-
Notifications
You must be signed in to change notification settings - Fork 65
/
models.py
181 lines (139 loc) · 6.33 KB
/
models.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
from layers import *
from ops import *
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
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 = {}
self.placeholders = {}
self.layers = []
self.activations = []
self.inputs = None
self.outputs = None
self.loss = 0
self.accuracy = 0
self.optimizer = None
self.opt_op = None
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
# Build sequential layer model
self.activations.append(self.inputs)
for layer in self.layers:
hidden = layer(self.activations[-1])
self.activations.append(hidden)
self.outputs = self.activations[-1]
# Store model variables for easy access
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
self.vars = {var.name: var for var in variables}
# Build metrics
self._loss()
self._accuracy()
self.opt_op = self.optimizer.minimize(self.loss)
def predict(self):
pass
def _loss(self):
raise NotImplementedError
def _accuracy(self):
raise NotImplementedError
def save(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = saver.save(sess, "tmp/%s.ckpt" % self.name)
print("Model saved in file: %s" % save_path)
def load(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = "tmp/%s.ckpt" % self.name
saver.restore(sess, save_path)
print("Model restored from file: %s" % save_path)
class MLP(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(MLP, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
for var in self.layers[0].vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(Dense(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=True,
logging=self.logging))
self.layers.append(Dense(input_dim=FLAGS.hidden1,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)
class GCN(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCN, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
for var in self.layers[0].vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(GraphConvolution(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=False,
#bias=True,
logging=self.logging,
name='conv1'))
self.layers.append(GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
#bias=True,
logging=self.logging,
name='conv2'))
def predict(self):
return tf.nn.softmax(self.outputs)