tensorflow/models · error · ValueError

top_n must be a positive integer or None.

Error message

top_n must be a positive integer or None.

What it means

Error "top_n must be a positive integer or None." thrown in tensorflow/models.

Source

Thrown at official/projects/yt8m/eval_utils/average_precision_calculator.py:77

class AveragePrecisionCalculator(object):
  """Calculate the average precision and average precision at n."""

  def __init__(self, top_n=None):
    """Construct an AveragePrecisionCalculator to calculate average precision.

    This class is used to calculate the average precision for a single label.

    Args:
      top_n: A positive Integer specifying the average precision at n, or None
        to use all provided data points.

    Raises:
      ValueError: An error occurred when the top_n is not a positive integer.
    """
    if not ((isinstance(top_n, int) and top_n >= 0) or top_n is None):
      raise ValueError("top_n must be a positive integer or None.")

    self._top_n = top_n  # average precision at n
    self._total_positives = 0  # total number of positives have seen
    self._heap = []  # max heap of (prediction, actual)

  @property
  def heap_size(self):
    """Gets the heap size maintained in the class."""
    return len(self._heap)

  @property
  def num_accumulated_positives(self):
    """Gets the number of positive samples that have been accumulated."""
    return self._total_positives

  def accumulate(self, predictions, actuals, num_positives=None):
    """Accumulate the predictions and their ground truth labels.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/yt8m/eval_utils/average_precision_calculator.py:77 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/b7618c73ffe7507f. Report an issue: GitHub.