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Computes precision@k of the predictions with respect to sparse labels.
tf.metrics.precision_at_k( labels, predictions, k, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None )
class_id is specified, we calculate precision by considering only the
entries in the batch for which
class_id is in the top-k highest
predictions, and computing the fraction of them for which
indeed a correct label.
class_id is not specified, we'll calculate precision as how often on
average a class among the top-k classes with the highest predicted values
of a batch entry is correct and can be found in the label for that entry.
precision_at_k creates two local variables,
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 (
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, a
top_k operation computes a
indicating the top
predictions. Set operations applied to
labels calculate the true positives and false positives weighted by
false_positive_at_<k> using these values.
None, weights default to 1. Use weights of 0 to mask values.
SparseTensorwith shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and
labelshas shape [batch_size, num_labels]. [D1, ... DN] must match
predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of
predictions. Values outside this range are ignored.
Tensorwith shape [D1, ... DN, num_classes] where N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes]. The final dimension contains the logit values for each class. [D1, ... DN] must match
k: Integer, k for @k metric.
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
class_idis outside this range, the method returns NAN.
Tensorwhose 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
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.
Tensorwith the value of
true_positivesdivided by the sum of
false_positivesvariables appropriately, and whose value matches
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
RuntimeError: If eager execution is enabled.