DO NOT EDIT.
This file was autogenerated. Do not edit it by hand, since your modifications would be overwritten.
Classes
class BinaryCrossentropy: Computes the cross-entropy loss between true labels and predicted labels.
class BinaryFocalCrossentropy: Computes focal cross-entropy loss between true labels and predictions.
class CTC: CTC (Connectionist Temporal Classification) loss.
class CategoricalCrossentropy: Computes the crossentropy loss between the labels and predictions.
class CategoricalFocalCrossentropy: Computes the alpha balanced focal crossentropy loss.
class CategoricalHinge: Computes the categorical hinge loss between y_true & y_pred.
class CosineSimilarity: Computes the cosine similarity between y_true & y_pred.
class Dice: Computes the Dice loss value between y_true and y_pred.
class Hinge: Computes the hinge loss between y_true & y_pred.
class Huber: Computes the Huber loss between y_true & y_pred.
class KLDivergence: Computes Kullback-Leibler divergence loss between y_true & y_pred.
class LogCosh: Computes the logarithm of the hyperbolic cosine of the prediction error.
class Loss: Loss base class.
class MeanAbsoluteError: Computes the mean of absolute difference between labels and predictions.
class MeanAbsolutePercentageError: Computes the mean absolute percentage error between y_true & y_pred.
class MeanSquaredError: Computes the mean of squares of errors between labels and predictions.
class MeanSquaredLogarithmicError: Computes the mean squared logarithmic error between y_true & y_pred.
class Poisson: Computes the Poisson loss between y_true & y_pred.
class SparseCategoricalCrossentropy: Computes the crossentropy loss between the labels and predictions.
class SquaredHinge: Computes the squared hinge loss between y_true & y_pred.
class Tversky: Computes the Tversky loss value between y_true and y_pred.
Functions
KLD(...): Computes Kullback-Leibler divergence loss between y_true & y_pred.
MAE(...): Computes the mean absolute error between labels and predictions.
MAPE(...): Computes the mean absolute percentage error between y_true & y_pred.
MSE(...): Computes the mean squared error between labels and predictions.
MSLE(...): Computes the mean squared logarithmic error between y_true & y_pred.
binary_crossentropy(...): Computes the binary crossentropy loss.
binary_focal_crossentropy(...): Computes the binary focal crossentropy loss.
categorical_crossentropy(...): Computes the categorical crossentropy loss.
categorical_focal_crossentropy(...): Computes the categorical focal crossentropy loss.
categorical_hinge(...): Computes the categorical hinge loss between y_true & y_pred.
cosine_similarity(...): Computes the cosine similarity between labels and predictions.
ctc(...): CTC (Connectionist Temporal Classification) loss.
deserialize(...): Deserializes a serialized loss class/function instance.
dice(...): Computes the Dice loss value between y_true and y_pred.
get(...): Retrieves a Keras loss as a function/Loss class instance.
hinge(...): Computes the hinge loss between y_true & y_pred.
huber(...): Computes Huber loss value.
kld(...): Computes Kullback-Leibler divergence loss between y_true & y_pred.
kullback_leibler_divergence(...): Computes Kullback-Leibler divergence loss between y_true & y_pred.
logcosh(...): Logarithm of the hyperbolic cosine of the prediction error.
mae(...): Computes the mean absolute error between labels and predictions.
mape(...): Computes the mean absolute percentage error between y_true & y_pred.
mse(...): Computes the mean squared error between labels and predictions.
msle(...): Computes the mean squared logarithmic error between y_true & y_pred.
poisson(...): Computes the Poisson loss between y_true and y_pred.
serialize(...): Serializes loss function or Loss instance.
sparse_categorical_crossentropy(...): Computes the sparse categorical crossentropy loss.
squared_hinge(...): Computes the squared hinge loss between y_true & y_pred.
tversky(...): Computes the Tversky loss value between y_true and y_pred.