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Computes precision@k of the predictions with respect to sparse labels.
tf.compat.v1.metrics.precision_at_k(
labels, predictions, k, class_id=None, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If 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,
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, 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. |
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.
|
RuntimeError
|
If eager execution is enabled. |