def build_graph(self):
self._create_variable()
with tf.name_scope("inference"):
self.ua_embeddings, self.ia_embeddings, self.ua_embeddings_sub1, self.ia_embeddings_sub1, self.ua_embeddings_sub2, self.ia_embeddings_sub2 = self._create_lightgcn_SSL_embed(
)
"""
*********************************************************
Generate Predictions & Optimize via BPR loss.
"""
with tf.name_scope("loss"):
if self.pretrain:
self.ssl_loss = tf.constant(0, dtype=tf.float32)
else:
if self.ssl_mode in ['user_side', 'item_side', 'both_side']:
# self.ssl_loss = self.calc_ssl_loss()
self.ssl_loss = self.calc_ssl_loss_v2()
elif self.ssl_mode in ['merge']:
self.ssl_loss = self.calc_ssl_loss_v3()
else:
raise ValueError("Invalid ssl_mode!")
self.sl_loss, self.emb_loss = self.create_bpr_loss()
self.loss = self.sl_loss + self.emb_loss + self.ssl_loss
with tf.name_scope("learner"):
# self.opt = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.loss)
self.opt = learner.optimizer(self.learner, self.loss, self.lr)
self.saver = tf.train.Saver()
def _create_optimizer(self):
with tf.name_scope("learner"):
self.optimizer = learner.optimizer(self.learner, self.loss,
self.learning_rate)
#!/usr/local/bin/python
