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.sqrt(tf.exp(self.z_log_std))
self.soft_z = tf.nn.softmax(self.z,axis =-1)
self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)(self.z)
def decoder(self, z):
reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
dropout=0.,
logging=self.logging)(z)
reconstructions = tf.reshape(reconstructions, [-1])
return reconstructions
def make_decoder(self):
self.l0 = Dense(input_dim=self.input_dim,
output_dim=FLAGS.hidden3,
act=tf.nn.elu,
dropout=0.,
bias=True,
logging=self.logging)
self.l1 = Dense(input_dim=FLAGS.hidden2,
output_dim=FLAGS.hidden3,
act=tf.nn.elu,
dropout=0.,
bias=True,
logging=self.logging)
self.l2 = Dense(input_dim=FLAGS.hidden3,
output_dim=FLAGS.hidden2,
act=lambda x: x,
dropout=self.dropout,
bias=True,
logging=self.logging)
self.l3 = Dense(input_dim=2 * FLAGS.hidden2,
output_dim=FLAGS.hidden3,
act=tf.nn.elu,
dropout=self.dropout,
bias=True,
logging=self.logging)
self.l3p5 = Dense(input_dim=FLAGS.hidden3,
output_dim=FLAGS.hidden3,
act=tf.nn.elu,
dropout=self.dropout,
bias=True,
logging=self.logging)
self.l4 = Dense(input_dim=FLAGS.hidden3,
output_dim=1,
act=lambda x: x,
dropout=self.dropout,
bias=True,
logging=self.logging)
self.l5 = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)
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)
def make_decoder(self):
self.l0 = GraphiteSparse(input_dim=self.input_dim,
output_dim=FLAGS.hidden3,
act=tf.nn.relu,
dropout=0.,
logging=self.logging)
self.l1 = Graphite(input_dim=FLAGS.hidden2,
output_dim=FLAGS.hidden3,
act=tf.nn.relu,
dropout=0.,
logging=self.logging)
self.l2 = Graphite(input_dim=FLAGS.hidden3,
output_dim=FLAGS.hidden2,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)
self.l3 = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)
self.l4 = Scale(input_dim=FLAGS.hidden2, logging=self.logging)