Module: tf.keras.metrics Built-in metrics.  View aliases  Main aliases 
tf.metrics  Classes class AUC: Computes the approximate AUC (Area under the curve) via a Riemann sum. 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 CategoricalAccuracy: Calculates how often predictions matches 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 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 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 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 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 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_accuracy(...): Calculates how often predictions matches binary labels. binary_crossentropy(...): Computes the binary crossentropy loss. categorical_accuracy(...): Calculates how often predictions matches 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 and y_pred. kl_divergence(...): Computes Kullback-Leibler divergence 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. 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 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 metric function or Metric instance. sparse_categorical_accuracy(...): Calculates how often predictions matches 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 and y_pred. top_k_categorical_accuracy(...): Computes how often targets are in the top K predictions.