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Check batch_size % microbatches = 0 and calculate privacy budget only when dpsgd is set.

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PiperOrigin-RevId: 244949900
This commit is contained in:
A. Unique TensorFlower 2019-04-23 16:40:11 -07:00
parent a3e03f773e
commit ab466b156c
2 changed files with 20 additions and 12 deletions
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2
tutorials/mnist_dpsgd_tutorial.py
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@ -170,7 +170,7 @@ def load_mnist():
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.batch_size % FLAGS.microbatches != 0:
if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
raise ValueError('Number of microbatches should divide evenly batch_size')
# Load training and test data.

30
tutorials/mnist_dpsgd_tutorial_keras.py
View file

@ -46,6 +46,20 @@ tf.flags.DEFINE_string('model_dir', None, 'Model directory')
FLAGS = tf.flags.FLAGS
def compute_epsilon(steps):
"""Computes epsilon value for given hyperparameters."""
if FLAGS.noise_multiplier == 0.0:
return float('inf')
orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
sampling_probability = FLAGS.batch_size / 60000
rdp = compute_rdp(q=sampling_probability,
noise_multiplier=FLAGS.noise_multiplier,
steps=steps,
orders=orders)
# Delta is set to 1e-5 because MNIST has 60000 training points.
return get_privacy_spent(orders, rdp, target_delta=1e-5)[0]
def load_mnist():
"""Loads MNIST and preprocesses to combine training and validation data."""
train, test = tf.keras.datasets.mnist.load_data()
@ -74,7 +88,7 @@ def load_mnist():
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.batch_size % FLAGS.microbatches != 0:
if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
raise ValueError('Number of microbatches should divide evenly batch_size')
# Load training and test data.
@ -125,17 +139,11 @@ def main(unused_argv):
batch_size=FLAGS.batch_size)
# Compute the privacy budget expended.
if FLAGS.noise_multiplier == 0.0:
if FLAGS.dpsgd:
eps = compute_epsilon(FLAGS.epochs * 60000 // FLAGS.batch_size)
print('For delta=1e-5, the current epsilon is: %.2f' % eps)
else:
print('Trained with vanilla non-private SGD optimizer')
orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
sampling_probability = FLAGS.batch_size / 60000
rdp = compute_rdp(q=sampling_probability,
noise_multiplier=FLAGS.noise_multiplier,
steps=(FLAGS.epochs * 60000 // FLAGS.batch_size),
orders=orders)
# Delta is set to 1e-5 because MNIST has 60000 training points.
eps = get_privacy_spent(orders, rdp, target_delta=1e-5)[0]
print('For delta=1e-5, the current epsilon is: %.2f' % eps)
if __name__ == '__main__':
tf.app.run()