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Computes false negatives at provided threshold values.
tf.compat.v1.metrics.false_negatives_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
labels
: A Tensor
whose shape matches predictions
. Will be cast to
bool
.predictions
: A floating point Tensor
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
: Optional Tensor
whose rank is either 0, or the same rank as
labels
, and 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 false_negatives
should be added to.updates_collections
: An optional list of collections that update_op
should
be added to.name
: An optional variable_scope name.false_negatives
: A float Tensor
of shape [len(thresholds)]
.update_op
: An operation that updates the false_negatives
variable and
returns its current value.ValueError
: If predictions
and labels
have mismatched shapes, or 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.