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[Diff Privacy] Update README and fix code exec wrt Python 3.6 #4410

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May 30, 2018
Merged

[Diff Privacy] Update README and fix code exec wrt Python 3.6 #4410

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32 changes: 18 additions & 14 deletions research/differential_privacy/dp_sgd/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,25 +27,23 @@ paper: https://arxiv.org/abs/1607.00133

Note: r0.11 might experience some problems

2. Bazel 0.3.1
2. Bazel 0.3.1 (<em>Optional</em>)

3. Download MNIST data

TODO(xpan): Complete the link:
[train](http://download.tensorflow.org/models/)
[test](http://download.tensorflow.org/models/)

Alternatively, download the tfrecord format MNIST from:
https://github.com/panyx0718/models/tree/master/slim
3. Download MNIST data (tfrecord format) <br>
```shell
cd models/research/slim
DATA_DIR=/tmp/mnist/
mkdir /tmp/mnist
python download_and_convert_data.py --dataset_name=mnist --dataset_dir="${DATA_DIR}"
```

<b>How to run:</b>

```shell
# Clone the codes under differential_privacy.
# Create an empty WORKSPACE file.
# Download the data to the data/ directory.

# List the codes.
# List the codes (Optional).
$ ls -R differential_privacy/
differential_privacy/:
dp_sgd __init__.py privacy_accountant README.md
Expand All @@ -71,21 +69,27 @@ BUILD gaussian_moments.py
differential_privacy/privacy_accountant/tf:
accountant.py accountant_test.py BUILD

# List the data.
# List the data (optional).
$ mv /tmp/mnist/mnist_train.tfrecord data
$ mv /tmp/mnist/mnist_test.tfrecord data
$ ls -R data/

./data:
mnist_test.tfrecord mnist_train.tfrecord

# Build the codes.
# Build the codes (optional).
$ bazel build -c opt differential_privacy/...

# Run the mnist differntial privacy training codes.
# Run the mnist differential privacy training codes.
# 1. With bazel
$ 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

# 2. Or without (by default data is in /tmp/mnist)
python dp_sgd/dp_mnist/dp_mnist.py

...
step: 1
step: 2
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2 changes: 1 addition & 1 deletion research/differential_privacy/dp_sgd/dp_mnist/dp_mnist.py
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Expand Up @@ -37,7 +37,7 @@
tf.flags.DEFINE_integer("batches_per_lot", 1,
"Number of batches per lot.")
# Together, batch_size and batches_per_lot determine lot_size.
tf.flags.DEFINE_integer("num_training_steps", 50000,
tf.flags.DEFINE_integer("num_training_steps", 100,
"The number of training steps."
"This counts number of lots.")

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2 changes: 1 addition & 1 deletion research/differential_privacy/dp_sgd/dp_optimizer/dp_optimizer.py
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Expand Up @@ -138,7 +138,7 @@ def minimize(self, loss, global_step=None, var_list=None,
sanitized_grads = self.compute_sanitized_gradients(
loss, var_list=var_list)

grads_and_vars = zip(sanitized_grads, var_list)
grads_and_vars = list(zip(sanitized_grads, var_list))
self._assert_valid_dtypes([v for g, v in grads_and_vars if g is not None])

apply_grads = self.apply_gradients(grads_and_vars,
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