tf.metrics.false_positives_at_thresholds

Computes false positives at provided threshold values.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.metrics.false_positives_at_thresholds

tf.metrics.false_positives_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_positives 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_positives	A float Tensor of shape [len(thresholds)].
update_op	An operation that updates the false_positives 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.