Module: tf.compat.v2.keras.metrics

Built-in metrics.

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Compat aliases for migration

See Migration guide for more details.

`tf.compat.v2.metrics`

Classes

class AUC: Computes the approximate AUC (Area under the curve) via a Riemann sum.

class Accuracy: Calculates how often predictions matches labels.

class BinaryAccuracy: Calculates how often predictions matches labels.

class BinaryCrossentropy: Computes the crossentropy metric between the labels and predictions.

class CategoricalAccuracy: Calculates how often predictions matches 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 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 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 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 Recall: Computes the recall of the predictions with respect to the labels.

class RootMeanSquaredError: Computes root mean squared error metric between y_true and y_pred.

class SensitivityAtSpecificity: Computes the sensitivity at a given specificity.

class SparseCategoricalAccuracy: Calculates how often predictions matches 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 the specificity at a given sensitivity.

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 and y_pred.

MAE(...)

MAPE(...)

MSE(...)

MSLE(...)

binary_accuracy(...)

binary_crossentropy(...)

categorical_accuracy(...)

categorical_crossentropy(...): Computes the categorical crossentropy loss.

deserialize(...)

get(...)

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.

mae(...)

mape(...)

mean_absolute_error(...)

mean_absolute_percentage_error(...)

mean_squared_error(...)

mean_squared_logarithmic_error(...)

mse(...)

msle(...)

poisson(...): Computes the Poisson loss between y_true and y_pred.

serialize(...)

sparse_categorical_accuracy(...)

sparse_categorical_crossentropy(...)

sparse_top_k_categorical_accuracy(...)

squared_hinge(...): Computes the squared hinge loss between y_true and y_pred.

top_k_categorical_accuracy(...)