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authorGravatar Shanqing Cai <cais@google.com>2017-03-31 09:31:36 -0800
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2017-03-31 10:47:23 -0700
commit0fe523ac05f1f49145f4b243953a7aad331ea4dc (patch)
treebf66a785d933f929d9447126cd0627942f83a909
parent93e822ea4f16ec33110ef4d2bb24d2f9aa2e9eaa (diff)
tfdbg doc: fix code blocks under numbered bullets
Change: 151838508
-rw-r--r--tensorflow/docs_src/programmers_guide/debugger.md4
1 files changed, 3 insertions, 1 deletions
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index 7ecddc548f..6f442e6e0c 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -418,8 +418,9 @@ that the graph contains, this kind of disk space issue can happen.
There are three possible workarounds or solutions:
1. The constructors of `LocalCLIDebugWrapperSession` and `LocalCLIDebugHook`
- provide a keyword argument, `dump_root`, with which you can specify the path
+ provide a keyword argument, `dump_root`, with which you can specify the path
to which **tfdbg** dumps the debug data. For example:
+
``` python
# For LocalCLIDebugWrapperSession
sess = tf_debug.LocalCLIDebugWrapperSession(dump_root="/with/lots/of/space")
@@ -432,6 +433,7 @@ There are three possible workarounds or solutions:
2. Reduce the batch size used during the runs.
3. Use the filtering options of **tfdbg**'s `run` command to watch only specific
nodes in the graph. For example:
+
```
tfdbg> run --node_name_filter .*hidden.*
tfdbg> run --op_type_filter Variable.*