rtt-logistic_regression_restore.py

#!/usr/bin/env python3
import latticex.rosetta as rtt  # difference from tensorflow
import math
import os
import csv
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) + "/cls_test_x.csv"
file_y = '../dsets/P' + str(mpc_player_id) + "/cls_test_y.csv"
real_X, real_Y = rtt.PrivateDataset(data_owner=(
    0, 1), label_owner=0).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])
print(X)
print(Y)

# initialize W & b
W = tf.Variable(tf.zeros([DIM_NUM, 1], dtype=tf.float64), name='w')
b = tf.Variable(tf.zeros([1], dtype=tf.float64), name='b')
print(W)
print(b)

# predict
pred_Y = tf.sigmoid(tf.matmul(X, W) + b)
print(pred_Y)


# save
saver = tf.train.Saver(var_list=None, max_to_keep=5, name='v2')
os.makedirs("./log/ckpt"+str(mpc_player_id), exist_ok=True)

# init
init = tf.global_variables_initializer()
reveal_Y = rtt.SecureReveal(pred_Y)
with tf.Session() as sess:
    sess.run(init)
    if os.path.exists('./log/ckpt'+str(mpc_player_id)+'/checkpoint'):
        saver.restore(sess, './log/ckpt'+str(mpc_player_id)+'/model')

    # predict
    Y_pred = sess.run(pred_Y, feed_dict={X: real_X})
    print("Y_pred:", Y_pred)

    reveal_y = sess.run(reveal_Y, feed_dict={X: real_X})
    print("reveal_Y:", reveal_y)

print(rtt.get_perf_stats(True))
rtt.deactivate()