Improvements to several READMEs

differential_privacy/dp_sgd/README.md

@@ -8,14 +8,14 @@ Open Sourced By: Xin Pan (xpan@google.com, github: panyx0718)
 
 <Introduction>
 
-Machine learning techniques based on neural networks are achieving remarkable 
-results in a wide variety of domains. Often, the training of models requires 
-large, representative datasets, which may be crowdsourced and contain sensitive 
-information. The models should not expose private information in these datasets. 
-Addressing this goal, we develop new algorithmic techniques for learning and a 
-refined analysis of privacy costs within the framework of differential privacy. 
-Our implementation and experiments demonstrate that we can train deep neural 
-networks with non-convex objectives, under a modest privacy budget, and at a 
+Machine learning techniques based on neural networks are achieving remarkable
+results in a wide variety of domains. Often, the training of models requires
+large, representative datasets, which may be crowdsourced and contain sensitive
+information. The models should not expose private information in these datasets.
+Addressing this goal, we develop new algorithmic techniques for learning and a
+refined analysis of privacy costs within the framework of differential privacy.
+Our implementation and experiments demonstrate that we can train deep neural
+networks with non-convex objectives, under a modest privacy budget, and at a
 manageable cost in software complexity, training efficiency, and model quality.
 
 paper: https://arxiv.org/abs/1607.00133
@@ -46,7 +46,7 @@ https://github.com/panyx0718/models/tree/master/slim
 # Download the data to the data/ directory.
 
 # List the codes.
-ls -R differential_privacy/
+$ ls -R differential_privacy/
 differential_privacy/:
 dp_sgd  __init__.py  privacy_accountant  README.md
 
@@ -72,16 +72,16 @@ differential_privacy/privacy_accountant/tf:
 accountant.py  accountant_test.py  BUILD
 
 # List the data.
-ls -R data/
+$ ls -R data/
 
 ./data:
 mnist_test.tfrecord  mnist_train.tfrecord
 
 # Build the codes.
-bazel build -c opt differential_privacy/...
+$ bazel build -c opt differential_privacy/...
 
 # Run the mnist differntial privacy training codes.
-bazel-bin/differential_privacy/dp_sgd/dp_mnist/dp_mnist \
+$ bazel-bin/differential_privacy/dp_sgd/dp_mnist/dp_mnist \
     --training_data_path=data/mnist_train.tfrecord \
     --eval_data_path=data/mnist_test.tfrecord \
     --save_path=/tmp/mnist_dir
@@ -102,6 +102,6 @@ train_accuracy: 0.53
 eval_accuracy: 0.53
 ...
 
-ls /tmp/mnist_dir/
+$ ls /tmp/mnist_dir/
 checkpoint  ckpt  ckpt.meta  results-0.json
 ```

lm_1b/README.md

@@ -73,7 +73,7 @@ LSTM-8192-2048 (50\% Dropout) | 32.2 | 3.3
 
 <b>How To Run</b>
 
-Pre-requesite:
+Prerequisites:
 
 * Install TensorFlow.
 * Install Bazel.
@@ -97,15 +97,15 @@ Pre-requesite:
   [link](http://download.tensorflow.org/models/LM_LSTM_CNN/vocab-2016-09-10.txt)
   * test dataset: link
   [link](http://download.tensorflow.org/models/LM_LSTM_CNN/test/news.en.heldout-00000-of-00050)
-* It is recommended to run on modern desktop instead of laptop.
+* It is recommended to run on a modern desktop instead of a laptop.
 
 ```shell
 # 1. Clone the code to your workspace.
 # 2. Download the data to your workspace.
 # 3. Create an empty WORKSPACE file in your workspace.
 # 4. Create an empty output directory in your workspace.
 # Example directory structure below:
-ls -R
+$ ls -R
 .:
 data  lm_1b  output  WORKSPACE
 
@@ -121,13 +121,13 @@ BUILD  data_utils.py  lm_1b_eval.py  README.md
 ./output:
 
 # Build the codes.
-bazel build -c opt lm_1b/...
+$ bazel build -c opt lm_1b/...
 # Run sample mode:
-bazel-bin/lm_1b/lm_1b_eval --mode sample \
-                           --prefix "I love that I" \
-                           --pbtxt data/graph-2016-09-10.pbtxt \
-                           --vocab_file data/vocab-2016-09-10.txt  \
-                           --ckpt 'data/ckpt-*'
+$ bazel-bin/lm_1b/lm_1b_eval --mode sample \
+                             --prefix "I love that I" \
+                             --pbtxt data/graph-2016-09-10.pbtxt \
+                             --vocab_file data/vocab-2016-09-10.txt  \
+                             --ckpt 'data/ckpt-*'
 ...(omitted some TensorFlow output)
 I love
 I love that
@@ -138,11 +138,11 @@ I love that I find that amazing
 ...(omitted)
 
