tf.metrics.false_positives_at_thresholds

tf.metrics.false_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 false positives at provided threshold values.

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.