ruby/tensorflow_privacy
1
0
Fork 0
forked from 626_privacy/tensorflow_privacy
Code Pull requests Activity

Add files via upload

Browse source
This commit is contained in:
woodyx218 2020-01-22 10:28:09 +08:00 committed by GitHub
parent 2ef5c6e332
commit 239827251a
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 21 additions and 25 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
tutorials/adult_tutorial.py
View file

@ -27,8 +27,6 @@ import tensorflow as tf
import pandas as pd
from sklearn.model_selection import KFold
# from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
# from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
from tensorflow_privacy.privacy.analysis.gdp_accountant import *
@ -46,6 +44,7 @@ flags.DEFINE_integer('max_mu', 2, 'GDP upper limit')
flags.DEFINE_string('model_dir', None, 'Model directory')
microbatches = 256
num_examples = 29305
def nn_model_fn(features, labels, mode):
'''Define CNN architecture using tf.keras.layers.'''
@ -137,12 +136,12 @@ def main(unused_argv):
shuffle=False)
# Training loop.
steps_per_epoch = 29305 // 256
steps_per_epoch = num_examples // microbatches
test_accuracy_list = []
for epoch in range(1, FLAGS.epochs + 1):
for step in range(steps_per_epoch):
whether = np.random.random_sample(29305) > (1-256/29305)
subsampling = [i for i in np.arange(29305) if whether[i]]
whether = np.random.random_sample(num_examples) > (1-microbatches/num_examples)
subsampling = [i for i in np.arange(num_examples) if whether[i]]
global microbatches
microbatches = len(subsampling)
@ -163,8 +162,8 @@ def main(unused_argv):
# Compute the privacy budget expended so far.
if FLAGS.dpsgd:
eps = compute_eps_Poisson(epoch, FLAGS.noise_multiplier, 29305, 256, 1e-5)
mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 29305, 256)
eps = compute_eps_poisson(epoch, FLAGS.noise_multiplier, num_examples, 256, 1e-5)
mu = compute_mu_poisson(epoch, FLAGS.noise_multiplier, num_examples, 256)
print('For delta=1e-5, the current epsilon is: %.2f' % eps)
print('For delta=1e-5, the current mu is: %.2f' % mu)

14
tutorials/imdb_tutorial.py
View file

@ -26,8 +26,6 @@ import numpy as np
import tensorflow as tf
from keras.preprocessing import sequence
#from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
#from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
from tensorflow_privacy.privacy.analysis.gdp_accountant import *
@ -51,7 +49,7 @@ microbatches = 512
max_features = 10000
# cut texts after this number of words (among top max_features most common words)
maxlen = 256
num_examples = 25000
def nn_model_fn(features, labels, mode):
'''Define NN architecture using tf.keras.layers.'''
@ -139,13 +137,13 @@ def main(unused_argv):
shuffle=False)
# Training loop.
steps_per_epoch = 25000 // 512
steps_per_epoch = num_examples // microbatches
test_accuracy_list = []
for epoch in range(1, FLAGS.epochs + 1):
for step in range(steps_per_epoch):
whether = np.random.random_sample(25000) > (1-512/25000)
subsampling = [i for i in np.arange(25000) if whether[i]]
whether = np.random.random_sample(num_examples) > (1-microbatches/num_examples)
subsampling = [i for i in np.arange(num_examples) if whether[i]]
global microbatches
microbatches = len(subsampling)
@ -166,8 +164,8 @@ def main(unused_argv):
# Compute the privacy budget expended so far.
if FLAGS.dpsgd:
eps = compute_eps_Poisson(epoch, FLAGS.noise_multiplier, 25000, 512, 1e-5)
mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 25000, 512)
eps = compute_eps_poisson(epoch, FLAGS.noise_multiplier, num_examples, microbatches, 1e-5)
mu = compute_mu_poisson(epoch, FLAGS.noise_multiplier, num_examples, microbatches)
print('For delta=1e-5, the current epsilon is: %.2f' % eps)
print('For delta=1e-5, the current mu is: %.2f' % mu)

17
tutorials/movielens_tutorial.py
View file

@ -28,8 +28,6 @@ import pandas as pd
from scipy.stats import rankdata
from sklearn.model_selection import train_test_split
#from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
#from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
from tensorflow_privacy.privacy.analysis.gdp_accountant import *
@ -48,6 +46,7 @@ flags.DEFINE_string('model_dir', None, 'Model directory')
microbatches = 10000
num_examples = 800167
def nn_model_fn(features, labels, mode):
'''NN adapted from github.com/hexiangnan/neural_collaborative_filtering'''
@ -132,7 +131,7 @@ def nn_model_fn(features, labels, mode):
return None
def load_adult():
def load_movielens():
"""Loads MovieLens 1M as from https://grouplens.org/datasets/movielens/1m"""
data = pd.read_csv('ratings.dat', sep='::', header=None,
names=["userId", "movieId", "rating", "timestamp"])
@ -158,7 +157,7 @@ def main(unused_argv):
tf.compat.v1.logging.set_verbosity(3)
# Load training and test data.
train_data, test_data, mean = load_adult()
train_data, test_data, mean = load_movielens()
# Instantiate the tf.Estimator.
ml_classifier = tf.estimator.Estimator(model_fn=nn_model_fn,
@ -172,12 +171,12 @@ def main(unused_argv):
shuffle=False)
# Training loop.
steps_per_epoch = 800167 // 10000
steps_per_epoch = num_examples // microbatches
test_accuracy_list = []
for epoch in range(1, FLAGS.epochs + 1):
for step in range(steps_per_epoch):
whether = np.random.random_sample(800167) > (1-10000/800167)
subsampling = [i for i in np.arange(800167) if whether[i]]
whether = np.random.random_sample(num_examples) > (1-microbatches/num_examples)
subsampling = [i for i in np.arange(num_examples) if whether[i]]
global microbatches
microbatches = len(subsampling)
@ -198,8 +197,8 @@ def main(unused_argv):
# Compute the privacy budget expended so far.
if FLAGS.dpsgd:
eps = compute_eps_Poisson(epoch, FLAGS.noise_multiplier, 800167, 10000, 1e-6)
mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 800167, 10000)
eps = compute_eps_poisson(epoch, FLAGS.noise_multiplier, num_examples, microbatches, 1e-6)
mu = compute_mu_poisson(epoch, FLAGS.noise_multiplier, num_examples, microbatches)
print('For delta=1e-6, the current epsilon is: %.2f' % eps)
print('For delta=1e-6, the current mu is: %.2f' % mu)