 # Run eval mode:
-bazel-bin/lm_1b/lm_1b_eval --mode eval \
-                           --pbtxt data/graph-2016-09-10.pbtxt \
-                           --vocab_file data/vocab-2016-09-10.txt  \
-                           --input_data data/news.en.heldout-00000-of-00050 \
-                           --ckpt 'data/ckpt-*'
+$ bazel-bin/lm_1b/lm_1b_eval --mode eval \
+                             --pbtxt data/graph-2016-09-10.pbtxt \
+                             --vocab_file data/vocab-2016-09-10.txt  \
+                             --input_data data/news.en.heldout-00000-of-00050 \
+                             --ckpt 'data/ckpt-*'
 ...(omitted some TensorFlow output)
 Loaded step 14108582.
 # perplexity is high initially because words without context are harder to
@@ -166,28 +166,28 @@ Eval Step: 4531, Average Perplexity: 29.285674.
 ...(omitted. At convergence, it should be around 30.)
 
 # Run dump_emb mode:
-bazel-bin/lm_1b/lm_1b_eval --mode dump_emb \
-                           --pbtxt data/graph-2016-09-10.pbtxt \
-                           --vocab_file data/vocab-2016-09-10.txt  \
-                           --ckpt 'data/ckpt-*' \
-                           --save_dir output
+$ bazel-bin/lm_1b/lm_1b_eval --mode dump_emb \
+                             --pbtxt data/graph-2016-09-10.pbtxt \
+                             --vocab_file data/vocab-2016-09-10.txt  \
+                             --ckpt 'data/ckpt-*' \
+                             --save_dir output
 ...(omitted some TensorFlow output)
 Finished softmax weights
 Finished word embedding 0/793471
 Finished word embedding 1/793471
 Finished word embedding 2/793471
 ...(omitted)
-ls output/
+$ ls output/
 embeddings_softmax.npy ...
 
 # Run dump_lstm_emb mode:
-bazel-bin/lm_1b/lm_1b_eval --mode dump_lstm_emb \
-                           --pbtxt data/graph-2016-09-10.pbtxt \
-                           --vocab_file data/vocab-2016-09-10.txt \
-                           --ckpt 'data/ckpt-*' \
-                           --sentence "I love who I am ." \
-                           --save_dir output
-ls output/
+$ bazel-bin/lm_1b/lm_1b_eval --mode dump_lstm_emb \
+                             --pbtxt data/graph-2016-09-10.pbtxt \
+                             --vocab_file data/vocab-2016-09-10.txt \
+                             --ckpt 'data/ckpt-*' \
+                             --sentence "I love who I am ." \
+                             --save_dir output
+$ ls output/
 lstm_emb_step_0.npy  lstm_emb_step_2.npy  lstm_emb_step_4.npy
 lstm_emb_step_6.npy  lstm_emb_step_1.npy  lstm_emb_step_3.npy
 lstm_emb_step_5.npy

next_frame_prediction/README.md

@@ -34,7 +34,7 @@ to tf.SequenceExample.
 <b>How to run:</b>
 
 ```shell
-ls -R
+$ ls -R
 .:
 data  next_frame_prediction  WORKSPACE
 
@@ -52,18 +52,18 @@ cross_conv2.png  cross_conv3.png  cross_conv.png
 
 
 # Build everything.
-bazel build -c opt next_frame_prediction/...
+$ bazel build -c opt next_frame_prediction/...
 
 # The following example runs the generated 2d objects.
 # For Sprites dataset, image_size should be 60, norm_scale should be 255.0.
 # Batch size is normally 16~64, depending on your memory size.
-#
+
 # Run training.
-bazel-bin/next_frame_prediction/cross_conv/train \
-  --batch_size=1 \
-  --data_filepattern=data/tfrecords \
-  --image_size=64 \
-  --log_root=/tmp/predict
+$ bazel-bin/next_frame_prediction/cross_conv/train \
+    --batch_size=1 \
+    --data_filepattern=data/tfrecords \
+    --image_size=64 \
+    --log_root=/tmp/predict
 
 step: 1, loss: 24.428671
 step: 2, loss: 19.211605
@@ -75,11 +75,11 @@ step: 7, loss: 1.747665
 step: 8, loss: 1.572436
 step: 9, loss: 1.586816
 step: 10, loss: 1.434191
-#
+
 # Run eval.
-bazel-bin/next_frame_prediction/cross_conv/eval \
-  --batch_size=1 \
-  --data_filepattern=data/tfrecords_test \
-  --image_size=64 \
-  --log_root=/tmp/predict
+$ bazel-bin/next_frame_prediction/cross_conv/eval \
+    --batch_size=1 \
+    --data_filepattern=data/tfrecords_test \
+    --image_size=64 \
+    --log_root=/tmp/predict
 ```

