keras-team/keras · error · ValueError

The lists `inputs` and `mask` should have the same length. R

Error message

The lists `inputs` and `mask` should have the same length. Received: inputs={inputs} of length {len(inputs)}, and mask={mask} of length {len(mask)}

What it means

Merge layers combine masks element-wise, so compute_mask requires len(mask) == len(inputs). This error fires when the number of masks does not match the number of input tensors.

Source

Thrown at keras/src/layers/merging/base_merge.py:265

    def compute_output_spec(self, inputs):
        output_shape = self.compute_output_shape([x.shape for x in inputs])
        output_sparse = all(x.sparse for x in inputs)
        return KerasTensor(
            output_shape, dtype=self.compute_dtype, sparse=output_sparse
        )

    def compute_mask(self, inputs, mask=None):
        if mask is None:
            return None
        if not isinstance(mask, (tuple, list)):
            raise ValueError(f"`mask` should be a list. Received: mask={mask}")
        if not isinstance(inputs, (tuple, list)):
            raise ValueError(
                f"`inputs` should be a list. Received: inputs={inputs}"
            )
        if len(mask) != len(inputs):
            raise ValueError(
                "The lists `inputs` and `mask` should have the same length. "
                f"Received: inputs={inputs} of length {len(inputs)}, and "
                f"mask={mask} of length {len(mask)}"
            )
        # Default implementation does an OR between the masks, which works
        # for `Add`, `Subtract`, `Average`, `Maximum`, `Minimum`, `Multiply`.
        if any(m is None for m in mask):
            return None
        output_mask = mask[0]
        for m in mask[1:]:
            output_mask = ops.logical_or(output_mask, m)
        return output_mask

    def get_config(self):
        return super().get_config()

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Supply exactly one mask entry per input, using None for unmasked inputs: mask=[m1, None]
2. Recompute mask lists whenever input arity changes
3. In Functional models, rely on automatic mask propagation instead of hand-built mask lists

Example fix

# before
out = merge([x1, x2], mask=[m1])

# after
out = merge([x1, x2], mask=[m1, None])

assert isinstance(mask, (list, tuple)) and len(mask) == len(inputs), 'one mask per input (use None)'

def masks_match_inputs(mask, inputs) -> bool:
    return mask is None or (isinstance(mask, (list, tuple)) and len(mask) == len(inputs))

When it happens

Trigger: Calling compute_mask([x1, x2], mask=[m1]) or __call__ paths where one input's mask is missing from the list (you must pass None placeholders).

Common situations: Mixing masked and unmasked inputs (forgetting a None entry); changing the number of inputs without updating the mask list.

Related errors

• `mask` should be a list. Received: mask={mask}
• `inputs` should be a list. Received: inputs={inputs}
• The lists `inputs` and `mask` should have the same length. R
• Inputs have incompatible shapes. Received shapes {shape1} an
• A merge layer should be called on a list of inputs. Received

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/6b9fd8bfdb9f2f3f. Report an issue: GitHub.