Fix double-pasted README and add code tags around the terminal commands

domain_adaptation/README.md

@@ -25,14 +25,20 @@ Twitter @bousmalis.
 In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
 domain separation (DSNs) you will need to set the directory you used to download
 MNIST and MNIST-M:\
+
+```
 $ export DSN_DATA_DIR=/your/dir
+```
 
 Then you need to build the binaries with Bazel:
 
+```
 $ bazel build -c opt domain_adaptation/domain_separation/...
+```
 
 You can then train with the following command:
 
+```
 $ ./bazel-bin/domain_adaptation/domain_separation/dsn_train  \
       --similarity_loss=dann_loss  \
       --basic_tower=dann_mnist  \
@@ -46,55 +52,13 @@ $ ./bazel-bin/domain_adaptation/domain_separation/dsn_train  \
       --master=""  \
       --dataset_dir=${DSN_DATA_DIR}  \
       -v --use_logging
+```
+
 
+Evaluation can be invoked with the following command:
 
-Evaluation can be invoked with the following command:\
+```
 $ ./bazel-bin/domain_adaptation/domain_separation/dsn_eval  \
     -v --dataset mnist_m --split test --num_examples=9001  \
     --dataset_dir=${DSN_DATA_DIR}
-# Domain Separation Networks
-
-## Introduction
-This code is the code used for the "Domain Separation Networks" paper
-by Bousmalis K., Trigeorgis G., et al. which was presented at NIPS 2016. The
-paper can be found here: https://arxiv.org/abs/1608.06019
-
-## Contact
-This code was open-sourced by Konstantinos Bousmalis (konstantinos@google.com, github:bousmalis)
-
-## Installation
-You will need to have the following installed on your machine before trying out the DSN code.
-
-*  Tensorflow: https://www.tensorflow.org/install/
-*  Bazel: https://bazel.build/
-
-## Running the code for adapting MNIST to MNIST-M
-In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
-domain separation (DSNs) you will need to set the directory you used to download
-MNIST and MNIST-M:\
-$ export DSN_DATA_DIR=/your/dir
-
-Then you need to build the binaries with Bazel:
-
-$ bazel build -c opt domain_adaptation/domain_separation/...
-
-Add models and models/slim to your $PYTHONPATH:
-
-$ export PYTHONPATH=$PYTHONPATH:$PWD/slim\
-$ export PYTHONPATH=$PYTHONPATH:$PWD
-
-You can then train with the following command:
-
-$ ./bazel-bin/domain_adaptation/domain_separation/dsn_train \
-      --similarity_loss=dann_loss  \
-      --basic_tower=dann_mnist  \
-      --source_dataset=mnist  \
-      --target_dataset=mnist_m  \
-      --learning_rate=0.0117249  \
-      --gamma_weight=0.251175  \
-      --weight_decay=1e-6  \
-      --layers_to_regularize=fc3  \
-      --nouse_separation  \
-      --master=""  \
-      --dataset_dir=${DSN_DATA_DIR}  \
-      -v --use_logging
+```