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.hidden1 = GraphConvolution(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
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)
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.hidden1 = GraphConvolution(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
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) # element-wise
self.reconstructions = InnerProductDecoder(
input_dim=FLAGS.hidden2,
act=lambda x: x,
# act=tf.nn.relu,
logging=self.logging)(self.z)