keras-team/keras · error · ValueError

`HashedCrossing` should be called on at least two inputs. Re

Error message

`HashedCrossing` should be called on at least two inputs. Received: inputs={inputs}

What it means

A crossing by definition needs at least two inputs; call() rejects lists with fewer than two elements.

Source

Thrown at keras/src/layers/preprocessing/hashed_crossing.py:196

            return backend_utils.convert_tf_tensor(outputs, dtype=self.dtype)

    def get_config(self):
        return {
            "num_bins": self.num_bins,
            "output_mode": self.output_mode,
            "sparse": self.sparse,
            "name": self.name,
            "dtype": self.dtype,
        }

    def _check_at_least_two_inputs(self, inputs):
        if not isinstance(inputs, (list, tuple)):
            raise ValueError(
                "`HashedCrossing` should be called on a list or tuple of "
                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}"
            )

View on GitHub (pinned to 7a34a03db6)

Solutions

Provide at least two tensors: layer([x, y])
If you only have one feature, use layers.Hashing instead of HashedCrossing

Example fix

// before
out = layer([x])
// after
out = layer([x, y])

Defensive patterns

Strategy: validation

Validate before calling

assert len(inputs) >= 2, "HashedCrossing needs >= 2 inputs"

Type guard

def has_two_inputs(inputs):
    return isinstance(inputs, (list, tuple)) and len(inputs) >= 2

Try / catch

catch ValueError from call() and either add a second input tensor or replace the layer with Hashing

Prevention

Always supply at least two inputs to HashedCrossing
A single input cannot be crossed; use Hashing or a lookup layer instead

When it happens

Trigger: layer([x]) — a one-element list — reaching _check_at_least_two_inputs during call().

Common situations: Passing a one-element list [x]; building a pipeline before the second feature is connected; copy-paste from a single-input layer.

Related errors

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/09ff83a6abdacd8c. Report an issue: GitHub.