keras-team/keras · error · ValueError

Invalid `beta` argument value. It should be a Python float…

Error message

Invalid `beta` argument value. It should be a Python float. Received: beta={beta} of type '{type(beta)}'

What it means

Raised by FBetaScore when the `beta` argument is not a Python float. beta weights recall vs precision (beta > 1 favors recall); it must be a plain Python float, not an int, string, tensor, or numpy scalar of non-float type.

Solutions

Write beta with a decimal point: beta=1.0, beta=2.0.
Coerce config values: float(cfg['beta']).
Note keras.metrics.F1Score is a shortcut for beta=1.0.

Example fix

# before
m = keras.metrics.FBetaScore(beta=1, average='macro')

# after
m = keras.metrics.FBetaScore(beta=1.0, average='macro')
# or simply
m = keras.metrics.F1Score(average='macro')

Defensive patterns

Strategy: type-guard

Validate before calling

beta = float(beta)

Type guard

def is_float_beta(v) -> bool:
    return isinstance(v, float)

Prevention

Always write beta with a decimal point (1.0, 2.0).
Coerce numeric config values with float() at load.

When it happens

Trigger: keras.metrics.FBetaScore(beta=1) (int, not 1.0); beta parsed from JSON/YAML as int; numpy floats or tf.Variable.

Common situations: Writing beta=1 or beta=2 as integers (the classic F1 case); config files storing 2 instead of 2.0.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/8843612fb1874ba9. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/metrics/f_score_metrics.py:86

        average=None,
        beta=1.0,
        threshold=None,
        name="fbeta_score",
        dtype=None,
    ):
        super().__init__(name=name, dtype=dtype)
        # Metric should be maximized during optimization.
        self._direction = "up"

        if average not in (None, "micro", "macro", "weighted"):
            raise ValueError(
                "Invalid `average` argument value. Expected one of: "
                "{None, 'micro', 'macro', 'weighted'}. "
                f"Received: average={average}"
            )

        if not isinstance(beta, float):
            raise ValueError(
                "Invalid `beta` argument value. "
                "It should be a Python float. "
                f"Received: beta={beta} of type '{type(beta)}'"
            )
        if beta <= 0.0:
            raise ValueError(
                "Invalid `beta` argument value. "
                "It should be > 0. "
                f"Received: beta={beta}"
            )

        if threshold is not None:
            if not isinstance(threshold, float):
                raise ValueError(
                    "Invalid `threshold` argument value. "
                    "It should be a Python float. "
                    f"Received: threshold={threshold} "
                    f"of type '{type(threshold)}'"

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