Module: tf.keras.losses | TensorFlow v2.16.1 (original) (raw)
Module: tf.keras.losses
Stay organized with collections Save and categorize content based on your preferences.
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.