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 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
.
categorical_crossentropy(...)
: Computes the categorical crossentropy loss.
categorical_hinge(...)
: Computes the categorical hinge loss between y_true
and y_pred
.
cosine(...)
: Computes the cosine similarity between labels and predictions.
cosine_proximity(...)
: Computes the cosine similarity between labels and predictions.
cosine_similarity(...)
: Computes the cosine similarity between labels and predictions.
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.
mean_absolute_percentage_error(...)
mean_squared_logarithmic_error(...)
poisson(...)
: Computes the Poisson loss between y_true and y_pred.
sparse_categorical_crossentropy(...)
squared_hinge(...)
: Computes the squared hinge loss between y_true
and y_pred
.