# Module: tf.metrics

Defined in `tensorflow/metrics/__init__.py`.

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.