keras-team/keras · error · TypeError

Expected an integer value for `n`, got {type(n)}.

Error message

Expected an integer value for `n`, got {type(n)}.

What it means

RepeatVector.__init__ requires n to be a genuine Python int. The explicit bool exclusion exists because isinstance(True, int) is True in Python, so True/False would otherwise silently become n=1/n=0. Floats, strings and None all fail this TypeError at construction.

Source

Thrown at keras/src/layers/reshaping/repeat_vector.py:31

    >>> x = keras.Input(shape=(32,))
    >>> y = keras.layers.RepeatVector(3)(x)
    >>> y.shape
    (None, 3, 32)

    Args:
        n: Integer, repetition factor.

    Input shape:
        2D tensor with shape `(batch_size, features)`.

    Output shape:
        3D tensor with shape `(batch_size, n, features)`.
    """

    def __init__(self, n, **kwargs):
        super().__init__(**kwargs)
        if not isinstance(n, int) or isinstance(n, bool):
            raise TypeError(
                f"Expected an integer value for `n`, got {type(n)}."
            )
        if n <= 0:
            raise ValueError(
                f"Argument `n` should be a positive integer. Received: n={n}"
            )
        self.n = n
        self.input_spec = InputSpec(ndim=2)

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.n, input_shape[1])

    def call(self, inputs):
        input_shape = ops.shape(inputs)
        reshaped = ops.reshape(inputs, (input_shape[0], 1, input_shape[1]))
        return ops.repeat(reshaped, self.n, axis=1)

    def get_config(self):

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Cast explicitly: RepeatVector(n=int(n)) after confirming the value is integral
2. Fix the config source so n is stored and loaded as an integer (int() after JSON/YAML load, or a typed CLI flag)
3. If the value arrives as a numpy integer, wrap with int()

Example fix

# before
n = float(cfg['repeat'])  # e.g. 4.0
layer = RepeatVector(n=n)  # TypeError

# after
n = int(cfg['repeat'])
layer = RepeatVector(n=n)

def coerce_n(n):
    if isinstance(n, bool) or not isinstance(n, int):
        raise TypeError(f'n must be int, got {type(n)}')
    return n

def is_strict_int(v) -> bool:
    return isinstance(v, int) and not isinstance(v, bool)

When it happens

Trigger: RepeatVector(n=3.0), RepeatVector(n='4'), RepeatVector(n=None), or RepeatVector(n=True). Any of these raises before the layer is usable.

Common situations: Reading n from JSON/YAML config where it deserializes as float or string; hyperparameter sweeps (optuna, ray) returning floats; forgetting to cast a CLI arg (always a string) to int.

Related errors

• Argument `n` should be a positive integer. Received: n={n}
• Expected an integer value for `size`, got {type(size)}.
• Unknown activation function '{activation}' cannot be seriali
• Could not interpret activation function identifier: {identif
• ConvNeXt does not support the `channels_first` image data fo

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/0c2f196daef9658a. Report an issue: GitHub.