keras-team/keras · error · ValueError

The `{name}` argument should be a number (or a list of two n

Error message

The `{name}` argument should be a number (or a list of two numbers) in the range [{self._FACTOR_BOUNDS[0]}, {self._FACTOR_BOUNDS[1]}]. Received: factor={factor}

What it means

RandomGaussianBlur._set_factor_by_name validates numeric factors (e.g. the blur sigma/factor) in __init__. A sequence form must be exactly two numbers denoting lower and upper bounds; sequences of any other length raise this ValueError immediately.

Source

Thrown at keras/src/layers/preprocessing/image_preprocessing/random_gaussian_blur.py:100

            if factor % 2 == 0:
                raise ValueError(error_msg)
            lower, upper = factor, factor
        else:
            raise ValueError(error_msg)

        return lower, upper

    def _set_factor_by_name(self, factor, name):
        error_msg = (
            f"The `{name}` argument should be a number "
            "(or a list of two numbers) "
            "in the range "
            f"[{self._FACTOR_BOUNDS[0]}, {self._FACTOR_BOUNDS[1]}]. "
            f"Received: factor={factor}"
        )
        if isinstance(factor, (tuple, list)):
            if len(factor) != 2:
                raise ValueError(error_msg)
            if (
                factor[0] > self._FACTOR_BOUNDS[1]
                or factor[1] < self._FACTOR_BOUNDS[0]
            ):
                raise ValueError(error_msg)
            lower, upper = sorted(factor)
        elif isinstance(factor, (int, float)):
            if (
                factor < self._FACTOR_BOUNDS[0]
                or factor > self._FACTOR_BOUNDS[1]
            ):
                raise ValueError(error_msg)
            factor = abs(factor)
            lower, upper = [max(-factor, self._FACTOR_BOUNDS[0]), factor]
        else:
            raise ValueError(error_msg)
        return lower, upper

View on GitHub (pinned to 7a34a03db6)

Solutions

Pass a 2-element list [lower, upper], e.g. factor=[0.0, 2.0]
Or a single number such as factor=1.0
Flatten nested config lists before construction

Example fix

# before
layers.RandomGaussianBlur(factor=[0.5])
# after
layers.RandomGaussianBlur(factor=[0.0, 0.5])

Defensive patterns

Strategy: validation

Validate before calling

f = [0.0, 2.0]
assert isinstance(f, (tuple, list)) and len(f) == 2, "factor must be [lower, upper]"

Type guard

def is_factor_pair(v) -> bool:
    return isinstance(v, (tuple, list)) and len(v) == 2

Try / catch

try:
    layer = RandomGaussianBlur(factor=f)
except ValueError:
    layer = RandomGaussianBlur(factor=1.0)

Prevention

Use [lower, upper] pairs or scalars in configs
Flatten nested YAML structures before layer construction

When it happens

Trigger: factor=[0.1, 0.2, 0.3] or factor=[0.1] passed to layers.RandomGaussianBlur(); an empty list from a config.

Common situations: Config generators emitting variable-length lists; nested YAML structures that wrap the pair once more; copy-paste from layers taking per-axis triples.

Related errors

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/9c23f92f08df0f08. Report an issue: GitHub.