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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 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`.