tf.data.experimental.choose_from_datasets
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Creates a dataset that deterministically chooses elements from datasets
. (deprecated)
tf . data . experimental . choose_from_datasets (
datasets , choice_dataset , stop_on_empty_dataset = False
)
Warning: 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.
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Last updated 2022-11-04 UTC.
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{"lastModified": "Last updated 2022-11-04 UTC."}
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