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More comprehensive example of TF v1 of Dataset.make_initializable_iterator().

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PiperOrigin-RevId: 305021425
Change-Id: Idf4d0af800957e91ce3a5bb983ec9e74db65075e
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A. Unique TensorFlower 2020-04-06 07:10:20 -07:00 committed by TensorFlower Gardener
parent cf88f3c5d2
commit 8c9bfce2ee
1 changed files with 53 additions and 11 deletions
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64
tensorflow/python/data/ops/dataset_ops.py
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@ -2103,15 +2103,40 @@ class DatasetV1(DatasetV2):
raise NotImplementedError("Dataset._as_variant_tensor")
@deprecation.deprecated(
None, "Use `for ... in dataset:` to iterate over a dataset. If using "
"`tf.estimator`, return the `Dataset` object directly from your input "
"function. As a last resort, you can use "
"`tf.compat.v1.data.make_one_shot_iterator(dataset)`.")
None, "This is a deprecated API that should only be used in TF 1 graph "
"mode and legacy TF 2 graph mode available through `tf.compat.v1`. In "
"all other situations -- namely, eager mode and inside `tf.function` -- "
"you can consume dataset elements using `for elem in dataset: ...` or "
"by explicitly creating iterator via `iterator = iter(dataset)` and "
"fetching its elements via `values = next(iterator)`. Furthermore, "
"this API is not available in TF 2. During the transition from TF 1 "
"to TF 2 you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)` "
"to create a TF 1 graph mode style iterator for a dataset created "
"through TF 2 APIs. Note that this should be a transient state of your "
"code base as there are in general no guarantees about the "
"interoperability of TF 1 and TF 2 code.")
def make_one_shot_iterator(self):
"""Creates an `Iterator` for enumerating the elements of this dataset.
Note: The returned iterator will be initialized automatically.
A "one-shot" iterator does not currently support re-initialization.
A "one-shot" iterator does not currently support re-initialization. For
that see `make_initializable_iterator`.
Example:
```python
# Building graph ...
dataset = ...
next_value = dataset.make_one_shot_iterator().get_next()
# ... from within a session ...
try:
while True:
value = sess.run(next_value)
...
except tf.errors.OutOfRangeError:
pass
```
Returns:
An `Iterator` over the elements of this dataset.
@ -2170,10 +2195,19 @@ class DatasetV1(DatasetV2):
get_legacy_output_classes(self))
@deprecation.deprecated(
None, "Use `for ... in dataset:` to iterate over a dataset. If using "
"`tf.estimator`, return the `Dataset` object directly from your input "
"function. As a last resort, you can use "
"`tf.compat.v1.data.make_initializable_iterator(dataset)`.")
None, "This is a deprecated API that should only be used in TF 1 graph "
"mode and legacy TF 2 graph mode available through `tf.compat.v1`. "
"In all other situations -- namely, eager mode and inside `tf.function` "
"-- you can consume dataset elements using `for elem in dataset: ...` "
"or by explicitly creating iterator via `iterator = iter(dataset)` "
"and fetching its elements via `values = next(iterator)`. "
"Furthermore, this API is not available in TF 2. During the transition "
"from TF 1 to TF 2 you can use "
"`tf.compat.v1.data.make_initializable_iterator(dataset)` to create a TF "
"1 graph mode style iterator for a dataset created through TF 2 APIs. "
"Note that this should be a transient state of your code base as there "
"are in general no guarantees about the interoperability of TF 1 and TF "
"2 code.")
def make_initializable_iterator(self, shared_name=None):
"""Creates an `Iterator` for enumerating the elements of this dataset.
@ -2181,10 +2215,19 @@ class DatasetV1(DatasetV2):
and you must run the `iterator.initializer` operation before using it:
```python
# Building graph ...
dataset = ...
iterator = dataset.make_initializable_iterator()
# ...
next_value = iterator.get_next() # This is a Tensor.
# ... from within a session ...
sess.run(iterator.initializer)
try:
while True:
value = sess.run(next_value)
...
except tf.errors.OutOfRangeError:
pass
```
Args:
@ -2198,7 +2241,6 @@ class DatasetV1(DatasetV2):
Raises:
RuntimeError: If eager execution is enabled.
"""
return self._make_initializable_iterator(shared_name)
def _make_initializable_iterator(self, shared_name=None): # pylint: disable=missing-docstring