tensorflow/models · error · ValueError

The `slicing_feature` and slicing values in `slicing_spec` m

Error message

The `slicing_feature` and slicing values in `slicing_spec` must have the same type. Got types: {(slicing_feature.dtype, self._slicing_feature_dtype)}.

What it means

Error "The `slicing_feature` and slicing values in `slicing_spec` must have the same type. Got types: {(slicing_feature.dtype, self._slicing_feature_dtype)}." thrown in tensorflow/models.

Source

Thrown at official/recommendation/uplift/metrics/sliced_metric.py:144

      sample_weight: tf.Tensor | None = None,
      slicing_feature: tf.Tensor,
      **kwargs,
  ):
    """Updates the state of the metrics for each slice.

    Args:
      *args: A variable amount of `tf.Tensor` instances that will be passed to
        the `update_state` method of each metric.
      sample_weight: An optional `tf.Tensor` used to weight the sample. Its
        dimensions must be broadcastable to the shape(s) of *args.
      slicing_feature: A `tf.Tensor` consisting of the feature to be sliced on.
        Its dimensions must be broadcastable to the shape(s) of *args.
      **kwargs: Keyword arguments that will be passed to the `update_state`
        method of each metric.
    """

    if slicing_feature.dtype != self._slicing_feature_dtype:
      raise ValueError(
          "The `slicing_feature` and slicing values in `slicing_spec` must "
          "have the same type. Got types: "
          f"{(slicing_feature.dtype, self._slicing_feature_dtype)}."
      )

    if sample_weight is not None:
      for _ in range(len(slicing_feature.shape) - len(sample_weight.shape)):
        sample_weight = tf.expand_dims(sample_weight, axis=-1)

      for _ in range(len(sample_weight.shape) - len(slicing_feature.shape)):
        slicing_feature = tf.expand_dims(slicing_feature, axis=-1)

    self._metric.update_state(*args, sample_weight=sample_weight, **kwargs)
    for slicing_val, metric in zip(
        self._slicing_values_tensors, self._sliced_metrics
    ):
      slice_mask = tf.cast(slicing_feature == slicing_val, dtype=tf.float32)
      if sample_weight is not None:

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/recommendation/uplift/metrics/sliced_metric.py:144 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/dae65bf3534fd69d. Report an issue: GitHub.