tensorflow/models · error · ValueError

the shape of predictions and actuals does not match.

Error message

the shape of predictions and actuals does not match.

What it means

Shape agreement check in AveragePrecisionCalculator.accumulate: predictions and actuals must be equal-length sequences (they are zipped pairwise to score each item); mismatched lengths mean one list has scores without ground truth or vice versa, making AP meaningless. Commonly fires when results from partial runs are concatenated independently, or when num_positives was supplied with trimmed arrays built separately.

Solutions

1. Ensure predictions and actuals are produced from the same batches and appended together
2. Trim or extend both arrays consistently when subsampling
3. Add a length assert in the caller before accumulating to catch divergence early

When it happens

Trigger: Thrown at official/projects/yt8m/eval_utils/average_precision_calculator.py:114 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/607e6dab7a4fb58c. Report an issue: GitHub.

Appendix: source

Thrown at official/projects/yt8m/eval_utils/average_precision_calculator.py:114

    After the function call, we may call peek_ap_at_n to actually calculate
    the average precision.
    Note predictions and actuals must have the same shape.

    Args:
      predictions: a list storing the prediction scores.
      actuals: a list storing the ground truth labels. Any value larger than 0
        will be treated as positives, otherwise as negatives. num_positives = If
        the 'predictions' and 'actuals' inputs aren't complete, then it's
        possible some true positives were missed in them. In that case, you can
        provide 'num_positives' in order to accurately track recall.
      num_positives: number of positive examples.

    Raises:
      ValueError: An error occurred when the format of the input is not the
      numpy 1-D array or the shape of predictions and actuals does not match.
    """
    if len(predictions) != len(actuals):
      raise ValueError("the shape of predictions and actuals does not match.")

    if num_positives is not None:
      if not isinstance(num_positives, numbers.Number) or num_positives < 0:  # pyrefly: ignore[unsupported-operation]
        raise ValueError(
            "'num_positives' was provided but it was a negative number.")

    if num_positives is not None:
      self._total_positives += num_positives
    else:
      self._total_positives += numpy.size(
          numpy.where(numpy.array(actuals) > 1e-5))
    topk = self._top_n
    heap = self._heap

    for i in range(numpy.size(predictions)):
      if topk is None or len(heap) < topk:
        heapq.heappush(heap, (predictions[i], actuals[i]))
      else:

View on GitHub (pinned to e006f5f0d5)