All Keras metrics.
Classes
class AUC
: Approximates the AUC (Area under the curve) of the ROC or PR curves.
class Accuracy
: Calculates how often predictions equal labels.
class BinaryAccuracy
: Calculates how often predictions match binary labels.
class BinaryCrossentropy
: Computes the crossentropy metric between the labels and predictions.
class BinaryIoU
: Computes the Intersection-Over-Union metric for class 0 and/or 1.
class CategoricalAccuracy
: Calculates how often predictions match one-hot labels.
class CategoricalCrossentropy
: Computes the crossentropy metric between the labels and predictions.
class CategoricalHinge
: Computes the categorical hinge metric between y_true
and y_pred
.
class CosineSimilarity
: Computes the cosine similarity between the labels and predictions.
class FalseNegatives
: Calculates the number of false negatives.
class FalsePositives
: Calculates the number of false positives.
class Hinge
: Computes the hinge metric between y_true
and y_pred
.
class IoU
: Computes the Intersection-Over-Union metric for specific target classes.
class KLDivergence
: Computes Kullback-Leibler divergence metric between y_true
and y_pred
.
class LogCoshError
: Computes the logarithm of the hyperbolic cosine of the prediction error.
class Mean
: Computes the (weighted) mean of the given values.
class MeanAbsoluteError
: Computes the mean absolute error between the labels and predictions.
class MeanAbsolutePercentageError
: Computes the mean absolute percentage error between y_true
and y_pred
.
class MeanIoU
: Computes the mean Intersection-Over-Union metric.
class MeanMetricWrapper
: Wraps a stateless metric function with the Mean metric.
class MeanRelativeError
: Computes the mean relative error by normalizing with the given values.
class MeanSquaredError
: Computes the mean squared error between y_true
and y_pred
.
class MeanSquaredLogarithmicError
: Computes the mean squared logarithmic error between y_true
and y_pred
.
class MeanTensor
: Computes the element-wise (weighted) mean of the given tensors.
class Metric
: Encapsulates metric logic and state.
class OneHotIoU
: Computes the Intersection-Over-Union metric for one-hot encoded labels.
class OneHotMeanIoU
: Computes mean Intersection-Over-Union metric for one-hot encoded labels.
class Poisson
: Computes the Poisson metric between y_true
and y_pred
.
class Precision
: Computes the precision of the predictions with respect to the labels.
class PrecisionAtRecall
: Computes best precision where recall is >= specified value.
class Recall
: Computes the recall of the predictions with respect to the labels.
class RecallAtPrecision
: Computes best recall where precision is >= specified value.
class RootMeanSquaredError
: Computes root mean squared error metric between y_true
and y_pred
.
class SensitivityAtSpecificity
: Computes best sensitivity where specificity is >= specified value.
class SparseCategoricalAccuracy
: Calculates how often predictions match integer labels.
class SparseCategoricalCrossentropy
: Computes the crossentropy metric between the labels and predictions.
class SparseTopKCategoricalAccuracy
: Computes how often integer targets are in the top K
predictions.
class SpecificityAtSensitivity
: Computes best specificity where sensitivity is >= specified value.
class SquaredHinge
: Computes the squared hinge metric between y_true
and y_pred
.
class Sum
: Computes the (weighted) sum of the given values.
class TopKCategoricalAccuracy
: Computes how often targets are in the top K
predictions.
class TrueNegatives
: Calculates the number of true negatives.
class TruePositives
: Calculates the number of true positives.
Functions
KLD(...)
: Computes Kullback-Leibler divergence loss between y_true
& y_pred
.
MAE(...)
: Computes the mean absolute error between labels and predictions.
MAPE(...)
: Computes the mean absolute percentage error between y_true
& y_pred
.
MSE(...)
: Computes the mean squared error between labels and predictions.
MSLE(...)
: Computes the mean squared logarithmic error between y_true
& y_pred
.
binary_accuracy(...)
: Calculates how often predictions match binary labels.
binary_crossentropy(...)
: Computes the binary crossentropy loss.
binary_focal_crossentropy(...)
: Computes the binary focal crossentropy loss.
categorical_accuracy(...)
: Calculates how often predictions match one-hot labels.
categorical_crossentropy(...)
: Computes the categorical crossentropy loss.
deserialize(...)
: Deserializes a serialized metric class/function instance.
get(...)
: Retrieves a Keras metric as a function
/Metric
class instance.
hinge(...)
: Computes the hinge loss between y_true
& y_pred
.
kl_divergence(...)
: Computes Kullback-Leibler divergence loss between y_true
& y_pred
.
kld(...)
: Computes Kullback-Leibler divergence loss between y_true
& y_pred
.
kullback_leibler_divergence(...)
: Computes Kullback-Leibler divergence loss between y_true
& 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
& 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
& 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
& y_pred
.
mse(...)
: Computes the mean squared error between labels and predictions.
msle(...)
: Computes the mean squared logarithmic error between y_true
& y_pred
.
poisson(...)
: Computes the Poisson loss between y_true and y_pred.
serialize(...)
: Serializes metric function or Metric
instance.
sparse_categorical_accuracy(...)
: Calculates how often predictions match integer labels.
sparse_categorical_crossentropy(...)
: Computes the sparse categorical crossentropy loss.
sparse_top_k_categorical_accuracy(...)
: Computes how often integer targets are in the top K
predictions.
squared_hinge(...)
: Computes the squared hinge loss between y_true
& y_pred
.
top_k_categorical_accuracy(...)
: Computes how often targets are in the top K
predictions.