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authorGravatar SneakyFish5 <32284796+SneakyFish5@users.noreply.github.com>2018-07-10 21:25:49 -0500
committerGravatar GitHub <noreply@github.com>2018-07-10 21:25:49 -0500
commit998e6aa7741dba78b3240a140de18674f61c669b (patch)
treeac42a3e53a6212bc9e2f7f7fe15fe22c74db29a0 /tensorflow/contrib/copy_graph
parentb96954c367b24ad9c0614b1b7d0d9c36582e88d8 (diff)
Fix a couple typos
Diffstat (limited to 'tensorflow/contrib/copy_graph')
-rw-r--r--tensorflow/contrib/copy_graph/python/util/copy_elements.py6
1 files changed, 3 insertions, 3 deletions
diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
index a0dd3881a8..5931c8a279 100644
--- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py
+++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
@@ -18,7 +18,7 @@ These functions allow for recursive copying of elements (ops and variables)
from one graph to another. The copied elements are initialized inside a
user-specified scope in the other graph. There are separate functions to
copy ops and variables.
-There is also a function to retrive the copied version of an op from the
+There is also a function to retrieve the copied version of an op from the
first graph inside a scope in the second graph.
@@copy_op_to_graph
@@ -77,7 +77,7 @@ def copy_variable_to_graph(org_instance, to_graph, scope=''):
else:
collections.append(scope + '/' + name)
- #See if its trainable.
+ #See if it's trainable.
trainable = (
org_instance in org_instance.graph.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES))
@@ -162,7 +162,7 @@ def copy_op_to_graph(org_instance, to_graph, variables, scope=''):
if isinstance(org_instance, ops.Tensor):
- #If its a Tensor, it is one of the outputs of the underlying
+ #If it's a Tensor, it is one of the outputs of the underlying
#op. Therefore, copy the op itself and return the appropriate
#output.
op = org_instance.op