keras-team/keras · error · ValueError

`sparse` may only be true if `output_mode` is `"one_hot"`, `

Error message

`sparse` may only be true if `output_mode` is `"one_hot"`, `"multi_hot"`, or `"count"`. Received: sparse={sparse} and output_mode={output_mode}

What it means

Hashing can only return a sparse tensor for modes that produce vector outputs per sample ('one_hot', 'multi_hot', 'count'). With output_mode='int' each input maps to a scalar bucket index, so a sparse representation is meaningless and the layer rejects sparse=True in that combination.

Source

Thrown at keras/src/layers/preprocessing/hashing.py:184

        if output_mode == "int" and (
            self.dtype_policy.name not in ("int32", "int64")
        ):
            raise ValueError(
                'When `output_mode="int"`, `dtype` should be an integer '
                f"type, 'int32' or 'in64'. Received: dtype={kwargs['dtype']}"
            )

        # 'output_mode' must be one of (INT, ONE_HOT, MULTI_HOT, COUNT)
        accepted_output_modes = ("int", "one_hot", "multi_hot", "count")
        if output_mode not in accepted_output_modes:
            raise ValueError(
                "Invalid value for argument `output_mode`. "
                f"Expected one of {accepted_output_modes}. "
                f"Received: output_mode={output_mode}"
            )

        if sparse and output_mode == "int":
            raise ValueError(
                "`sparse` may only be true if `output_mode` is "
                '`"one_hot"`, `"multi_hot"`, or `"count"`. '
                f"Received: sparse={sparse} and "
                f"output_mode={output_mode}"
            )

        self.num_bins = num_bins
        self.mask_value = mask_value
        self.strong_hash = True if salt is not None else False
        self.output_mode = output_mode
        self.sparse = sparse
        self.salt = None
        if salt is not None:
            if isinstance(salt, (tuple, list)) and len(salt) == 2:
                self.salt = list(salt)
            elif isinstance(salt, int):
                self.salt = [salt, salt]
            else:

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Drop sparse=True (or set sparse=False) when output_mode='int'.
2. If you wanted sparse output, switch output_mode to 'one_hot', 'multi_hot', or 'count'.

Example fix

# before
layer = keras.layers.Hashing(num_bins=64, output_mode="int", sparse=True)
# after
layer = keras.layers.Hashing(num_bins=64, output_mode="int")

sparse_ok = sparse and output_mode != "int"
layer = Hashing(num_bins=n, output_mode=output_mode, sparse=sparse_ok)

When it happens

Trigger: keras.layers.Hashing(num_bins=N, output_mode='int', sparse=True).

Common situations: Copy-pasting sparse=True from a one_hot/multi_hot pipeline into a Hashing layer configured for integer output; enabling sparse globally for memory savings without checking mode compatibility.

Related errors

• Invalid value for argument `output_mode`. Expected one of {a
• The `salt` argument for `Hashing` can only be a tuple of siz
• `salt` can only be used when `oov_method='farmhash'`. Receiv
• `sparse` may only be true if `output_mode` is `'one_hot'`, `
• `sparse=True` can only be used with the TensorFlow backend.

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/2152a41fb04d179c. Report an issue: GitHub.