tf.contrib.metrics.streaming_sparse_precision_at_top_k(top_k_predictions, labels, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)

tf.contrib.metrics.streaming_sparse_precision_at_top_k(top_k_predictions, labels, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)

See the guide: Metrics (contrib) > Metric Ops

Computes precision@k of top-k predictions with respect to sparse labels.

If class_id is not specified, we calculate precision as the ratio of true positives (i.e., correct predictions, items in top_k_predictions that are found in the corresponding row in labels) to positives (all top_k_predictions). If class_id is specified, we calculate precision by considering only the rows in the batch for which class_id is in the top k highest predictions, and computing the fraction of them for which class_id is in the corresponding row in labels.

We expect precision to decrease as k increases.

streaming_sparse_precision_at_top_k creates two local variables, true_positive_at_k and false_positive_at_k, that are used to compute the precision@k frequency. This frequency is ultimately returned as precision_at_k: an idempotent operation that simply divides true_positive_at_k by total (true_positive_at_k + false_positive_at_k).

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the precision_at_k. Internally, set operations applied to top_k_predictions and labels calculate the true positives and false positives weighted by weights. Then update_op increments true_positive_at_k and false_positive_at_k using these values.

If weights is None, weights default to 1. Use weights of 0 to mask values.

Args:

  • top_k_predictions: Integer Tensor with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and top_k_predictions has shape [batch size, k]. The final dimension contains the indices of top-k labels. [D1, ... DN] must match labels.
  • labels: int64 Tensor or SparseTensor with shape [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and labels has shape [batch_size, num_labels]. [D1, ... DN] must match top_k_predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions. Values outside this range are ignored.
  • class_id: Integer class ID for which we want binary metrics. This should be in range [0, num_classes), where num_classes is the last dimension of predictions. If class_id is outside this range, the method returns NAN.
  • weights: Tensor whose rank is either 0, or n-1, where n is the rank of labels. If the latter, it must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding labels dimension).
  • metrics_collections: An optional list of collections that values should be added to.
  • updates_collections: An optional list of collections that updates should be added to.
  • name: Name of new update operation, and namespace for other dependent ops.

Returns:

  • precision: Scalar float64 Tensor with the value of true_positives divided by the sum of true_positives and false_positives.
  • update_op: Operation that increments true_positives and false_positives variables appropriately, and whose value matches precision.

Raises:

  • ValueError: If weights is not None and its shape doesn't match predictions, or if either metrics_collections or updates_collections are not a list or tuple.
  • ValueError: If top_k_predictions has rank < 2.

Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py.