# tf.contrib.metrics.count

tf.contrib.metrics.count(
values,
weights=None,
metrics_collections=None,
name=None
)


Computes the number of examples, or sum of weights.

When evaluating some metric (e.g. mean) on one or more subsets of the data, this auxiliary metric is useful for keeping track of how many examples there are in each subset.

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

#### Args:

• values: A Tensor of arbitrary dimensions. Only it's shape is used.
• 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).
• 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:

• count: A Tensor representing the current value of the metric.
• update_op: An operation that accumulates the metric from a batch of data.

#### Raises:

• ValueError: If weights is not None and its shape doesn't match values, or if either metrics_collections or updates_collections are not a list or tuple.