keras-team/keras · error · ValueError

All `HashedCrossing` inputs should have equal shape…

Error message

All `HashedCrossing` inputs should have equal shape. Received: inputs={inputs}

What it means

HashedCrossing implements crossing via tf.sparse.cross, which requires all input features to share shape (), (batch_size,) or (batch_size, 1). This check in _check_input_shape_and_type rejects any input whose rank or last dimension does not match that pattern.

Solutions

Align shapes before the layer, usually tf.reshape(x, [-1, 1]) for every input
Verify upstream batching/slicing yields the same batch size for each crossed feature
In Functional models, use identically-shaped Input layers

Example fix

// before
out = layer([a, b])  # a: (32,1), b: (33,1)
// after
b = b[: tf.shape(a)[0]]
out = layer([a, b])

Defensive patterns

Strategy: validation

Validate before calling

s0 = tuple(inputs[0].shape)
assert all(tuple(x.shape) == s0 for x in inputs[1:])

Type guard

def equal_shapes(inputs):
    s = tuple(inputs[0].shape)
    return all(tuple(x.shape) == s for x in inputs[1:])

Try / catch

catch ValueError from call(), align shapes with tf.reshape(x, [-1, 1]), then retry

Prevention

Ensure all crossed features have equal shapes (and batch sizes) before the layer
Verify upstream slicing does not drop or add a batch dimension for one feature

When it happens

Trigger: tuple(x.shape) differing across inputs, e.g. (32, 1) vs (33, 1) or () vs (32,), reaching _check_input_shape_and_type.

Common situations: One feature sliced with [:-1] by accident; ragged-to-dense conversions producing different lengths; mixing inputs from different batch sources.

Related errors

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/05d317f1e3a82802. Report an issue: GitHub.

Appendix: source

Thrown at keras/src/layers/preprocessing/hashed_crossing.py:211

                f"inputs. Received: inputs={inputs}"
            )
        if len(inputs) < 2:
            raise ValueError(
                "`HashedCrossing` should be called on at least two inputs. "
                f"Received: inputs={inputs}"
            )

    def _check_input_shape_and_type(self, inputs):
        first_shape = tuple(inputs[0].shape)
        rank = len(first_shape)
        if rank > 2 or (rank == 2 and first_shape[-1] != 1):
            raise ValueError(
                "All `HashedCrossing` inputs should have shape `()`, "
                "`(batch_size)` or `(batch_size, 1)`. "
                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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