Class | Description |
---|---|
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
|
LossCosineProximity |
Created by susaneraly on 9/9/16.
|
LossFMeasure |
F–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 |
Multi-Class Cross Entropy loss function:
L = sum_i actual_i * log( predicted_i ) |
LossMixtureDensity |
This is a cost function associated with a mixture-density 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
|
LossNegativeLogLikelihood |
Negative log likelihood loss function
|
LossPoisson |
Created by susaneraly on 9/9/16.
|
LossSquaredHinge |
Created by susaneraly on 9/9/16.
|
Copyright © 2017. All rights reserved.