Module: tf.keras.losses

Built-in loss functions.

Classes

class BinaryCrossentropy: Computes the binary cross entropy loss between the labels and predictions.

class CategoricalCrossentropy: Computes categorical cross entropy loss between the y_true and y_pred.

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.

Functions

KLD(...)

MAE(...)

MAPE(...)

MSE(...)

MSLE(...)

binary_crossentropy(...)

categorical_crossentropy(...)

categorical_hinge(...)

cosine(...)

cosine_proximity(...)

deserialize(...)

get(...)

hinge(...)

kld(...)

kullback_leibler_divergence(...)

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(...)

serialize(...)

sparse_categorical_crossentropy(...)

squared_hinge(...)