### `tf.contrib.metrics.streaming_precision_at_thresholds(predictions, labels, thresholds, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_precision_at_thresholds}
Computes precision values for different `thresholds` on `predictions`.
The `streaming_precision_at_thresholds` function creates four local variables,
`true_positives`, `true_negatives`, `false_positives` and `false_negatives`
for various values of thresholds. `precision[i]` is defined as the total
weight of values in `predictions` above `thresholds[i]` whose corresponding
entry in `labels` is `True`, divided by the total weight of values in
`predictions` above `thresholds[i]` (`true_positives[i] / (true_positives[i] +
false_positives[i])`).
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`precision`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
##### Args:
* `predictions`: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
* `labels`: A `bool` `Tensor` whose shape matches `predictions`.
* `thresholds`: A python list or tuple of float thresholds in `[0, 1]`.
* `weights`: An optional `Tensor` whose shape is broadcastable to `predictions`.
* `metrics_collections`: An optional list of collections that `auc` 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:
* `precision`: A float `Tensor` of shape `[len(thresholds)]`.
* `update_op`: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables that
are used in the computation of `precision`.
##### 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.