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adv_train_pgd.py
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adv_train_pgd.py
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from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf
import scipy.sparse as sp
import matplotlib
matplotlib.use('Agg')
import copy
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from PGD_attack import PGDAttack
import os
from utils import load_data, preprocess_features, preprocess_adj, construct_feed_dict
from models import GCN
C = 1. # initial learning rate
ATTACK = True
# Set random seed
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)
# Settings
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('dataset', 'cora', 'Dataset string.') # 'cora', 'citeseer', 'pubmed'
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('att_steps', 40, 'Number of steps to attack.')
flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.')
flags.DEFINE_float('dropout', 0., 'Dropout rate (1 - keep probability).')
flags.DEFINE_float('weight_decay', 5e-4, 'Weight for L2 loss on embedding matrix.')
flags.DEFINE_integer('early_stopping', 10, 'Tolerance for early stopping (# of epochs).')
flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
flags.DEFINE_integer('train_steps', 400, 'Number of steps to train')
flags.DEFINE_bool('warm_start',False,'load saved model to start')
flags.DEFINE_bool('discrete',True,'use discret (0,1) adversarial examples to train')
flags.DEFINE_float('perturb_ratio', 0.05, 'perturb ratio of total edges.')
flags.DEFINE_string('save_dir','adv_train_models','directory to save adversarial trained models')
if not os.path.exists(FLAGS.save_dir):
os.makedirs(FLAGS.save_dir)
# Load data
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(FLAGS.dataset)
total_edges = adj.data.shape[0]/2
n_node = adj.shape[0]
# Some preprocessing
features = preprocess_features(features)
# for non sparse
features = sp.coo_matrix((features[1],(features[0][:,0],features[0][:,1])),shape=features[2]).toarray()
support = preprocess_adj(adj)
# for non sparse
support = [sp.coo_matrix((support[1],(support[0][:,0],support[0][:,1])),shape=support[2]).toarray()]
num_supports = 1
model_func = GCN
save_name = 'rob_'+FLAGS.dataset
if not os.path.exists(save_name):
os.makedirs(save_name)
# Define placeholders
placeholders = {
'lmd': tf.placeholder(tf.float32),
'mu': tf.placeholder(tf.float32),
's': [tf.placeholder(tf.float32, shape=(n_node,n_node)) for _ in range(num_supports)],
'adj': [tf.placeholder(tf.float32, shape=(n_node,n_node)) for _ in range(num_supports)],
'support': [tf.placeholder(tf.float32) for _ in range(num_supports)],
'features': tf.placeholder(tf.float32, shape=features.shape),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask': tf.placeholder(tf.int32),
'label_mask_expand': tf.placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero': tf.placeholder(tf.int32) # helper variable for sparse dropout
}
# Create model
# for non sparse
model = model_func(placeholders, input_dim=features.shape[1], attack='PGD', logging=False)
# Initialize session
sess = tf.Session()
def evaluate(features, support, labels, mask, placeholders):
t_test = time.time()
feed_dict_val = construct_feed_dict(features, support, labels, mask, placeholders)
feed_dict_val.update({placeholders['support'][i]: support[i] for i in range(len(support))})
outs_val = sess.run([model.attack_loss, model.accuracy], feed_dict=feed_dict_val)
return outs_val[0], outs_val[1], (time.time() - t_test)
# Init variables
sess.run(tf.global_variables_initializer())
if FLAGS.warm_start:
model.load(sess)
adj = adj.toarray()
nat_support = copy.deepcopy(support)
adv_support = new_adv_support = support[:]
lmd = 1
eps = total_edges * FLAGS.perturb_ratio
xi = 1e-5
mu = 200
attack_label = np.load('label_'+FLAGS.dataset+'.npy')
loss_record = []
best_acc, best_loss = 0, 1e8
