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 ) Note that labels are represented by a onehot distribution See LossSparseMCXENT for the equivalent but with labels as integers instead 
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
This Loss function requires that the Labels are given as a multihot encoded vector.

LossNegativeLogLikelihood 
Negative log likelihood loss function
In practice, this is implemented as an alias for
LossMCXENT due to the mathematical equivalence 
LossPoisson 
Created by susaneraly on 9/9/16.

LossSparseMCXENT 
Sparse MultiClass Cross Entropy loss function:
L = sum_i actual_i * log( predicted_i ) Note: this is the same loss function as LossMCXENT , the only difference being the format for the labels 
this loss function uses integer indices (zero indexed) for the loss array, whereas LossMCXENT uses the equivalent
onehot representation 
LossSquaredHinge 
Created by susaneraly on 9/9/16.

LossWasserstein 
Wasserstein loss function, which calculates the Wasserstein distance, also known as earthmover's distance.

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