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.