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Losses resume

In this section you’ll find a list of all losses fonctionalities.

  ICONS RESUME
BinaryCrossentropy Computes the cross-entropy loss between true labels and predicted labels.
CategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.​
CategoricalHinge Computes the categorical hinge loss between y_true and y_pred.​ 
Hinge Computes the hinge loss between y_true and y_pred.​
Huber Computes the Huber loss between y_true and y_pred.​
KLDivergence Computes Kullback-Leibler divergence loss between y_true and y_pred.​
LogCosh Computes the logarithm of the hyperbolic cosine of the prediction error.
MeanAbsoluteError Computes the mean of absolute difference between labels and predictions.​
MeanAbsolutePercentageError Computes the mean absolute percentage error between y_true and y_pred.​
MeanSquaredError Computes the mean of squares of errors between labels and predictions.
MeanSquaredLogarithmicError Computes the mean squared logarithmic error between y_true and y_pred.
Poisson Computes the Poisson loss between y_true and y_pred.​
SquaredHinge Computes the squared hinge loss between y_true and y_pred.​
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