parameters: (values, threshold, ignore_mask=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None)
return_statement: return streaming_mean(is_below_threshold, _mask_to_weights(ignore_mask),
                          metrics_collections, updates_collections,
                          name or 'percentage_below_threshold')
docstring: Computes the percentage of values less than the given threshold.
  
    The `streaming_percentage_less` function creates two local variables,
    `total` and `count` that are used to compute the percentage of `values` that
    fall below `threshold`. This rate is ultimately returned as `percentage`
    which is an idempotent operation that simply divides `total` by `count.
    To facilitate the estimation of the percentage of values that fall under
    `threshold` over multiple batches of data, the function creates an
    `update_op` operation whose behavior is dependent on the value of
    `ignore_mask`. If `ignore_mask` is None, then `update_op`
    increments `total` with the number of elements of `values` that are less
    than `threshold` and `count` with the number of elements in `values`. If
    `ignore_mask` is not `None`, then `update_op` increments `total` with the
    number of elements of `values` that are less than `threshold` and whose
    corresponding entries in `ignore_mask` are False, and `count` is incremented
    with the number of elements of `ignore_mask` that are False.
  
    Args:
      values: A numeric `Tensor` of arbitrary size.
      threshold: A scalar threshold.
      ignore_mask: An optional mask of the same shape as 'values' which indicates
        which elements to ignore during metric computation.
      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_op_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 `ignore_mask` is not None and its shape doesn't match `values
        or if either `metrics_collections` or `updates_collections` are supplied
        but are not a list or tuple.
docstring_summary: Computes the percentage of values less than the given threshold.
docstring_tokens: Computes the percentage of values less than the given threshold .
function: def streaming_percentage_less(values, threshold, ignore_mask=None,
                                metrics_collections=None,
                                updates_collections=None,
                                name=None):
    """Computes the percentage of values less than the given threshold.
  
    The `streaming_percentage_less` function creates two local variables,
    `total` and `count` that are used to compute the percentage of `values` that
    fall below `threshold`. This rate is ultimately returned as `percentage`
    which is an idempotent operation that simply divides `total` by `count.
    To facilitate the estimation of the percentage of values that fall under
    `threshold` over multiple batches of data, the function creates an
    `update_op` operation whose behavior is dependent on the value of
    `ignore_mask`. If `ignore_mask` is None, then `update_op`
    increments `total` with the number of elements of `values` that are less
    than `threshold` and `count` with the number of elements in `values`. If
    `ignore_mask` is not `None`, then `update_op` increments `total` with the
    number of elements of `values` that are less than `threshold` and whose
    corresponding entries in `ignore_mask` are False, and `count` is incremented
    with the number of elements of `ignore_mask` that are False.
  
    Args:
      values: A numeric `Tensor` of arbitrary size.
      threshold: A scalar threshold.
      ignore_mask: An optional mask of the same shape as 'values' which indicates
        which elements to ignore during metric computation.
      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_op_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 `ignore_mask` is not None and its shape doesn't match `values
        or if either `metrics_collections` or `updates_collections` are supplied
        but are not a list or tuple.
    """
    is_below_threshold = math_ops.to_float(math_ops.less(values, threshold))
    return streaming_mean(is_below_threshold, _mask_to_weights(ignore_mask),
                          metrics_collections, updates_collections,
                          name or 'percentage_below_threshold')
function_tokens: def streaming_percentage_less ( values , threshold , ignore_mask = None , metrics_collections = None , updates_collections = None , name = None ) : is_below_threshold = math_ops . to_float ( math_ops . less ( values , threshold ) ) return streaming_mean ( is_below_threshold , _mask_to_weights ( ignore_mask ) , metrics_collections , updates_collections , name or 'percentage_below_threshold' )