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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 `y_true` and `y_pred`.

`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(...)`

`MAPE(...)`

`MSE(...)`

`MSLE(...)`

`binary_crossentropy(...)`

`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(...)`

`get(...)`

`hinge(...)`: Computes the hinge 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`.

`logcosh(...)`: Logarithm of the hyperbolic cosine of the prediction error.

`mae(...)`

`mape(...)`

`mean_absolute_error(...)`

`mean_absolute_percentage_error(...)`

`mean_squared_error(...)`

`mean_squared_logarithmic_error(...)`

`mse(...)`

`msle(...)`

`poisson(...)`: Computes the Poisson loss between y_true and y_pred.

`serialize(...)`

`sparse_categorical_crossentropy(...)`

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

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