tensorflow/models · error · ValueError
positive_fraction should be in range [0,1]. Received: %s.
Error message
positive_fraction should be in range [0,1]. Received: %s.
What it means
Error "positive_fraction should be in range [0,1]. Received: %s." thrown in tensorflow/models.
Source
Thrown at official/vision/utils/object_detection/balanced_positive_negative_sampler.py:55
from official.vision.utils.object_detection import ops
class BalancedPositiveNegativeSampler(minibatch_sampler.MinibatchSampler):
"""Subsamples minibatches to a desired balance of positives and negatives."""
def __init__(self, positive_fraction=0.5, is_static=False):
"""Constructs a minibatch sampler.
Args:
positive_fraction: desired fraction of positive examples (scalar in [0,1])
in the batch.
is_static: If True, uses an implementation with static shape guarantees.
Raises:
ValueError: if positive_fraction < 0, or positive_fraction > 1
"""
if positive_fraction < 0 or positive_fraction > 1:
raise ValueError('positive_fraction should be in range [0,1]. '
'Received: %s.' % positive_fraction)
self._positive_fraction = positive_fraction
self._is_static = is_static
def _get_num_pos_neg_samples(self, sorted_indices_tensor, sample_size):
"""Counts the number of positives and negatives numbers to be sampled.
Args:
sorted_indices_tensor: A sorted int32 tensor of shape [N] which contains
the signed indices of the examples where the sign is based on the label
value. The examples that cannot be sampled are set to 0. It samples
at most sample_size*positive_fraction positive examples and remaining
from negative examples.
sample_size: Size of subsamples.
Returns:
A tuple containing the number of positive and negative labels in the
subsample.View on GitHub (pinned to e006f5f0d5)
Solutions
- Set positive_fraction to a value within [0, 1].
- Fix the sampler config; typical values are around 0.5.
When it happens
Trigger: Thrown at official/vision/utils/object_detection/balanced_positive_negative_sampler.py:55 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/d226c902f2a053c0.
Report an issue: GitHub.