Computes [email protected] of the predictions with respect to sparse labels.
Compat aliases for migration
See Migration guide for more details.
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
class_id is indeed a correct label. If
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 [email protected] 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
Tensor 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.
| || |
| || Float |
| ||Integer, k for @k metric.|
| || 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 |
| || |
| ||An optional list of collections that values should be added to.|
| ||An optional list of collections that updates should be added to.|
| ||Name of new update operation, and namespace for other dependent ops.|
| || Scalar |
| || |
| || If |
| ||If eager execution is enabled.|
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.