Computes average [email protected] of predictions with respect to sparse labels.
Compat aliases for migration
See Migration guide for more details.
tf.metrics.average_precision_at_k( labels, predictions, k, weights=None, metrics_collections=None, updates_collections=None, name=None )
average_precision_at_k creates two local variables,
average_precision_at_<k>/max, that are used to compute the frequency. This frequency is ultimately returned as
average_precision_at_<k>: an idempotent operation that simply divides
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. This will calculate an average precision for range |
| || |
| ||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 k is invalid.|
| ||If eager execution is enabled.|
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.