resnet/README.md

@@ -23,7 +23,7 @@ https://arxiv.org/pdf/1605.07146v1.pdf
 <b>Settings:</b>
 
 * Random split 50k training set into 45k/5k train/eval split.
-* Pad to 36x36 and random crop. Horizontal flip. Per-image whitenting. 
+* Pad to 36x36 and random crop. Horizontal flip. Per-image whitening.
 * Momentum optimizer 0.9.
 * Learning rate schedule: 0.1 (40k), 0.01 (60k), 0.001 (>60k).
 * L2 weight decay: 0.002.
@@ -65,40 +65,40 @@ curl -o cifar-100-binary.tar.gz https://www.cs.toronto.edu/~kriz/cifar-100-binar
 <b>How to run:</b>
 
 ```shell
-# cd to the your workspace.
+# cd to your workspace.
 # It contains an empty WORKSPACE file, resnet codes and cifar10 dataset.
 # Note: User can split 5k from train set for eval set.
-ls -R
-  .:
-  cifar10  resnet  WORKSPACE
+$ ls -R
+.:
+cifar10  resnet  WORKSPACE
 
-  ./cifar10:
-  data_batch_1.bin  data_batch_2.bin  data_batch_3.bin  data_batch_4.bin
-  data_batch_5.bin  test_batch.bin
+./cifar10:
+data_batch_1.bin  data_batch_2.bin  data_batch_3.bin  data_batch_4.bin
+data_batch_5.bin  test_batch.bin
 
-  ./resnet:
-  BUILD  cifar_input.py  g3doc  README.md  resnet_main.py  resnet_model.py
+./resnet:
+BUILD  cifar_input.py  g3doc  README.md  resnet_main.py  resnet_model.py
 
 # Build everything for GPU.
-bazel build -c opt --config=cuda resnet/...
+$ bazel build -c opt --config=cuda resnet/...
 
 # Train the model.
-bazel-bin/resnet/resnet_main --train_data_path=cifar10/data_batch* \
-                             --log_root=/tmp/resnet_model \
-                             --train_dir=/tmp/resnet_model/train \
-                             --dataset='cifar10' \
-                             --num_gpus=1
+$ bazel-bin/resnet/resnet_main --train_data_path=cifar10/data_batch* \
+                               --log_root=/tmp/resnet_model \
+                               --train_dir=/tmp/resnet_model/train \
+                               --dataset='cifar10' \
+                               --num_gpus=1
 
 # While the model is training, you can also check on its progress using tensorboard:
-tensorboard --logdir=/tmp/resnet_model
+$ tensorboard --logdir=/tmp/resnet_model
 
 # Evaluate the model.
 # Avoid running on the same GPU as the training job at the same time,
 # otherwise, you might run out of memory.
-bazel-bin/resnet/resnet_main --eval_data_path=cifar10/test_batch.bin \
-                             --log_root=/tmp/resnet_model \
-                             --eval_dir=/tmp/resnet_model/test \
-                             --mode=eval \
-                             --dataset='cifar10' \
-                             --num_gpus=0
+$ bazel-bin/resnet/resnet_main --eval_data_path=cifar10/test_batch.bin \
+                               --log_root=/tmp/resnet_model \
+                               --eval_dir=/tmp/resnet_model/test \
+                               --mode=eval \
+                               --dataset='cifar10' \
+                               --num_gpus=0
 ```

textsum/README.md

@@ -16,7 +16,7 @@ The results described below are based on model trained on multi-gpu and
 multi-machine settings. It has been simplified to run on only one machine
 for open source purpose.
 
-<b>DataSet</b>
+<b>Dataset</b>
 
 We used the Gigaword dataset described in [Rush et al. A Neural Attention Model
 for Sentence Summarization](https://arxiv.org/abs/1509.00685).