tf.contrib.metrics.streaming_pearson_correlation(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes pearson correlation coefficient between predictions, labels. The streaming_pearson_correlation function delegates to streaming_covariance the tracking of three [co]variances: - streaming_covariance(predictions, labels), i.e. covariance - streaming_covariance(predictions, predictions), i.e. variance - streaming_covariance(labels, labels), i.

tf.contrib.metrics.streaming_precision(*args, **kwargs) Computes the precision of the predictions with respect to the labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). The streaming_precision function creates two local variables, true_positives and false_positives, that are used to c

tf.contrib.metrics.streaming_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean squared error between the labels and predictions. The streaming_mean_squared_error function creates two local variables, total and count that are used to compute the mean squared error. This average is weighted by weights, and it is ultimately returned as mean_squared_error: an idempotent operation that simply divides total by count

tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean relative error by normalizing with the given values. The streaming_mean_relative_error function creates two local variables, total and count that are used to compute the mean relative absolute error. This average is weighted by weights, and it is ultimately returned as mean_relative_error: an idempotent operation that s

tf.contrib.metrics.streaming_mean_cosine_distance(predictions, labels, dim, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the cosine distance between the labels and predictions. The streaming_mean_cosine_distance function creates two local variables, total and count that are used to compute the average cosine distance between predictions and labels. This average is weighted by weights, and it is ultimately returned as mean_distance, which is an idempoten

tf.contrib.metrics.streaming_mean_iou(*args, **kwargs) Calculate per-step mean Intersection-Over-Union (mIOU). (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each s

tf.contrib.metrics.streaming_mean_absolute_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean absolute error between the labels and predictions. The streaming_mean_absolute_error function creates two local variables, total and count that are used to compute the mean absolute error. This average is weighted by weights, and it is ultimately returned as mean_absolute_error: an idempotent operation that simply divides total by

tf.contrib.metrics.streaming_mean(values, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the (weighted) mean of the given values. The streaming_mean function creates two local variables, total and count that are used to compute the average of values. This average is ultimately returned as mean which is an idempotent operation that simply divides total by count. For estimation of the metric over a stream of data, the function creates an update_op operation

tf.contrib.metrics.streaming_covariance(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the unbiased sample covariance between predictions and labels. The streaming_covariance function creates four local variables, comoment, mean_prediction, mean_label, and count, which are used to compute the sample covariance between predictions and labels across multiple batches of data. The covariance is ultimately returned as an idempotent operati

tf.contrib.metrics.streaming_auc(predictions, labels, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, curve='ROC', name=None) Computes the approximate AUC via a Riemann sum. The streaming_auc function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The a