tf.data.experimental.choose_from_datasets

Creates a dataset that deterministically chooses elements from datasets. (deprecated)

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

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.Dataset.choose_from_datasets(...) instead. Note that, unlike the experimental endpoint, the non-experimental endpoint sets stop_on_empty_dataset=True by default. You should set this argument explicitly in case you would like to match the behavior of the experimental endpoint.

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.