TensorFlow 1 version
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Public API for tf.keras.metrics namespace.
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 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 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 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.