attack = PGDAttack(sess, model, features, eps, FLAGS.att_steps, mu, adj, FLAGS.perturb_ratio)
for n in range(FLAGS.train_steps):
print('\n\n============================= iteration {}/{} =============================='.format(n+1,FLAGS.train_steps))
print('TRAIN')
train_label = attack_label
train_label_mask = train_mask + test_mask
old_adv_support = adv_support[:]
adv_support = new_adv_support[:]
print('support diff:',np.sum(old_adv_support[0]-adv_support[0]))
train_feed_dict = construct_feed_dict(features, adv_support, train_label, train_label_mask, placeholders)
train_feed_dict.update({placeholders['support'][i]:adv_support[i] for i in range(len(adv_support))})
train_feed_dict.update({placeholders['lmd']: lmd})
train_feed_dict.update({placeholders['dropout']: FLAGS.dropout})
train_feed_dict.update({placeholders['adj'][i]: adj for i in range(num_supports)}) # feed ori adj all the time
train_feed_dict.update({placeholders['s'][i]: np.zeros([n_node,n_node]) for i in range(num_supports)})
train_label_mask_expand = np.tile(train_label_mask, [train_label.shape[1],1]).transpose()
train_feed_dict.update({placeholders['label_mask_expand']: train_label_mask_expand})
train_feed_dict.update({placeholders['mu']: 0})
outs = sess.run([model.opt_op, model.loss, model.accuracy], feed_dict=train_feed_dict)
loss_record.append(outs[1])
print('[model outs] adv train acc: {}, adv train loss: {}'.format(outs[2], outs[1]))
print('----------------------------------------------------------------------------')
print('ATTACK')
attack_label_mask = train_mask+test_mask
attack_feed_dict = construct_feed_dict(features, support, attack_label, attack_label_mask, placeholders)
attack_feed_dict.update({placeholders['lmd']: lmd})
attack_feed_dict.update({placeholders['dropout']: FLAGS.dropout})
attack_feed_dict.update({placeholders['adj'][i]: adj for i in range(num_supports)})
attack_feed_dict.update({placeholders['s'][i]: np.zeros([n_node,n_node]) for i in range(num_supports)})
attack_label_mask_expand = np.tile(attack_label_mask, [attack_label.shape[1],1]).transpose()
attack_feed_dict.update({placeholders['label_mask_expand']: attack_label_mask_expand})
new_adv_support = attack.perturb(attack_feed_dict, FLAGS.discrete, attack_label, attack_label_mask, FLAGS.att_steps, ori_support=support)
train_loss, train_acc, _ = attack.evaluate(new_adv_support, y_train, train_mask)
test_loss_adv, test_acc_adv, _ = attack.evaluate(new_adv_support, y_test, test_mask)
print('[adv support] train acc: {}, train loss: {}, test acc: {}, test loss: {}'.format(train_acc, train_loss, test_acc_adv, test_loss_adv))
train_loss, train_acc, _ = attack.evaluate(nat_support, y_train, train_mask)
test_loss_nat, test_acc_nat, _ = attack.evaluate(nat_support, y_test, test_mask)
print('[nat support] train acc: {}, train loss: {}, test acc: {}, test loss: {}'.format(train_acc, train_loss, test_acc_nat, test_loss_nat))
if test_loss_adv < best_loss:
best_loss = test_loss_adv
model.save(sess, save_name + '/' + save_name)
# final evaluation
new_adv_support = attack.perturb(attack_feed_dict, FLAGS.discrete, attack_label, attack_label_mask, 100, ori_support=support)
test_cost, test_acc, test_duration = evaluate(features, new_adv_support, y_train, train_mask, placeholders)
print("Train set results:", "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration))
test_cost, test_acc, test_duration = evaluate(features, new_adv_support, y_val, val_mask, placeholders)
print("Validation set results:", "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration))
test_cost, test_acc, test_duration = evaluate(features, new_adv_support, y_test, test_mask, placeholders)
print("Test set results:", "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration))
loss_record = np.array(loss_record)
np.save('loss_record'+FLAGS.dataset+str(eps)+'.npy',loss_record)
del sess