Evaluation-related metrics.

`accuracy(...)`

: Calculates how often `predictions`

matches `labels`

.

`auc(...)`

: Computes the approximate AUC via a Riemann sum.

`average_precision_at_k(...)`

: Computes average [email protected] 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 [email protected] of the predictions with respect to sparse labels.

`precision_at_thresholds(...)`

: Computes precision values for different `thresholds`

on `predictions`

.

`precision_at_top_k(...)`

: Computes [email protected] of the predictions with respect to sparse labels.

`recall(...)`

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

`recall_at_k(...)`

: Computes [email protected] 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 [email protected] 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.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v1/metrics