See Stable See Nightly
DO NOT EDIT. This file was autogenerated. Do not edit it by hand, since your modifications would be overwritten. ## Classes [`class BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy): Computes the cross-entropy loss between true labels and predicted labels. [`class BinaryFocalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryFocalCrossentropy): Computes focal cross-entropy loss between true labels and predictions. [`class CTC`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CTC): CTC (Connectionist Temporal Classification) loss. [`class CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy): Computes the crossentropy loss between the labels and predictions. [`class CategoricalFocalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalFocalCrossentropy): Computes the alpha balanced focal crossentropy loss. [`class CategoricalGeneralizedCrossEntropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalGeneralizedCrossEntropy): Computes the Generalized Cross Entropy loss between `y_true` \& `y_pred`. [`class CategoricalHinge`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalHinge): Computes the categorical hinge loss between `y_true` \& `y_pred`. [`class Circle`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Circle): Computes Circle Loss between integer labels and L2-normalized embeddings. [`class CosineSimilarity`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CosineSimilarity): Computes the cosine similarity between `y_true` \& `y_pred`. [`class Dice`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Dice): Computes the Dice loss value between `y_true` and `y_pred`. [`class Hinge`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Hinge): Computes the hinge loss between `y_true` \& `y_pred`. [`class Huber`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Huber): Computes the Huber loss between `y_true` \& `y_pred`. [`class KLDivergence`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLDivergence): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`class LogCosh`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/LogCosh): Computes the logarithm of the hyperbolic cosine of the prediction error. [`class Loss`](https://www.tensorflow.org/api_docs/python/tf/keras/Loss): Loss base class. [`class MeanAbsoluteError`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanAbsoluteError): Computes the mean of absolute difference between labels and predictions. [`class MeanAbsolutePercentageError`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanAbsolutePercentageError): Computes the mean absolute percentage error between `y_true` \& `y_pred`. [`class MeanSquaredError`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanSquaredError): Computes the mean of squares of errors between labels and predictions. [`class MeanSquaredLogarithmicError`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanSquaredLogarithmicError): Computes the mean squared logarithmic error between `y_true` \& `y_pred`. [`class Poisson`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Poisson): Computes the Poisson loss between `y_true` \& `y_pred`. [`class Reduction`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Reduction) [`class SparseCategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy): Computes the crossentropy loss between the labels and predictions. [`class SquaredHinge`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SquaredHinge): Computes the squared hinge loss between `y_true` \& `y_pred`. [`class Tversky`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Tversky): Computes the Tversky loss value between `y_true` and `y_pred`. ## Functions [`KLD(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`MAE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAE): Computes the mean absolute error between labels and predictions. [`MAPE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE): Computes the mean absolute percentage error between `y_true` \& `y_pred`. [`MSE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSE): Computes the mean squared error between labels and predictions. [`MSLE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSLE): Computes the mean squared logarithmic error between `y_true` \& `y_pred`. [`binary_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_crossentropy): Computes the binary crossentropy loss. [`binary_focal_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_focal_crossentropy): Computes the binary focal crossentropy loss. [`categorical_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_crossentropy): Computes the categorical crossentropy loss. [`categorical_focal_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_focal_crossentropy): Computes the categorical focal crossentropy loss. [`categorical_generalized_cross_entropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_generalized_cross_entropy): Computes the Generalized Cross Entropy loss. [`categorical_hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_hinge): Computes the categorical hinge loss between `y_true` \& `y_pred`. [`circle(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/circle): Computes the Circle loss. [`cosine_similarity(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/cosine_similarity): Computes the cosine similarity between labels and predictions. [`ctc(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/ctc): CTC (Connectionist Temporal Classification) loss. [`deserialize(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/deserialize): Deserializes a serialized loss class/function instance. [`dice(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/dice): Computes the Dice loss value between `y_true` and `y_pred`. [`get(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/get): Retrieves a Keras loss as a `function`/`Loss` class instance. [`hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/hinge): Computes the hinge loss between `y_true` \& `y_pred`. [`huber(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/huber): Computes Huber loss value. [`kld(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`kullback_leibler_divergence(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`logcosh(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/logcosh): Logarithm of the hyperbolic cosine of the prediction error. [`mae(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAE): Computes the mean absolute error between labels and predictions. [`mape(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE): Computes the mean absolute percentage error between `y_true` \& `y_pred`. [`mse(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSE): Computes the mean squared error between labels and predictions. [`msle(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSLE): Computes the mean squared logarithmic error between `y_true` \& `y_pred`. [`poisson(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/poisson): Computes the Poisson loss between y_true and y_pred. [`serialize(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/serialize): Serializes loss function or `Loss` instance. [`sparse_categorical_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/sparse_categorical_crossentropy): Computes the sparse categorical crossentropy loss. [`squared_hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/squared_hinge): Computes the squared hinge loss between `y_true` \& `y_pred`. [`tversky(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/tversky): Computes the Tversky loss value between `y_true` and `y_pred`.