keras-team/keras · error · ValueError

All `HashedCrossing` inputs should have an integer or…

Error message

All `HashedCrossing` inputs should have an integer or string dtype. Received: inputs={inputs}

What it means

Feature crossing hashes raw values, so inputs must hold integer or string values. This check in HashedCrossing._check_input_shape_and_type fires when any input tensor's dtype is neither integer nor string (e.g. a float tensor).

Solutions

Cast floats to string: tf.strings.as_string(x)
Cast numeric codes to int: tf.cast(x, tf.int64)
Keep float features out of crossings or bucketize them (Discretization) into integer bins first

Example fix

// before
out = layer([price_float, category_int])  # price is float32
// after
price = tf.strings.as_string(price_float)
out = layer([price, tf.cast(category_int, tf.int64)])

Defensive patterns

Strategy: validation

Validate before calling

for i, x in enumerate(inputs):
    if not (tf.as_dtype(x.dtype).is_integer or x.dtype == tf.string):
        inputs[i] = tf.cast(x, tf.int64)  # or tf.strings.as_string(x)

Type guard

def int_or_string_dtype(inputs):
    import tensorflow as tf
    return all(tf.as_dtype(x.dtype).is_integer or x.dtype == tf.string for x in inputs)

Try / catch

catch ValueError from call() and cast offending inputs (tf.cast(x, tf.int64) or tf.strings.as_string(x)) before retrying

Prevention

Cast numeric features to a string or integer dtype before crossing
Keep crossed features as raw strings or integer ids, not floats

When it happens

Trigger: An input whose dtype is neither integer nor tf.string (e.g. float32) reaching the dtype check in _check_input_shape_and_type.

Common situations: Crossing float-encoded numeric features (normalized prices, log transforms); integer ids stored as float64 after pandas/numpy ops; passing embeddings or continuous columns.

Related errors

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/c1c1bf88f513dcd8. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/layers/preprocessing/hashed_crossing.py:226

                f"Received: inputs={inputs}"
            )
        if not all(tuple(x.shape) == first_shape for x in inputs[1:]):
            raise ValueError(
                "All `HashedCrossing` inputs should have equal shape. "
                f"Received: inputs={inputs}"
            )
        if any(
            isinstance(x, (tf.RaggedTensor, tf.SparseTensor)) for x in inputs
        ):
            raise ValueError(
                "All `HashedCrossing` inputs should be dense tensors. "
                f"Received: inputs={inputs}"
            )
        if not all(
            tf.as_dtype(x.dtype).is_integer or x.dtype == tf.string
            for x in inputs
        ):
            raise ValueError(
                "All `HashedCrossing` inputs should have an integer or "
                f"string dtype. Received: inputs={inputs}"
            )

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