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tf.contrib.metrics.streaming_mean_cosine_distance

```tf.contrib.metrics.streaming_mean_cosine_distance(
predictions,
labels,
dim,
weights=None,
metrics_collections=None,
name=None
)
```

See the guide: Metrics (contrib) > Metric `Ops`

Computes the cosine distance between the labels and predictions.

The `streaming_mean_cosine_distance` function creates two local variables, `total` and `count` that are used to compute the average cosine distance between `predictions` and `labels`. This average is weighted by `weights`, and it is ultimately returned as `mean_distance`, which is 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_distance`.

If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

Args:

• `predictions`: A `Tensor` of the same shape as `labels`.
• `labels`: A `Tensor` of arbitrary shape.
• `dim`: The dimension along which the cosine distance is computed.
• `weights`: An optional `Tensor` whose shape is broadcastable to `predictions`, and whose dimension `dim` is 1.
• `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:

• `mean_distance`: A `Tensor` representing the current mean, the value of `total` divided by `count`.
• `update_op`: An operation that increments the `total` and `count` variables appropriately.

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.