-
Notifications
You must be signed in to change notification settings - Fork 7
/
model.py
134 lines (105 loc) · 5.32 KB
/
model.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
from layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder
import tensorflow.compat.v1 as tf
import numpy as np
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
name = kwargs.get('name')
if not name:
name = self.__class__.__name__.lower()
self.name = name
logging = kwargs.get('logging', False)
self.logging = logging
self.vars = {}
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
self.vars = {var.name: var for var in variables}
def fit(self):
pass
def predict(self):
pass
class APGE(Model):
def __init__(self, placeholders, num_features, features_nonzero, privacy_attr,dim_attr, **kwargs):
super(APGE, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.adj = placeholders['adj']
self.dropout = placeholders['dropout']
self.sample = placeholders['sample']
self.privacy_attr = privacy_attr
self.dim_attr = dim_attr
self.build()
def _build(self):
with tf.variable_scope('Encoder', reuse=None):
self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging,
name='e_dense_1')(self.inputs)
#self.noise = gaussian_noise_layer(self.hidden1, 0.1)
self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging,
name='e_dense_2')(self.hidden1)
self.z_mean = self.embeddings
self.embeddings = tf.identity(self.embeddings, name="emb")
self.embeddings_long = tf.layers.dense(inputs=self.embeddings, units=64,activation=tf.nn.relu)
self.embeddings_concat = tf.concat([self.privacy_attr, self.embeddings_long], 1)
self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)(self.embeddings_concat)
self.attr_logits = tf.layers.dense(inputs=self.embeddings_concat, units=self.dim_attr[0])
self.pri_logits = dense(self.embeddings_long, 64, self.dim_attr[1], name='pri_den')
def dense(x, n1, n2, name):
"""
Used to create a dense layer.
:param x: input tensor to the dense layer
:param n1: no. of input neurons
:param n2: no. of output neurons
:param name: name of the entire dense layer.i.e, variable scope name.
:return: tensor with shape [batch_size, n2]
"""
with tf.variable_scope(name, reuse= tf.AUTO_REUSE):
# np.random.seed(1)
tf.set_random_seed(1)
weights = tf.get_variable("weights", shape=[n1, n2],
initializer=tf.random_normal_initializer(mean=0., stddev=0.01))
bias = tf.get_variable("bias", shape=[n2], initializer=tf.constant_initializer(0.0))
out = tf.add(tf.matmul(x, weights), bias, name='matmul')
return out
class Discriminator(Model):
def __init__(self, **kwargs):
super(Discriminator, self).__init__(**kwargs)
self.act = tf.nn.relu
def construct(self, inputs, reuse = False):
# with tf.name_scope('Discriminator'):
with tf.variable_scope('Discriminator'):
if reuse:
tf.get_variable_scope().reuse_variables()
# np.random.seed(1)
tf.set_random_seed(1)
dc_den1 = tf.nn.relu(dense(inputs, FLAGS.hidden2, FLAGS.hidden3, name='dc_den1'))
dc_den2 = tf.nn.relu(dense(dc_den1, FLAGS.hidden3, FLAGS.hidden4, name='dc_den2'))
output = dense(dc_den2, FLAGS.hidden4, 1, name='dc_output')
return output
def gaussian_noise_layer(input_layer, std):
noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32)
return input_layer + noise