly on 9/9/16.

LossFMeasure 
Fâ€“measure loss function is a loss function design for training on imbalanced datasets.

LossHinge 
Created by susaneraly on 8/15/16.

LossKLD 
Kullback Leibler Divergence loss function

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) 
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.

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) 
LossMAPE 
Created by susaneraly on 8/15/16.

LossMCXENT 
MultiClass Cross Entropy loss function:
L = sum_i actual_i * log( predicted_i ) 
LossMixtureDensity 
This is a cost function associated with a mixturedensity network.

LossMixtureDensity.Builder  
LossMixtureDensity.MixtureDensityComponents 
This class is a data holder for the mixture density
components for convenient manipulation.

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) 
LossMSLE 
Mean Squared Logarithmic Error loss function: L = 1/N sum_i (log(1+predicted_i)  log(1+actual_i))^2

LossMultiLabel 
MultiLabelLoss Function, maybe more commonly known as BPMLL

LossNegativeLogLikelihood 
Negative log likelihood loss function

LossPoisson 
Created by susaneraly on 9/9/16.

LossSquaredHinge 
Created by susaneraly on 9/9/16.
