tensorflow/models · error · ValueError

len(means) must match the number of channels

Error message

len(means) must match the number of channels

What it means

Error "len(means) must match the number of channels" thrown in tensorflow/models.

Source

Thrown at official/legacy/image_classification/resnet/imagenet_preprocessing.py:455

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.
    num_channels: number of color channels in the image that will be distorted.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError('Input must be of size [height, width, C>0]')

  if len(means) != num_channels:
    raise ValueError('len(means) must match the number of channels')

  # We have a 1-D tensor of means; convert to 3-D.
  # Note(b/130245863): we explicitly call `broadcast` instead of simply
  # expanding dimensions for better performance.
  means = tf.broadcast_to(means, tf.shape(image))

  return image - means


def _smallest_size_at_least(height, width, resize_min):
  """Computes new shape with the smallest side equal to `smallest_side`.

  Computes new shape with the smallest side equal to `smallest_side` while
  preserving the original aspect ratio.

  Args:
    height: an int32 scalar tensor indicating the current height.
    width: an int32 scalar tensor indicating the current width.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/legacy/image_classification/resnet/imagenet_preprocessing.py:455 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/23aa522899b0e16f. Report an issue: GitHub.