TensorFlow 1 version
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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(...): 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 function.
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(...): 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.