tf.metrics.mean_cosine_distance

tf.metrics.mean_cosine_distance(
    labels,
    predictions,
    dim,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Defined in tensorflow/python/ops/metrics_impl.py.

Computes the cosine distance between the labels and predictions.

The 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:

• labels: A Tensor of arbitrary shape.
• predictions: A Tensor of the same shape as labels.
• dim: The dimension along which the cosine distance is computed.
• 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). Also, dimension dim must be 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.
• RuntimeError: If eager execution is enabled.