namenotexist

gcmc embed_size=64, lr=0.0001, layer_size=[64,64,64], node_dropout=[0.1], mess_dropout=[0.1,0.1,0.1], regs=[1e-5], adj_type=gcmc Best Iter=[18]@[29694.0] recall=[0.11159 0.16493 0.20652 0.23976 0.26751], precision=[0.03519 0.02612 0.02181 0.01905 0.01708], hit=[0.42849 0.53815 0.60697 0.65262 0.68712], ndcg=[0.16498 0.19986 0.22356 0.24146 0.25595]

nmf embed_size=64, lr=0.0100, layer_size=[64], keep_prob=[0.9], regs=[1e-5,1e-5,1e-2], Best Iter=[8]@[8244.7] recall=[0.09032 0.14617 0.18829 0.22327 0.25221], precision=[0.02603 0.02144 0.01868 0.01683 0.01538], hit=[0.36710 0.50693 0.58889 0.64529 0.68404], ndcg=[0.11706 0.15456 0.17977 0.19965 0.21561], auc=[0.00000]

i try the most easy model MF, which has the same hyper-perameter setting as Sections 4.2.3，but only has 0.1055 recall@20, which is declared 0.1291 in your paper?

why the training process so slow

why did i often occur "loss is nan"

you mean tf.reduce_mean?

why did i can only use 149M GPU memory, which has a full memory of 10GB

> I have no idea... Have you installed the tensorflow-gpu successfully? i fix it . the reason is that i install tensorflow-gpu 1.14.0,when i change it to tensorflow-gpu 1.8.0, it...

Have you solved it? I have the same problem.

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