sep="\t")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = None
# Model selection
if args.model == "CDAE":
train_data, test_data, n_user, n_item = load_data_all(
test_size=0.2, sep="\t")
model = ICDAE(sess, n_user, n_item)
if args.model == "CML":
model = CML(sess, n_user, n_item)
if args.model == "LRML":
model = LRML(sess, n_user, n_item)
if args.model == "BPRMF":
model = BPRMF(sess, n_user, n_item)
if args.model == "NeuMF":
model = NeuMF(sess, n_user, n_item)
if args.model == "GMF":
model = GMF(sess, n_user, n_item)
if args.model == "MLP":
model = MLP(sess, n_user, n_item)
if args.model == "JRL":
model = JRL(sess, n_user, n_item)
# build and execute the model
if model is not None:
model.build_network()
model.execute(train_data, test_data)
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
model = None
# Model selection
if args.model == "CDAE":
train_data, test_data, n_user, n_item = load_data_all(test_size=0.2, sep="\t")
# model = ICDAE(n_user, n_item)
model = CDAE(n_user, n_item)
if args.model == "CML":
model = CML(n_user, n_item)
if args.model == "LRML":
model = LRML(n_user, n_item)
if args.model == "BPRMF":
model = BPRMF(n_user, n_item)
if args.model == "NeuMF":
model = NeuMF(n_user, n_item)
if args.model == "GMF":
model = GMF(n_user, n_item)
if args.model == "MLP":
model = MLP(n_user, n_item)
if args.model == "JRL":
model = JRL(n_user, n_item)
# build and execute the model
if model is not None:
model.build_network()
model.execute(train_data, test_data)