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Computes average precision@k of predictions with respect to sparse labels.
tf.compat.v1.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>/total and 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
average_precision_at_<k>/total by average_precision_at_<k>/max.
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 k predictions. Set operations applied to top_k 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 | |
|---|---|
labels
|
int64 Tensor or SparseTensor with 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 labels has 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.
|
predictions
|
Float Tensor with 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 labels.
|
k
|
Integer, k for @k metric. This will calculate an average precision for
range [1,k], as documented above.
|
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 | |
|---|---|
mean_average_precision
|
Scalar float64 Tensor with the mean average
precision values.
|
update
|
Operation that increments variables appropriately, and whose
value matches metric.
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Raises | |
|---|---|
ValueError
|
if k is invalid. |
RuntimeError
|
If eager execution is enabled. |