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.