tf.contrib.metrics

 

upper level

 

Modules

 

None

 

Classes

 

None

 

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.

cohen_kappa(…):

Calculates Cohen’s kappa.

confusion_matrix(…):

Deprecated. Use tf.confusion_matrix instead.

count(…):

Computes the number of examples, or sum of weights.

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.

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.

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. (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.

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. (deprecated)

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. (deprecated)

streaming_root_mean_squared_error(…):

Computes the root mean squared error between the labels and predictions.

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.

streaming_true_negatives_at_thresholds(…):

streaming_true_positives(…):

Sum the weights of true_positives.

streaming_true_positives_at_thresholds(…):