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author | TensorFlower Gardener <gardener@tensorflow.org> | 2018-07-12 13:13:46 -0700 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-07-12 13:13:55 -0700 |
commit | 513af8ee971237267c00b3e0f9f0dea05503f70a (patch) | |
tree | c271106c8779c9ee050429d96cfd8437b3b3be16 | |
parent | 9d41a9e66bb85cfe361507109ad896ee1ef5f6a8 (diff) | |
parent | 998e6aa7741dba78b3240a140de18674f61c669b (diff) |
Merge pull request #20678 from SneakyFish5:fix-typos
PiperOrigin-RevId: 204350215
-rw-r--r-- | tensorflow/contrib/copy_graph/python/util/copy_elements.py | 6 |
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 |