CODEWITHSUNDEEP
machine-learningrecommendation-engineknowledge-graphgraph-neural-network
actor 12
actor 12

Reputation: 11

In the training of KGCN, how are the project feature vectors and user feature vectors updated, and how is the loss of the model updated?

I'm having trouble reading the KGCN open source code used for recommendations.In the train method of the source code, the following statements were used for training the model:

# KGCN is a custom classes

model = KGCN(args, n_user, n_entity, n_relation, adj_entity, adj_relation)
with tf.Session() as sess:
    for step in range(args.n_epochs):
         _, loss = model.train(sess, get_feed_dict(model, train_data, start, start+args.batch_size))

But the model.train is defined as:

    def train(self, sess, feed_dict):
        return sess.run([self.optimizer, self.loss], feed_dict)

So how are the project feature vectors and user feature vectors updated, and how is the loss of the model updated? Please let me know if you need the source code.

Upvotes: 0

Views: 13

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