Module: tf.keras.losses

Built-in loss functions.

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.

Func