# Module: tf.contrib.metrics

### Module tf.contrib.metrics

Ops for evaluation metrics and summary statistics.

See the Metrics (contrib) guide.

## Members

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.

confusion_matrix(...): Deprecated. Use tf.confusion_matrix instead.

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.

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

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

streaming_concat(...): Concatenate values along an axis across batches.

streaming_covariance(...): Computes the unbiased sample covariance between predictions and labels.

streaming_false_negatives(...): Computes the total number of false positives.

streaming_false_negatives_at_thresholds(...)

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

streaming_false_positives_at_thresholds(...)

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

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

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.

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

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.

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

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

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.

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

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

streaming_sparse_average_precision_at_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.

streaming_true_negatives_at_thresholds(...)

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

streaming_true_positives_at_thresholds(...)