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's __init__ when beta is not a Python float. Keras enforces the type strictly, so ints like 1 or 2 raise this even though they look numeric.

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)}'"

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Write beta with a decimal point: beta=1.0, beta=2.0.
2. Coerce config values: float(cfg['beta']).
3. 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')

beta = float(beta)

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

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.

Related errors

• Invalid `threshold` argument value. It should be a Python fl
• Invalid `average` argument value. Expected one of: {None, 'm
• Invalid `beta` argument value. It should be > 0. Received: b
• Invalid `threshold` argument value. It should verify 0 < thr
• FBetaScore expects 2D inputs with shape (batch_size, output_

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