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### `tf.contrib.metrics.streaming_mean_absolute_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_absolute_error}
Computes the mean absolute error between the labels and predictions.
The `streaming_mean_absolute_error` function creates two local variables,
`total` and `count` that are used to compute the mean absolute error. This
average is ultimately returned as `mean_absolute_error`: an idempotent
operation that simply divides `total` by `count`. To facilitate the estimation
of the mean absolute error over a stream of data, the function utilizes two
operations. First, an `absolute_errors` operation computes the absolute value
of the differences between `predictions` and `labels`. Second, an `update_op`
operation whose behavior is dependent on the value of `weights`. If `weights`
is None, then `update_op` increments `total` with the reduced sum of
`absolute_errors` and increments `count` with the number of elements in
`absolute_errors`. If `weights` is not `None`, then `update_op` increments
`total` with the reduced sum of the product of `weights` and `absolute_errors`
and increments `count` with the reduced sum of `weights`. In addition to
performing the updates, `update_op` also returns the `mean_absolute_error`
value.
##### Args:
* <b>`predictions`</b>: A `Tensor` of arbitrary shape.
* <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
* <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
`weights` is not None, the function computes a weighted mean.
* <b>`metrics_collections`</b>: An optional list of collections that
`mean_absolute_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_op_scope name.
##### Returns:
* <b>`mean_absolute_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_absolute_error`.
##### Raises:
* <b>`ValueError`</b>: 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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