DO NOT EDIT. This file was autogenerated. Do not edit it by hand, since your modifications would be overwritten. ## Classes [`class AUC`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC): Approximates the AUC (Area under the curve) of the ROC or PR curves. [`class Accuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy): Calculates how often predictions equal labels. [`class BinaryAccuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryAccuracy): Calculates how often predictions match binary labels. [`class BinaryCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryCrossentropy): Computes the crossentropy metric between the labels and predictions. [`class BinaryIoU`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryIoU): Computes the Intersection-Over-Union metric for class 0 and/or 1. [`class CategoricalAccuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy): Calculates how often predictions match one-hot labels. [`class CategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalCrossentropy): Computes the crossentropy metric between the labels and predictions. [`class CategoricalHinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalHinge): Computes the categorical hinge metric between `y_true` and `y_pred`. [`class ConcordanceCorrelation`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/ConcordanceCorrelation): Calculates the Concordance Correlation Coefficient (CCC). [`class CosineSimilarity`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CosineSimilarity): Computes the cosine similarity between the labels and predictions. [`class F1Score`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/F1Score): Computes F-1 Score. [`class FBetaScore`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FBetaScore): Computes F-Beta score. [`class FalseNegatives`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FalseNegatives): Calculates the number of false negatives. [`class FalsePositives`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FalsePositives): Calculates the number of false positives. [`class Hinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Hinge): Computes the hinge metric between `y_true` and `y_pred`. [`class IoU`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/IoU): Computes the Intersection-Over-Union metric for specific target classes. [`class KLDivergence`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/KLDivergence): Computes Kullback-Leibler divergence metric between `y_true` and `y_pred`. [`class LogCoshError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/LogCoshError): Computes the logarithm of the hyperbolic cosine of the prediction error. [`class Mean`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Mean): Compute the (weighted) mean of the given values. [`class MeanAbsoluteError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsoluteError): Computes the mean absolute error between the labels and predictions. [`class MeanAbsolutePercentageError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsolutePercentageError): Computes mean absolute percentage error between `y_true` and `y_pred`. [`class MeanIoU`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanIoU): Computes the mean Intersection-Over-Union metric. [`class MeanMetricWrapper`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanMetricWrapper): Wrap a stateless metric function with the `Mean` metric. [`class MeanSquaredError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanSquaredError): Computes the mean squared error between `y_true` and `y_pred`. [`class MeanSquaredLogarithmicError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanSquaredLogarithmicError): Computes mean squared logarithmic error between `y_true` and `y_pred`. [`class Metric`](https://www.tensorflow.org/api_docs/python/tf/keras/Metric): Encapsulates metric logic and state. [`class OneHotIoU`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/OneHotIoU): Computes the Intersection-Over-Union metric for one-hot encoded labels. [`class OneHotMeanIoU`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/OneHotMeanIoU): Computes mean Intersection-Over-Union metric for one-hot encoded labels. [`class PearsonCorrelation`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/PearsonCorrelation): Calculates the Pearson Correlation Coefficient (PCC). [`class Poisson`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Poisson): Computes the Poisson metric between `y_true` and `y_pred`. [`class Precision`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Precision): Computes the precision of the predictions with respect to the labels. [`class PrecisionAtRecall`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/PrecisionAtRecall): Computes best precision where recall is \>= specified value. [`class R2Score`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/R2Score): Computes R2 score. [`class Recall`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Recall): Computes the recall of the predictions with respect to the labels. [`class RecallAtPrecision`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RecallAtPrecision): Computes best recall where precision is \>= specified value. [`class RootMeanSquaredError`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError): Computes root mean squared error metric between `y_true` and `y_pred`. [`class SensitivityAtSpecificity`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity): Computes best sensitivity where specificity is \>= specified value. [`class SparseCategoricalAccuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy): Calculates how often predictions match integer labels. [`class SparseCategoricalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalCrossentropy): Computes the crossentropy metric between the labels and predictions. [`class SparseTopKCategoricalAccuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseTopKCategoricalAccuracy): Computes how often integer targets are in the top `K` predictions. [`class SpecificityAtSensitivity`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SpecificityAtSensitivity): Computes best specificity where sensitivity is \>= specified value. [`class SquaredHinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SquaredHinge): Computes the hinge metric between `y_true` and `y_pred`. [`class Sum`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Sum): Compute the (weighted) sum of the given values. [`class TopKCategoricalAccuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TopKCategoricalAccuracy): Computes how often targets are in the top `K` predictions. [`class TrueNegatives`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TrueNegatives): Calculates the number of true negatives. [`class TruePositives`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TruePositives): Calculates the number of true positives. ## Functions [`KLD(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`MAE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAE): Computes the mean absolute error between labels and predictions. [`MAPE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE): Computes the mean absolute percentage error between `y_true` \& `y_pred`. [`MSE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSE): Computes the mean squared error between labels and predictions. [`MSLE(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSLE): Computes the mean squared logarithmic error between `y_true` \& `y_pred`. [`binary_accuracy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_accuracy) [`binary_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_crossentropy): Computes the binary crossentropy loss. [`binary_focal_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_focal_crossentropy): Computes the binary focal crossentropy loss. [`categorical_accuracy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_accuracy) [`categorical_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_crossentropy): Computes the categorical crossentropy loss. [`categorical_focal_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_focal_crossentropy): Computes the categorical focal crossentropy loss. [`categorical_hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/categorical_hinge): Computes the categorical hinge loss between `y_true` \& `y_pred`. [`concordance_correlation(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/concordance_correlation): Computes the Concordance coefficient between labels and predictions. [`deserialize(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/deserialize): Deserializes a serialized metric class/function instance. [`get(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/get): Retrieves a Keras metric as a `function`/`Metric` class instance. [`hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/hinge): Computes the hinge loss between `y_true` \& `y_pred`. [`huber(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/huber): Computes Huber loss value. [`kld(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`kullback_leibler_divergence(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLD): Computes Kullback-Leibler divergence loss between `y_true` \& `y_pred`. [`logcosh(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/logcosh): Logarithm of the hyperbolic cosine of the prediction error. [`mae(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAE): Computes the mean absolute error between labels and predictions. [`mape(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE): Computes the mean absolute percentage error between `y_true` \& `y_pred`. [`mse(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSE): Computes the mean squared error between labels and predictions. [`msle(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MSLE): Computes the mean squared logarithmic error between `y_true` \& `y_pred`. [`pearson_correlation(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/pearson_correlation): Computes the Pearson coefficient between labels and predictions. [`poisson(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/poisson): Computes the Poisson loss between y_true and y_pred. [`serialize(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/serialize): Serializes metric function or `Metric` instance. [`sparse_categorical_accuracy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_accuracy) [`sparse_categorical_crossentropy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/sparse_categorical_crossentropy): Computes the sparse categorical crossentropy loss. [`sparse_top_k_categorical_accuracy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_top_k_categorical_accuracy): Computes how often integer targets are in the top `K` predictions. [`squared_hinge(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/squared_hinge): Computes the squared hinge loss between `y_true` \& `y_pred`. [`top_k_categorical_accuracy(...)`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/top_k_categorical_accuracy)