tf.metrics.true_positives_at_thresholds(
labels,
predictions,
thresholds,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes true positives at provided threshold values.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args:
labels
: ATensor
whose shape matchespredictions
. Will be cast tobool
.predictions
: A floating pointTensor
of arbitrary shape and whose values are in the range[0, 1]
.thresholds
: A python list or tuple of float thresholds in[0, 1]
.weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglabels
dimension).metrics_collections
: An optional list of collections thattrue_positives
should be added to.updates_collections
: An optional list of collections thatupdate_op
should be added to.name
: An optional variable_scope name.
Returns:
true_positives
: A floatTensor
of shape[len(thresholds)]
.update_op
: An operation that updates thetrue_positives
variable and returns its current value.
Raises:
ValueError
: Ifpredictions
andlabels
have mismatched shapes, or ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.