tf.metrics.percentage_below

tf.metrics.percentage_below(
    values,
    threshold,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

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

Computes the percentage of values less than the given threshold.

The percentage_below function creates two local variables, total and count that are used to compute the percentage of values that fall below threshold. This rate is weighted by weights, and it is ultimately returned as percentage 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 percentage.

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

Args:

• values: A numeric Tensor of arbitrary size.
• threshold: A scalar threshold.
• weights: Optional Tensor whose rank is either 0, or the same rank as values, and must be broadcastable to values (i.e., all dimensions must be either 1, or the same as the corresponding values 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:

• percentage: 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 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.
• RuntimeError: If eager execution is enabled.