szha closed pull request #13280: [Example] Update cpp example README
URL: https://github.com/apache/incubator-mxnet/pull/13280
 
 
   

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diff --git a/cpp-package/example/README.md b/cpp-package/example/README.md
index 64f604469a7..06ea17bbc1f 100644
--- a/cpp-package/example/README.md
+++ b/cpp-package/example/README.md
@@ -20,15 +20,15 @@ The makefile will also download the necessary data files 
and store in a data fol
 
 ## Examples
 
-This directory contains following examples. In order to run the examples, 
ensure that the path to the MXNet shared library is added to the OS specific 
environment variable viz. **LD\_LIBRARY\_PATH** for Linux, Mac and Ubuntu OS 
and **PATH** for Windows OS.
+This directory contains following examples. In order to run the examples, 
ensure that the path to the MXNet shared library is added to the OS specific 
environment variable viz. **LD\_LIBRARY\_PATH** for Linux, Mac and Ubuntu OS 
and **PATH** for Windows OS. For example `export 
LD_LIBRARY_PATH=/usr/local/cuda/lib64:/home/ubuntu/incubator-mxnet/lib` on 
ubuntu using gpu.
 
 ### 
[alexnet.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/alexnet.cpp>)
 
 The example implements the C++ version of AlexNet. The networks trains on 
MNIST data. The number of epochs can be specified as a command line argument. 
For example to train with 10 epochs use the following:
 
-       ```
-       build/alexnet 10
-       ```
+```
+build/alexnet 10
+```
 
 ### 
[googlenet.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/googlenet.cpp>)
 
@@ -97,7 +97,7 @@ build/lenet_with_mxdataiter 10
 
 In addition, there is `run_lenet_with_mxdataiter.sh` that downloads the mnist 
data and run `lenet_with_mxdataiter` example.
 
-###[inception_bn.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inception_bn.cpp>)
+### 
[inception_bn.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inception_bn.cpp>)
 
 The code implements an Inception network using the C++ API with batch 
normalization. The example uses MNIST data to train the network. The model 
trains for 100 epochs. The example can be run by executing the following 
command:
 


 

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