tf.metrics.false_positives( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/python/ops/metrics_impl.py
.
Sum the weights of false positives.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
labels
: The ground truth values, a Tensor
whose dimensions must match predictions
. Will be cast to bool
.predictions
: The predicted values, a Tensor
of arbitrary dimensions. Will be cast to bool
.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 the metric value variable should be added to.updates_collections
: An optional list of collections that the metric update ops should be added to.name
: An optional variable_scope name.value_tensor
: A Tensor
representing the current value of the metric.update_op
: An operation that accumulates the error from a batch of data.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.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/metrics/false_positives