def create_network(self):
self.A = tf.cast(self.A, tf.float32)
self.X = tf.cast(self.X, tf.float32)
self.P = tf.cast(self.P, tf.float32)
hidden_dim = self.FLAGS.hidden_dim
latent_dim = self.FLAGS.latent_dim
length = self.FLAGS.regularization_scale
num_train = self.FLAGS.num_train
num_layers = self.FLAGS.num_layers
num_attn = self.FLAGS.num_attn
self._X = blocks.encoder_gat_gate(self.X, self.A, num_layers, hidden_dim, num_attn, length, num_train)
self.Z, self.P_mean, self.P_logvar = blocks.readout_and_mlp(self._X, latent_dim, length, num_train)
self.loss = None
if(self.FLAGS.task_type == 'regression'):
self.loss = self.loss_regression(self.P, self.P_mean, self.P_logvar)
elif(self.FLAGS.task_type == 'classification'):
self.loss = self.loss_classification(self.P, self.P_mean)
self.lr = tf.Variable(0.0, trainable = False)
self.opt = self.optimizer( self.lr, self.FLAGS.optimizer )
self.sess = tf.Session()
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
self.saver = tf.train.Saver()
tf.train.start_queue_runners(sess=self.sess)
print ("Network Ready")
def create_network(self):
self.A = tf.cast(self.A, tf.float64)
self.X = tf.cast(self.X, tf.float64)
self.P = tf.cast(self.P, tf.float64)
self.Z = None
self._X = None
self._P = None
latent_dim = self.FLAGS.latent_dim
num_layers = self.FLAGS.num_layers
if self.FLAGS.model == 'GCN':
self._X = blocks.encoder_gcn(self.X, self.A, num_layers)
elif self.FLAGS.model == 'GCN+a':
self._X = blocks.encoder_gat(self.X, self.A, num_layers)
elif self.FLAGS.model == 'GCN+g':
self._X = blocks.encoder_gcn_gate(self.X, self.A, num_layers)
elif self.FLAGS.model == 'GCN+a+g':
self._X = blocks.encoder_gat_gate(self.X, self.A, num_layers)
elif self.FLAGS.model == 'GGNN':
self._X = blocks.encoder_ggnn(self.X, self.A, num_layers)
self.Z, self._P = blocks.readout_edgewise(self._X, latent_dim)
self.loss = self.calLoss(self.P, self._P, self.target_mask,
self.target_std, self.target_mean)
self.lr = tf.Variable(0.0, trainable=False)
self.opt = self.optimizer(self.lr, self.FLAGS.optimizer)
self.sess = tf.Session()
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
self.saver = tf.train.Saver()
tf.train.start_queue_runners(sess=self.sess)
print("Network Ready")
def create_network(self):
self.A = tf.cast(self.A, tf.float64)
self.X = tf.cast(self.X, tf.float64)
self.P = tf.cast(self.P, tf.float64)
self.Z = None
self._X = None
self._P = None
latent_dim = self.FLAGS.latent_dim
num_layers = self.FLAGS.num_layers
if (self.FLAGS.model == 'GCN'):
self._X = blocks.encoder_gcn(self.X, self.A, num_layers)
elif (self.FLAGS.model == 'GCN+a'):
self._X = blocks.encoder_gat(self.X, self.A, num_layers)
elif (self.FLAGS.model == 'GCN+g'):
self._X = blocks.encoder_gcn_gate(self.X, self.A, num_layers)
elif (self.FLAGS.model == 'GCN+a+g'):
self._X = blocks.encoder_gat_gate(self.X, self.A, num_layers)
if (self.FLAGS.readout == 'atomwise'):
self.Z, self._P = blocks.readout_atomwise(self._X, latent_dim)
elif (self.FLAGS.readout == 'graph_gather'):
self.Z, self._P = blocks.readout_graph_gather(
self.X, self._X, latent_dim)
self.loss = self.calLoss(self.P, self._P)
self.lr = tf.Variable(0.0, trainable=False)
self.opt = self.optimizer(self.lr, self.FLAGS.optimizer)
self.sess = tf.Session()
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
self.saver = tf.train.Saver(max_to_keep=1)
tf.add_to_collection('pred_prop', self._P)
tf.train.start_queue_runners(sess=self.sess)
print("Network Ready")