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Apply tf1->tf2 name replaces to doc-strings and comments in tensorflow.

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No code changes, only doc-strings and comments.

PiperOrigin-RevId: 243837271
This commit is contained in:
Mark Daoust 2019-04-16 10:46:39 -07:00 committed by TensorFlower Gardener
parent b5d97a2590
commit ec1effdb69
6 changed files with 23 additions and 23 deletions

14
tensorflow/python/saved_model/builder_impl.py
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@ -64,9 +64,9 @@ class _SavedModelBuilder(object):
Typical usage for the `SavedModelBuilder`:
```python
...
builder = tf.saved_model.Builder(export_dir)
builder = tf.compat.v1.saved_model.Builder(export_dir)
with tf.Session(graph=tf.Graph()) as sess:
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
...
builder.add_meta_graph_and_variables(sess,
["foo-tag"],
@ -74,7 +74,7 @@ class _SavedModelBuilder(object):
assets_list=foo_assets)
...
with tf.Session(graph=tf.Graph()) as sess:
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
...
builder.add_meta_graph(["bar-tag", "baz-tag"])
...
@ -252,8 +252,8 @@ class _SavedModelBuilder(object):
train_op: Op or group of opts that trains the model when run. This will
not be run automatically when the graph is loaded, instead saved in
a SignatureDef accessible through the exported MetaGraph.
saver: An instance of tf.train.Saver that will be used to export the
metagraph. If None, a sharded Saver that restores all variables will
saver: An instance of tf.compat.v1.train.Saver that will be used to export
the metagraph. If None, a sharded Saver that restores all variables will
be used.
Raises:
@ -332,7 +332,7 @@ class _SavedModelBuilder(object):
strip_default_attrs: Boolean. If `True`, default-valued attributes will be
removed from the NodeDefs. For a detailed guide, see
[Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
saver: An instance of tf.train.Saver that will be used to export the
saver: An instance of tf.compat.v1.train.Saver that will be used to export the
metagraph and save variables. If None, a sharded Saver that restores
all variables will be used.
@ -441,7 +441,7 @@ class SavedModelBuilder(_SavedModelBuilder):
Args:
assets_collection_to_add: The collection where the asset paths are setup.
"""
# Add assets to the collection with key `constants.ASSETS_KEY`, in the
# Add assets to the collection with key `saved_model.ASSETS_KEY`, in the
# graph.
asset_filename_map = _maybe_save_assets(_add_asset_to_collection,
assets_collection_to_add)

10
tensorflow/python/saved_model/loader.py
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@ -33,9 +33,9 @@ Typical usage:
```python
...
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
with tf.Session(graph=tf.Graph()) as sess:
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
...
builder.add_meta_graph_and_variables(sess,
["foo-tag"],
@ -43,7 +43,7 @@ with tf.Session(graph=tf.Graph()) as sess:
assets_collection=foo_assets)
...
with tf.Session(graph=tf.Graph()) as sess:
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
...
builder.add_meta_graph(["bar-tag", "baz-tag"],
assets_collection=bar_baz_assets)
@ -52,8 +52,8 @@ with tf.Session(graph=tf.Graph()) as sess:
builder.save()
...
with tf.Session(graph=tf.Graph()) as sess:
tf.saved_model.loader.load(sess, ["foo-tag"], export_dir)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
tf.compat.v1.saved_model.loader.load(sess, ["foo-tag"], export_dir)
...
```

12
tensorflow/python/saved_model/loader_impl.py
View File

@ -353,10 +353,10 @@ class SavedModelLoader(object):
"""Restore SavedModel variable values into the session.
Args:
sess: tf.Session to restore variable values.
saver: a tf.train.Saver object. Can be None if there are no variables in
graph. This may be the saver returned by the load_graph() function, or a
default `tf.train.Saver()`.
sess: tf.compat.v1.Session to restore variable values.
saver: a tf.compat.v1.train.Saver object. Can be None if there are no
variables in graph. This may be the saver returned by the load_graph()
function, or a default `tf.compat.v1.train.Saver()`.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to
tensor instances loaded into the passed session, but it is *not* written
@ -383,7 +383,7 @@ class SavedModelLoader(object):
"""Run initialization ops defined in the `MetaGraphDef`.
Args:
sess: tf.Session to restore variable values.
sess: tf.compat.v1.Session to restore variable values.
tags: a set of string tags identifying a MetaGraphDef.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to
@ -404,7 +404,7 @@ class SavedModelLoader(object):
"""Load the MetaGraphDef graph and restore variable values into the session.
Args:
sess: tf.Session to restore variable values.
sess: tf.compat.v1.Session to restore variable values.
tags: a set of string tags identifying a MetaGraphDef.
import_scope: Optional `string` -- if specified, prepend this string
followed by '/' to all loaded tensor names. This scope is applied to

2
tensorflow/python/saved_model/model_utils/export_utils.py
View File

@ -79,7 +79,7 @@ def build_all_signature_defs(receiver_tensors,
additional serving signatures, which may be used to feed inputs at
different points within the input receiver subgraph. A typical usage is
to allow feeding raw feature `Tensor`s *downstream* of the
tf.parse_example() op. Defaults to None.
tf.io.parse_example() op. Defaults to None.
serving_only: boolean; if true, resulting signature defs will only include
valid serving signatures. If false, all requested signatures will be
returned.

6
tensorflow/python/saved_model/save.py
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@ -773,9 +773,9 @@ def save(obj, export_dir, signatures=None):
@compatibility(eager)
Not well supported when graph building. From TensorFlow 1.x,
`tf.enable_eager_execution()` should run first. Calling tf.saved_model.save in
a loop when graph building from TensorFlow 1.x will add new save operations to
the default graph each iteration.
`tf.compat.v1.enable_eager_execution()` should run first. Calling
tf.saved_model.save in a loop when graph building from TensorFlow 1.x will
add new save operations to the default graph each iteration.
May not be called from within a function body.
@end_compatibility

2
tensorflow/python/saved_model/simple_save.py
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@ -45,7 +45,7 @@ def simple_save(session, export_dir, inputs, outputs, legacy_init_op=None):
Although in many cases it's not necessary to understand all of the many ways
to configure a SavedModel, this method has a few practical implications:
- It will be treated as a graph for inference / serving (i.e. uses the tag
`tag_constants.SERVING`)
`saved_model.SERVING`)
- The SavedModel will load in TensorFlow Serving and supports the
[Predict
API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto).