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main.py
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main.py
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"""
Paper: http://www.vldb.org/pvldb/vol11/p1071-park.pdf
Authors: Mahmoud Mohammadi, Noseong Park Adopted from https://github.com/carpedm20/DCGAN-tensorflow
Created : 07/20/2017
Modified: 10/15/2018
"""
import os
import datetime
import tensorflow as tf
import sys
from model import TableGan
from utils import pp, generate_data, show_all_variables
flags = tf.app.flags
flags.DEFINE_integer("epoch", 10, "Epoch to train [25]")
flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_integer("train_size", sys.maxsize, "The size of train images [np.inf]")
flags.DEFINE_integer("y_dim", 2, "Number of unique labels")
flags.DEFINE_integer("batch_size", 500, "The size of batch images [64]")
flags.DEFINE_integer("input_height", 16, "The size of image to use (will be center cropped). [108]")
flags.DEFINE_integer("input_width", None,
"The size of image to use (will be center cropped). If None, same value as input_height [None]")
flags.DEFINE_integer("output_height", 16, "The size of the output images to produce [64]")
flags.DEFINE_integer("output_width", None,
"The size of the output images to produce. If None, same value as output_height [None]")
flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, mnist, lsun]")
# flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
flags.DEFINE_string("checkpoint_par_dir", "checkpoint", "Parent Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("checkpoint_dir", "", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("generate_data", False, "True for visualizing, False for nothing [False]")
flags.DEFINE_float("alpha", 0.5, "The weight of original GAN part of loss function [0-1.0]")
flags.DEFINE_float("beta", 0.5, "The weight of information loss part of loss function [0-1.0]")
flags.DEFINE_float("delta_m", 0.5, "")
flags.DEFINE_float("delta_v", 0.5, "")
flags.DEFINE_string("test_id", "5555",
"The experiment settings ID.Affecting the values of alpha, beta, delta_m and delta_v.")
# flags.DEFINE_integer("maxcol", "0", " The maximum number of columns in ")
flags.DEFINE_integer("label_col", -1,
"The column used in the dataset as the label column (from 0). Used if the Classifer NN is active.")
flags.DEFINE_integer("attrib_num", 0, "The number of columns in the dataset. Used if the Classifer NN is active.")
flags.DEFINE_integer("feature_size", 266, "Size of last FC layer to calculate the Hinge Loss fucntion.")
flags.DEFINE_boolean("shadow_gan", False, "True for loading fake data from samples directory[False]")
flags.DEFINE_integer("shgan_input_type", 0, " Input for Discrimiator of shadow_gan. 1=Fake, 2=Test, 3=Train Data")
FLAGS = flags.FLAGS
def main(_):
a = datetime.datetime.now()
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_par_dir):
os.makedirs(FLAGS.checkpoint_par_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
test_cases = [
{'id': 'OI_11_00', 'alpha': 1.0, 'beta': 1.0, 'delta_v': 0.0, 'delta_m': 0.0}
, {'id': 'OI_11_11', 'alpha': 1.0, 'beta': 1.0, 'delta_v': 0.1, 'delta_m': 0.1}
, {'id': 'OI_11_22', 'alpha': 1.0, 'beta': 1.0, 'delta_v': 0.2, 'delta_m': 0.2}
, {'id': 'OI_101_00', 'alpha': 1.0, 'beta': 0.1, 'delta_v': 0.0, 'delta_m': 0.0}
, {'id': 'OI_101_11', 'alpha': 1.0, 'beta': 0.1, 'delta_v': 0.1, 'delta_m': 0.1}
, {'id': 'OI_101_22', 'alpha': 1.0, 'beta': 0.1, 'delta_v': 0.2, 'delta_m': 0.2}
, {'id': 'OI_1001_00', 'alpha': 1.0, 'beta': 0.01, 'delta_v': 0.0, 'delta_m': 0.0}
, {'id': 'OI_1001_11', 'alpha': 1.0, 'beta': 0.01, 'delta_v': 0.1, 'delta_m': 0.1}
, {'id': 'OI_1001_22', 'alpha': 1.0, 'beta': 0.01, 'delta_v': 0.2, 'delta_m': 0.2}
]
found = False
for case in test_cases:
if case['id'] == FLAGS.test_id:
found = True
FLAGS.alpha = case['alpha']
FLAGS.beta = case['beta']
FLAGS.delta_m = case['delta_m']
FLAGS.delta_v = case['delta_v']
print(case)
if not found:
print("Using OI_11_00")
FLAGS.test_id = "OI_11_00"
FLAGS.alpha = 1.0
FLAGS.beta = 1.0
FLAGS.delta_m = 0.0
FLAGS.delta_v = 0.0
FLAGS.input_height = 7
FLAGS.input_width = 7
FLAGS.output_height = 7
FLAGS.output_width = 7
if FLAGS.shadow_gan:
checkpoint_folder = FLAGS.checkpoint_par_dir + '/' + FLAGS.dataset + "/" + 'atk_' + FLAGS.test_id
else:
checkpoint_folder = f'{FLAGS.checkpoint_par_dir}/{FLAGS.dataset}/{FLAGS.test_id}'
if not os.path.exists(checkpoint_folder):
os.makedirs(checkpoint_folder)
FLAGS.checkpoint_dir = checkpoint_folder
pp.pprint(flags.FLAGS.__flags)
print(FLAGS.y_dim)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
print("Chekcpoint : " + FLAGS.checkpoint_dir)
with tf.Session(config=run_config) as sess:
tablegan = TableGan(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
y_dim=FLAGS.y_dim,
dataset_name=FLAGS.dataset,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
alpha=FLAGS.alpha,
beta=FLAGS.beta,
delta_mean=FLAGS.delta_m,
delta_var=FLAGS.delta_v,
label_col=FLAGS.label_col,
attrib_num=FLAGS.attrib_num,
is_shadow_gan=FLAGS.shadow_gan,
test_id=FLAGS.test_id
)
show_all_variables()
if FLAGS.train:
tablegan.train(FLAGS)
else:
if not tablegan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
# Below is codes for visualization
if FLAGS.shadow_gan: # using Disriminator sampler for Membership Attack
OPTION = 5
else:
OPTION = 1
generate_data(sess, tablegan, FLAGS, OPTION)
print('Time Elapsed: ')
b = datetime.datetime.now()
print(b - a)
if __name__ == '__main__':
tf.app.run()