tf.data.experimental.choose_from_datasets

Creates a dataset that deterministically chooses elements from datasets.

tf.data.experimental.choose_from_datasets(
    datasets, choice_dataset, stop_on_empty_dataset=False
)

For example, given the following datasets:

datasets = [tf.data.Dataset.from_tensors("foo").repeat(),
            tf.data.Dataset.from_tensors("bar").repeat(),
            tf.data.Dataset.from_tensors("baz").repeat()]

# Define a dataset containing `[0, 1, 2, 0, 1, 2, 0, 1, 2]`.
choice_dataset = tf.data.Dataset.range(3).repeat(3)

result = tf.data.experimental.choose_from_datasets(datasets, choice_dataset)

The elements of result will be:

"foo", "bar", "baz", "foo", "bar", "baz", "foo", "bar", "baz"

Args
`datasets`	A non-empty list of `tf.data.Dataset` objects with compatible structure.
`choice_dataset`	A `tf.data.Dataset` of scalar `tf.int64` tensors between `0` and `len(datasets) - 1`.
`stop_on_empty_dataset`	If `True`, selection stops if it encounters an empty dataset. If `False`, it skips empty datasets. It is recommended to set it to `True`. Otherwise, the selected elements start off as the user intends, but may change as input datasets become empty. This can be difficult to detect since the dataset starts off looking correct. Default to `False` for backward compatibility.

Returns
A dataset that interleaves elements from `datasets` according to the values of `choice_dataset`.

Raises
`TypeError`	If `datasets` or `choice_dataset` has the wrong type.
`ValueError`	If `datasets` is empty.

tf.data.experimental.choose_from_datasets

Args

Returns

Raises