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### `tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_relative_error}
Computes the mean relative error by normalizing with the given values.
The `streaming_mean_relative_error` function creates two local variables,
`total` and `count` that are used to compute the mean relative absolute error.
This average is weighted by `weights`, and it is ultimately returned as
`mean_relative_error`: an idempotent operation that simply divides `total` by
`count`.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`mean_reative_error`. Internally, a `relative_errors` operation divides the
absolute value of the differences between `predictions` and `labels` by the
`normalizer`. Then `update_op` increments `total` with the reduced sum of the
product of `weights` and `relative_errors`, and it increments `count` with the
reduced sum of `weights`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
##### Args:
* <b>`predictions`</b>: A `Tensor` of arbitrary shape.
* <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
* <b>`normalizer`</b>: A `Tensor` of the same shape as `predictions`.
* <b>`weights`</b>: Optional `Tensor` indicating the frequency with which an example is
sampled. Rank must be 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).
* <b>`metrics_collections`</b>: An optional list of collections that
`mean_relative_error` should be added to.
* <b>`updates_collections`</b>: An optional list of collections that `update_op` should
be added to.
* <b>`name`</b>: An optional variable_scope name.
##### Returns:
* <b>`mean_relative_error`</b>: A `Tensor` representing the current mean, the value of
`total` divided by `count`.
* <b>`update_op`</b>: An operation that increments the `total` and `count` variables
appropriately and whose value matches `mean_relative_error`.
##### Raises:
* <b>`ValueError`</b>: 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.
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