Module: tf.metrics

Evaluation-related metrics.

Functions

accuracy(...): Calculates how often predictions matches labels.

auc(...): Computes the approximate AUC via a Riemann sum.

average_precision_at_k(...): Computes average precision@k of predictions with respect to sparse labels.

false_negatives(...): Computes the total number of false negatives.

false_negatives_at_thresholds(...): Computes false negatives at provided threshold values.

false_positives(...): Sum the weights of false positives.

false_positives_at_thresholds(...): Computes false positives at provided threshold values.

mean(...): Computes the (weighted) mean of the given values.

mean_absolute_error(...): Computes the mean absolute error between the labels and predictions.

mean_cosine_distance(...): Computes the cosine distance between the labels and predictions.

mean_iou(...): Calculate per-step mean Intersection-Over-Union (mIOU).

mean_per_class_accuracy(...): Calculates the mean of the per-class accuracies.

mean_relative_error(...): Computes the mean relative error by normalizing with the given values.

mean_squared_error(...): Computes the mean squared error between the labels and predictions.

mean_tensor(...): Computes the element-wise (weighted) mean of the given tensors.

percentage_below(...): Computes the percentage of values less than the given threshold.

precision(...): Computes the precision of the predictions with respect to the labels.

precision_at_k(...): Computes precision@k of the predictions with respect to sparse labels.

precision_at_thresholds(...): Computes precision values for different thresholds on predictions.

precision_at_top_k(...): Computes precision@k of the predictions with respect to sparse labels.

recall(...): Computes the recall of the predictions with respect to the labels.

recall_at_k(...): Computes recall@k of the predictions with respect to sparse labels.

recall_at_thresholds(...): Computes various recall values for different thresholds on predictions.

recall_at_top_k(...): Computes recall@k of top-k predictions with respect to sparse labels.

root_mean_squared_error(...): Computes the root mean squared error between the labels and predictions.

sensitivity_at_specificity(...): Computes the specificity at a given sensitivity.

sparse_average_precision_at_k(...): Renamed to average_precision_at_k, please use that method instead. (deprecated)

sparse_precision_at_k(...): Renamed to precision_at_k, please use that method instead. (deprecated)

specificity_at_sensitivity(...): Computes the specificity at a given sensitivity.

true_negatives(...): Sum the weights of true_negatives.

true_negatives_at_thresholds(...): Computes true negatives at provided threshold values.

true_positives(...): Sum the weights of true_positives.

true_positives_at_thresholds(...): Computes true positives at provided threshold values.