-
Notifications
You must be signed in to change notification settings - Fork 9
/
vat_citation.py
273 lines (228 loc) · 10.1 KB
/
vat_citation.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
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf
from my_utils import *
from layers import *
from metrics import *
# 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_string('model', 'gcn', 'Model string.') # 'gcn', 'gcn_cheby', 'dense'
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('epochs', 400, 'Number of epochs to train.')
flags.DEFINE_integer('hidden1', 16, 'Number of units in hidden layer 1.')
flags.DEFINE_float('dropout', 0.0, 'Dropout rate (1 - keep probability).')
flags.DEFINE_float('weight_decay', 5e-4, 'Weight for L2 loss on embedding matrix.')
flags.DEFINE_integer('early_stopping', 400, 'Tolerance for early stopping (# of epochs).')
flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
flags.DEFINE_float('epsilon', 1.0, "norm length for (virtual) adversarial training ")
flags.DEFINE_integer('num_power_iterations', 1, "the number of power iterations")
flags.DEFINE_float('xi', 1e-4, "small constant for finite difference")
flags.DEFINE_float('alpha', 1.0, "Weight for VAT loss")
flags.DEFINE_bool('mask_vat', False, 'calculate vat loss only on unlabeled data.')
flags.DEFINE_bool('reload', False, 'reload parameter.')
flags.DEFINE_string('model_path', './model/vat/cora/model', 'path to reload model.')
flags.DEFINE_string('model_save_path', './model/vat/cora/model', 'path to save model.')
# Load data
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(FLAGS.dataset)
# there is a confusing point, the following line will print the default value
# of parameters, but will print the input value after the first call of FLAG
# values
print(FLAGS.flag_values_dict())
# Some preprocessing
features = preprocess_features(features)
if FLAGS.model == 'gcn':
# support = [preprocess_adj(adj)]
support = preprocess_adj(adj)
num_supports = 1
# elif FLAGS.model == 'gcn_cheby':
# support = chebyshev_polynomials(adj, FLAGS.max_degree)
# num_supports = 1 + FLAGS.max_degree
# model_func = GCN
# elif FLAGS.model == 'dense':
# support = [preprocess_adj(adj)] # Not used
# num_supports = 1
# model_func = MLP
else:
raise ValueError('Invalid argument for model: ' + str(FLAGS.model))
# Define placeholders
placeholders = {
# 'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'support': tf.sparse_placeholder(tf.float32),
# 'features': tf.sparse_placeholder(tf.float32, shape=tf.constant(features[2], dtype=tf.int64)),
'features': tf.placeholder(tf.float32, shape=(None, features.shape[1])),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask': tf.placeholder(tf.int32),
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero': tf.placeholder(tf.int32) # helper variable for sparse dropout
}
# Create model
def logit(x, is_training=True):
# first layer
x = tf.nn.dropout(x, 1 - placeholders['dropout'])
# x = sparse_dropout(x, 1 - placeholders['dropout'],
# placeholders['num_features_nonzero'])
l1_weights = tf.get_variable(
'l1_W', shape=[features.shape[1], FLAGS.hidden1],
initializer=tf.glorot_uniform_initializer()
)
# l1_biases = tf.get_variable(
# 'l1_b', shape=[FLAGS.hidden1], initializer=tf.constant_initializer(0.0)
# )
l1_out = tf.sparse_tensor_dense_matmul(
# placeholders['support'], tf.sparse_tensor_dense_matmul(x, l1_weights)
placeholders['support'], tf.matmul(x, l1_weights)
)
l1_out = tf.nn.relu(l1_out)
# second layer
l1_out = tf.nn.dropout(l1_out, 1 - placeholders['dropout'])
l2_weights = tf.get_variable(
'l2_W', shape=[FLAGS.hidden1, placeholders['labels'].get_shape().as_list()[1]],
initializer=tf.glorot_uniform_initializer()
)
output = tf.sparse_tensor_dense_matmul(
placeholders['support'], tf.matmul(l1_out, l2_weights)
)
return output
def get_normalized_vector(d):
d /= (1e-12 + tf.reduce_max(tf.abs(d), range(1, len(d.get_shape())), keep_dims=True))
d /= tf.sqrt(1e-6 + tf.reduce_sum(tf.pow(d, 2.0), range(1, len(d.get_shape())), keep_dims=True))
return d
def generate_virtual_adversarial_perturbation(x, logits, is_training=True):
d = tf.random_normal(shape=tf.shape(x))
for _ in range(FLAGS.num_power_iterations):
# d = FLAGS.xi * get_normalized_vector(d)
d = FLAGS.xi * tf.nn.l2_normalize(d, axis=1)
logit_p = logits
logit_m = logit(x + d, is_training=is_training)
# dist = kl_divergence_with_logit(logit_p, logit_m)
if FLAGS.mask_vat:
dist = my_kld_with_logit_with_mask(logit_p, logit_m,
placeholders['labels_mask'])
else:
dist = my_kld_with_logit(logit_p, logit_m)
grad = tf.gradients(dist, [d], aggregation_method=2)[0]
d = tf.stop_gradient(grad)
# return FLAGS.epsilon * get_normalized_vector(d)
return FLAGS.epsilon * tf.nn.l2_normalize(d, axis=1)
# return FLAGS.epsilon * tf.nn.l2_normalize(d, axis=1), d, dist, logit_m
def virtual_adversarial_loss(x, logits, is_training=True, name="vat_loss"):
r_vadv = generate_virtual_adversarial_perturbation(x, logits, is_training=is_training)
# r_vadv, r_d, r_dist, r_logit_m = generate_virtual_adversarial_perturbation(
# x, logits, is_training=is_training
# )
logits = tf.stop_gradient(logits)
logit_p = logits
logit_m = logit(x + r_vadv, is_training=is_training)
if FLAGS.mask_vat:
vat_loss = my_kld_with_logit_with_mask(logit_p, logit_m,
placeholders['labels_mask'])
else:
vat_loss = my_kld_with_logit(logit_p, logit_m)
return tf.identity(vat_loss, name=name)
# return tf.identity(vat_loss, name=name), r_vadv, r_d, r_dist, r_logit_m, logit_m
with tf.variable_scope("VGCN") as scope:
logits = logit(placeholders['features'])
sup_loss = masked_softmax_cross_entropy(
logits, placeholders['labels'], placeholders['labels_mask']
)
l2_norm = 0.0
for var in tf.trainable_variables():
l2_norm += tf.nn.l2_loss(var)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# vat_loss, r_vadv, r_d, r_dist, r_logit_m, logit_m = \
# virtual_adversarial_loss(placeholders['features'], logits)
vat_loss = virtual_adversarial_loss(placeholders['features'], logits)
obj_func = sup_loss + FLAGS.weight_decay * l2_norm + \
FLAGS.alpha * vat_loss
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
opt_op = optimizer.minimize(obj_func)
accuracy = masked_accuracy(logits, placeholders['labels'],
placeholders['labels_mask'])
# Initialize session
sess = tf.Session()
# Init variables
saver = tf.train.Saver()
if FLAGS.reload:
saver.restore(sess, FLAGS.model_path)
else:
sess.run(tf.global_variables_initializer())
# l1_wei = sess.run(tf.trainable_variables()[0])
# np.savetxt("my_l1_weights.csv", l1_wei)
# l2_wei = sess.run(tf.trainable_variables()[-1])
# np.savetxt("my_l2_weights.csv", l2_wei)
# writer = tf.summary.FileWriter('./my_train.log', sess.graph)
cost_val = []
# Train model
for epoch in range(FLAGS.epochs):
t = time.time()
# Construct feed dictionary
feed_dict = construct_feed_dict(features, support, y_train, train_mask, placeholders)
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
# Training step
outs = sess.run(
[opt_op, obj_func, accuracy, sup_loss, l2_norm, vat_loss],
# [opt_op, obj_func, accuracy, sup_loss, l2_norm, vat_loss, r_vadv,
# r_d, r_dist, logits, r_logit_m, logit_m],
feed_dict=feed_dict
)
# Print training results
print(
"Epoch:", '%04d' % (epoch + 1),
"tr_obj=", "{:.5f}".format(outs[1]),
"tr_acc=", "{:.5f}".format(outs[2]),
"tr_loss=", "{:.5f}".format(outs[3]),
"tr_l2=", "{:.5f}".format(outs[4]),
"tr_vat=", "{:.10f}".format(outs[5])
)
# Validation
feed_dict_val = construct_feed_dict(features, support, y_val, val_mask, placeholders)
outs_val = sess.run(
[obj_func, accuracy, sup_loss, l2_norm, vat_loss],
feed_dict=feed_dict_val
)
cost_val.append(outs_val[2])
# Print validation results
print(
"Epoch:", '%04d' % (epoch + 1),
"va_obj=", "{:.5f}".format(outs_val[0]),
"va_acc=", "{:.5f}".format(outs_val[1]),
"va_loss=", "{:.5f}".format(outs_val[2]),
"va_l2=", "{:.5f}".format(outs_val[3]),
"va_vat=", "{:.10f}".format(outs_val[4])
)
# Testing
feed_dict_tes = construct_feed_dict(features, support, y_test, test_mask,
placeholders)
outs_tes = sess.run(
[obj_func, accuracy, sup_loss, l2_norm, vat_loss],
feed_dict=feed_dict_tes
)
# Print testing results
print(
"Epoch:", '%04d' % (epoch + 1),
"te_obj=", "{:.5f}".format(outs_tes[0]),
"te_acc=", "{:.5f}".format(outs_tes[1]),
"te_loss=", "{:.5f}".format(outs_tes[2]),
"te_l2=", "{:.5f}".format(outs_tes[3]),
"te_vat=", "{:.10f}".format(outs_tes[4])
)
epoch_duration = time.time() - t
print('-------', 'time=', "{:.5f}".format(epoch_duration), '------')
if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(cost_val[-(FLAGS.early_stopping+1):-1]):
print("Early stopping...")
break
print("Optimization Finished!")
# Testing
t_test = time.time()
feed_dict_tes = construct_feed_dict(features, support, y_test, test_mask, placeholders)
outs_tes = sess.run([obj_func, accuracy, logits], feed_dict=feed_dict_tes)
test_cost, test_acc, test_duration = outs_tes[0], outs_tes[1], (time.time() - t_test)
print("Test set results:", "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "time=", "{:.5f}".format(test_duration))