def main():
# parse arguments
args = parse_args()
if args is None:
exit()
if args.model_public:
rtt.set_restore_model(False)
else:
rtt.set_restore_model(False, plain_model='P0')
# open session
# with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
with tf.Session() as sess:
start_time = time.time()
cnn = ResNet(sess, args)
# build graph
cnn.build_model()
print("pystats build_model elapse:{0} s".format(time.time() -
start_time))
# show network architecture
show_all_variables()
# if args.phase == 'train':
# # launch the graph in a session
# cnn.train()
# print(" [*] Training finished! \n")
# cnn.test()
# print(" [*] Test finished!")
if args.phase == 'test':
start_time = time.time()
cnn.test(args)
print("pystats predict elapse:{0} s".format(time.time() -
start_time))
print(" [*] Test finished!")
from util import read_dataset
np.set_printoptions(suppress=True)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.random.seed(0)
EPOCHES = 10
BATCH_SIZE = 16
learning_rate = 0.0002
task_id = 'task-id'
rtt.py_protocol_handler.set_loglevel(0)
rtt.activate("Helix", task_id=task_id)
rtt.set_restore_model(['p0', 'p1', 'p2'], task_id=task_id)
#rtt.set_restore_model(['p0', 'p1', 'p2'])
node_id = rtt.get_current_node_id(task_id=task_id)
# real data
# ######################################## difference from tensorflow
file_x = '../dsets/' + node_id + "/reg_test_x.csv"
file_y = '../dsets/' + node_id + "/reg_test_y.csv"
real_X, real_Y = rtt.PrivateDataset(data_owner=(0, 'p9'),
label_owner=1,
task_id=task_id).load_data(file_x,
file_y,
header=None)
# ######################################## difference from tensorflow
DIM_NUM = real_X.shape[1]
import tensorflow as tf
import numpy as np
from util import read_dataset
np.set_printoptions(suppress=True)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.random.seed(0)
EPOCHES = 10
BATCH_SIZE = 16
learning_rate = 0.0002
rtt.activate("SecureNN")
rtt.set_restore_model(False, plain_model='P0')
mpc_player_id = rtt.py_protocol_handler.get_party_id()
# real data
# ######################################## difference from tensorflow
file_x = '../dsets/P' + str(mpc_player_id) + "/reg_test_x.csv"
file_y = '../dsets/P' + str(mpc_player_id) + "/reg_test_y.csv"
real_X, real_Y = rtt.PrivateDataset(data_owner=(0, 1),
label_owner=1).load_data(file_x,
file_y,
header=None)
# ######################################## difference from tensorflow
DIM_NUM = real_X.shape[1]
X = tf.placeholder(tf.float64, [None, DIM_NUM])
Y = tf.placeholder(tf.float64, [None, 1])
import numpy as np
from util import read_dataset
np.set_printoptions(suppress=True)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.random.seed(0)
EPOCHES = 10
BATCH_SIZE = 16
learning_rate = 0.0002
rtt.py_protocol_handler.set_loglevel(0)
rtt.activate("Helix")
rtt.set_restore_model(['p0', 'p1', 'p2'])
#rtt.set_restore_model(['p0', 'p1', 'p2'])
node_id = rtt.get_current_node_id()
# real data
# ######################################## difference from tensorflow
file_x = '../dsets/' + node_id + "/reg_test_x.csv"
file_y = '../dsets/' + node_id + "/reg_test_y.csv"
real_X, real_Y = rtt.PrivateDataset(data_owner=(0, 'p9'),
label_owner=1).load_data(file_x,
file_y,
header=None)
# ######################################## difference from tensorflow
DIM_NUM = real_X.shape[1]
X = tf.placeholder(tf.float64, [None, DIM_NUM])