mse = mean_squared_error(test_queue[1], inferences)
rmse = np.sqrt(mse)
logging.info('rmse: %.4f[%.4f]' % (rmse, time() - start))
elif args.mode == 'autoneural':
start = time()
if dim == 2:
model = AutoNeural(num_users, num_items, args.embedding_dim,
args.weight_decay).cuda()
elif dim == 3:
model = AutoNeural_Triple(num_ps, num_qs, num_rs,
args.embedding_dim,
args.weight_decay).cuda()
embedding_oprimizer = torch.optim.Adagrad(model.embedding_parameters(),
args.lr)
mlp_optimizer = torch.optim.Adagrad(model.mlp_parameters(), args.lr)
losses = []
for train_epoch in range(args.train_epochs):
if dim == 2:
loss = train_single(train_queue, model, embedding_oprimizer,
args)
if train_epoch % 15000 == 0:
_ = train_single(valid_queue, model, mlp_optimizer, args)
_ = train_single(valid_queue, model, mlp_optimizer, args)
rmse = evaluate(model, test_queue)
elif dim == 3:
loss = train_single_triple(train_queue, model,
embedding_oprimizer, args)
