gae/model.py
from gae.layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder
import tensorflow as tf
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 GCNModelAE(Model):
def __init__(self, placeholders, num_features, features_nonzero, **kwargs):
super(GCNModelAE, 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.build()
def _build(self):
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)(self.inputs)
self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.z_mean = self.embeddings
self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)(self.embeddings)
class GCNModelVAE(Model):
def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs):
super(GCNModelVAE, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.n_samples = num_nodes
self.adj = placeholders['adj']
self.dropout = placeholders['dropout']
self.build()
def _build(self):
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)(self.inputs)
self.z_mean = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.z_log_std = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std)
self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)(self.z)