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Ops for evaluation metrics and summary statistics.
See the Contrib Metrics guide.
Functions
accuracy(...): Computes the percentage of times that predictions matches labels.
aggregate_metric_map(...): Aggregates the metric names to tuple dictionary.
aggregate_metrics(...): Aggregates the metric value tensors and update ops into two lists.
auc_using_histogram(...): AUC computed by maintaining histograms.
auc_with_confidence_intervals(...): Computes the AUC and asymptotic normally distributed confidence interval.
cohen_kappa(...): Calculates Cohen's kappa.
confusion_matrix(...): Deprecated. Use tf.math.confusion_matrix instead.
count(...): Computes the number of examples, or sum of weights.
f1_score(...): Computes the approximately best F1-score across different thresholds.
precision_at_recall(...): Computes the precision at a given recall.
precision_recall_at_equal_thresholds(...): A helper method for creating metrics related to precision-recall curves.
recall_at_precision(...): Computes recall at precision.
set_difference(...): Compute set difference of elements in last dimension of a and b.
set_intersection(...): Compute set intersection of elements in last dimension of a and b.
set_size(...): Compute number of unique elements along last dimension of a.
set_union(...): Compute set union of elements in last dimension of a and b.
sparse_recall_at_top_k(...): Computes recall@k of top-k predictions with respect to sparse labels.
streaming_accuracy(...): Calculates how often predictions matches labels. (deprecated)
streaming_auc(...): Computes the approximate AUC via a Riemann sum. (deprecated)
streaming_concat(...): Concatenate values along an axis across batches.
streaming_covariance(...): Computes the unbiased sample covariance between predictions and labels.
streaming_curve_points(...): Computes curve (ROC or PR) values for a prespecified number of points.
streaming_dynamic_auc(...): Computes the apporixmate AUC by a Riemann sum with data-derived thresholds.
streaming_false_negative_rate(...): Computes the false negative rate of predictions with respect to labels.
streaming_false_negative_rate_at_thresholds(...): Computes various fnr values for different thresholds on predictions.
streaming_false_negatives(...): Computes the total number of false negatives. (deprecated)
streaming_false_negatives_at_thresholds(...)
streaming_false_positive_rate(...): Computes the false positive rate of predictions with respect to labels.
streaming_false_positive_rate_at_thresholds(...): Computes various fpr values for different thresholds on predictions.
streaming_false_positives(...): Sum the weights of false positives. (deprecated)
streaming_false_positives_at_thresholds(...)
streaming_mean(...): Computes the (weighted) mean of the given values. (deprecated)
streaming_mean_absolute_error(...): Computes the mean absolute error between the labels and predictions. (deprecated)
streaming_mean_cosine_distance(...): Computes the cosine distance between the labels and predictions.
streaming_mean_iou(...): Calculate per-step mean Intersection-Over-Union (mIOU).
streaming_mean_relative_error(...): Computes the mean relative error by normalizing with the given values.
streaming_mean_squared_error(...): Computes the mean squared error between the labels and predictions. (deprecated)
streaming_mean_tensor(...): Computes the element-wise (weighted) mean of the given tensors. (deprecated)
streaming_pearson_correlation(...): Computes Pearson correlation coefficient between predictions, labels.
streaming_percentage_less(...): Computes the percentage of values less than the given threshold.
streaming_precision(...): Computes the precision of the predictions with respect to the labels. (deprecated)
streaming_precision_at_thresholds(...): Computes precision values for different thresholds on predictions. (deprecated)
streaming_recall(...): Computes the recall of the predictions with respect to the labels. (deprecated)
streaming_recall_at_k(...): Computes the recall@k of the predictions with respect to dense labels. (deprecated)
streaming_recall_at_thresholds(...): Computes various recall values for different thresholds on predictions. (deprecated)
streaming_root_mean_squared_error(...): Computes the root mean squared error between the labels and predictions. (deprecated)
streaming_sensitivity_at_specificity(...): Computes the sensitivity at a given specificity.
streaming_sparse_average_precision_at_k(...): Computes average precision@k of predictions with respect to sparse labels.
streaming_sparse_average_precision_at_top_k(...): Computes average precision@k of predictions with respect to sparse labels.
streaming_sparse_precision_at_k(...): Computes precision@k of the predictions with respect to sparse labels.
streaming_sparse_precision_at_top_k(...): Computes precision@k of top-k predictions with respect to sparse labels.
streaming_sparse_recall_at_k(...): Computes recall@k of the predictions with respect to sparse labels.
streaming_specificity_at_sensitivity(...): Computes the specificity at a given sensitivity.
streaming_true_negatives(...): Sum the weights of true_negatives. (deprecated)
streaming_true_negatives_at_thresholds(...)
streaming_true_positives(...): Sum the weights of true_positives. (deprecated)