Module: tf.keras.losses

Built-in loss functions.

View aliases

Main aliases

tf.losses

Classes

class BinaryCrossentropy: Computes the cross-entropy loss between true labels and predicted labels.

class CategoricalCrossentropy: Computes the crossentropy loss between the labels and predictions.

class CategoricalHinge: Computes the categorical hinge loss between y_true and y_pred.

class CosineSimilarity: Computes the cosine similarity between labels and predictions.

class Hinge: Computes the hinge loss between y_true and y_pred.

class Huber: Computes the Huber loss between y_true and y_pred.

class KLDivergence: Computes Kullback-Leibler divergence loss between y_true and 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 and 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 and y_pred.

class Poisson: Computes the Poisson loss between y_true and y_pred.

class Reduction: Types of loss reduction.

class SparseCategoricalCrossentropy: Computes the crossentropy loss between the labels and predictions.

class SquaredHinge: Computes the squared hinge loss between y_true and y_pred.

Functions

KLD(...): Computes Kullback-Leibler divergence loss between y_true and y_pred.

MAE(...): Computes the mean absolute error between labels and predictions.

MAPE(...): Computes the mean absolute percentage error between y_true and y_pred.

MSE(...): Computes the mean squared error between labels and predictions.

MSLE(...): Computes the mean squared logarithmic error between y_true and y_pred.

binary_crossentropy(...): Computes the binary crossentropy loss.

categorical_crossentropy(...): Computes the categorical crossentropy loss.

categorical_hinge(...): Computes the categorical hinge loss between y_true and y_pred.

cosine_similarity(...): Computes the cosine similarity between labels and predictions.

deserialize(...): Deserializes a serialized loss class/function instance.

get(...): Retrieves a Keras loss as a function/Loss class instance.

hinge(...): Computes the hinge loss between y_true and y_pred.

huber(...): Computes Huber loss value.

kl_divergence(...): Computes Kullback-Leibler divergence loss between y_true and y_pred.

kld(...): Computes Kullback-Leibler divergence loss between y_true and y_pred.

kullback_leibler_divergence(...): Computes Kullback-Leibler divergence loss between y_true and y_pred.

log_cosh(...): Logarithm of the hyperbolic cosine of the prediction error.

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 and y_pred.

mean_absolute_error(...): Computes the mean absolute error between labels and predictions.

mean_absolute_percentage_error(...): Computes the mean absolute percentage error between y_true and y_pred.

mean_squared_error(...): Computes the mean squared error between labels and predictions.

mean_squared_logarithmic_error(...): Computes the mean squared logarithmic error between y_true and y_pred.

mse(...): Computes the mean squared error between labels and predictions.

msle(...): Computes the mean squared logarithmic error between y_true and 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 and y_pred.