-
Notifications
You must be signed in to change notification settings - Fork 0
/
adv_train.py
179 lines (146 loc) · 8.65 KB
/
adv_train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
#!/usr/bin/python3
# Script for adversarial training procedure
# See arXiv 1611.01046
import tensorflow as tf
import numpy as np
import pandas as pd
import time, os, sys
import argparse
# User-defined
from network import Network
from utils import Utils
from data import Data
from model import Model
from config import config_train, directories
tf.logging.set_verbosity(tf.logging.ERROR)
def train(config, args):
config.use_adversary = True
assert(config.use_adversary), 'use_adversary must be set to True!'
start_time = time.time()
pretrain_step, joint_step, v_auc_best, v_cvm = 0, 0, 0., 10.
ckpt = tf.train.get_checkpoint_state(directories.checkpoints)
print('Reading data ...')
if args.input is None:
input_file = directories.train
test_file = directories.test
else:
input_file = args.input
test_file = args.test
features, labels, pivots, pivot_labels = Data.load_data(input_file, adversary=True, parquet=args.parquet,
adv_n_classes=config.adv_n_classes)
print('FSHAPE', features.shape)
test_features, test_labels, test_pivots, test_pivot_labels = Data.load_data(test_file, adversary=True,
parquet=args.parquet, adv_n_classes=config.adv_n_classes)
# Build graph
model = Model(config, features=features, labels=labels, args=args)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
train_handle = sess.run(model.train_iterator.string_handle())
test_handle = sess.run(model.test_iterator.string_handle())
train_feed = {model.training_phase: True, model.handle: train_handle}
test_feed = {model.training_phase: False, model.handle: test_handle}
if args.restore_last and ckpt.model_checkpoint_path:
# Continue training saved model
saver.restore(sess, ckpt.model_checkpoint_path)
print('{} restored.'.format(ckpt.model_checkpoint_path))
else:
if args.restore_path:
new_saver = tf.train.import_meta_graph('{}.meta'.format(args.restore_path))
new_saver.restore(sess, args.restore_path)
print('{} restored.'.format(args.restore_path))
sess.run(model.test_iterator.initializer, feed_dict={
model.test_features_placeholder:test_features,
model.test_labels_placeholder:test_labels,
model.test_pivots_placeholder:test_pivots,
model.test_pivot_labels_placeholder:test_pivot_labels})
# Pretrain classifier
if not args.skip_pretrain:
print('Pretraining classifer for {} epochs'.format(config.n_epochs_initial))
for epoch in range(config.n_epochs_initial):
sess.run(model.train_iterator.initializer, feed_dict={
model.features_placeholder:features,
model.labels_placeholder:labels,
model.pivots_placeholder:pivots,
model.pivot_labels_placeholder:pivot_labels})
# Run utils
v_auc_best = Utils.run_diagnostics(model, config, directories, sess, saver, train_handle,
test_handle, start_time, v_auc_best, epoch, pretrain_step, args.name, v_cvm)
while True:
try:
pretrain_step += 1
# Update weights
sess.run([model.predictor_train_op, model.update_accuracy],
feed_dict=train_feed)
if pretrain_step % 1000 == 0:
# Periodically show diagnostics
v_MI_kraskov, v_pred, v_labels, v_pivots, v_conf = sess.run([model.MI_logits_theta_kraskov,
model.pred, model.labels, model.pivots[:,0], model.softmax], feed_dict=test_feed)
v_cvm = Utils.cvm_z(v_pivots, v_pred, v_labels, confidence=v_conf, selection_fraction=0.05)
v_auc_best = Utils.run_diagnostics(model, config_train, directories, sess, saver, train_handle,
test_handle, start_time, v_auc_best, epoch, pretrain_step, args.name, v_cvm)
except tf.errors.OutOfRangeError:
print('End of epoch!')
break
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'adv_pretrain_{}_end.ckpt'.format(args.name)),
global_step=epoch)
print("Initial training Complete. Model saved to file: {} Time elapsed: {:.3f} s".format(save_path, time.time()-start_time))
# Begin adversarial training
print('<<<============================ Pretraining complete. Beginning adversarial training ============================>>>')
for epoch in range(config.num_epochs):
sess.run(model.train_iterator.initializer, feed_dict={
model.features_placeholder:features,
model.labels_placeholder:labels,
model.pivots_placeholder:pivots,
model.pivot_labels_placeholder:pivot_labels})
if epoch > 0:
# Run utils
v_auc_best = Utils.run_adv_diagnostics(model, config, directories, sess, saver, train_handle,
test_handle, start_time, v_auc_best, epoch, joint_step, args.name, v_cvm)
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'adv_{}_epoch{}_step{}.ckpt'.format(args.name, epoch, joint_step)), global_step=epoch)
print('Starting epoch {}, Weights saved to file: {}'.format(epoch, save_path))
while True:
try:
# Train adversary for adv_iterations relative to predictive model
joint_step, *ops = sess.run([model.joint_step, model.joint_train_op, model.update_accuracy], train_feed)
for _ in range(config.adv_iterations):
sess.run([model.adversary_train_op], test_feed)
if joint_step % 1000 == 0: # Run diagnostics
v_MI_kraskov, v_pred, v_labels, v_pivots, v_conf = sess.run([model.MI_logits_theta_kraskov,
model.pred, model.labels, model.pivots[:,0], model.softmax], feed_dict=test_feed)
v_cvm = Utils.cvm_z(v_pivots, v_pred, v_labels, confidence=v_conf, selection_fraction=0.05)
v_auc_best = Utils.run_adv_diagnostics(model, config_train, directories, sess, saver, train_handle,
test_handle, start_time, v_auc_best, epoch, joint_step, args.name, v_cvm)
except tf.errors.OutOfRangeError:
print('End of epoch!')
break
except KeyboardInterrupt:
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'adv_{}_last.ckpt'.format(args.name)), global_step=epoch)
print('Interrupted, model saved to: ', save_path)
sys.exit()
save_path = saver.save(sess, os.path.join(directories.checkpoints,
'adv_{}_end.ckpt'.format(args.name)),
global_step=epoch)
print("Training Complete. Model saved to file: {} Time elapsed: {:.3f} s".format(save_path, time.time()-start_time))
def main(**kwargs):
parser = argparse.ArgumentParser()
parser.add_argument("-rl", "--restore_last", help="restore last saved model", action="store_true")
parser.add_argument("-r", "--restore_path", help="path to model to be restored", type=str)
parser.add_argument("-opt", "--optimizer", default="adam", help="Selected optimizer", type=str)
parser.add_argument("-n", "--name", default="adv", help="Checkpoint/Tensorboard label")
parser.add_argument("-i", "--input", default=None, help="Path to training file", type=str)
parser.add_argument("-test", "--test", default=None, help="Path to test file", type=str)
parser.add_argument("-pq", "--parquet", help="Use if dataset in parquet format", action="store_true")
parser.add_argument("-skip_pt", "--skip_pretrain", help="skip pretraining of classifier", action="store_true")
parser.add_argument("-lambda", "--adv_lambda", default=0.0, help="Adversary-classification tradeoff parameter",
type=float)
args = parser.parse_args()
config = config_train
# Launch training
train(config, args)
if __name__ == '__main__':
main()