If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
predictions
The predicted values, a Tensor of arbitrary dimensions. Will
be cast to bool.
labels
The ground truth values, a Tensor whose dimensions must match
predictions. 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.
Returns
value_tensor
A Tensor representing the current value of the metric.
update_op
An operation that accumulates the error from a batch of data.
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.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.metrics.streaming_false_positives\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/metrics/python/ops/metric_ops.py#L138-L180) |\n\nSum the weights of false positives. (deprecated) \n\n tf.contrib.metrics.streaming_false_positives(\n predictions, labels, weights=None, metrics_collections=None,\n updates_collections=None, name=None\n )\n\n| **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.metrics.false_positives. Note that the order of the labels and predictions arguments has been switched.\n\nIf `weights` is `None`, weights default to 1. Use weights of 0 to mask values.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `predictions` | The predicted values, a `Tensor` of arbitrary dimensions. Will be cast to `bool`. |\n| `labels` | The ground truth values, a `Tensor` whose dimensions must match `predictions`. Will be cast to `bool`. |\n| `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). |\n| `metrics_collections` | An optional list of collections that the metric value variable should be added to. |\n| `updates_collections` | An optional list of collections that the metric update ops should be added to. |\n| `name` | An optional variable_scope name. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|----------------|---------------------------------------------------------------|\n| `value_tensor` | A `Tensor` representing the current value of the metric. |\n| `update_op` | An operation that accumulates the error from a batch of data. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n\n\u003cbr /\u003e"]]