Computes false negatives at provided threshold values.
tf.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.
Args |
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.
|
Returns |
false_negatives
|
A float Tensor of shape [len(thresholds)] .
|
update_op
|
An operation that updates the false_negatives variable and
returns its current value.
|
Raises |
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.
|