else:
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
train_support = sparse_to_tuple(train_support)
train_support_t = sparse_to_tuple(train_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
assert u_features[2][1] == v_features[2][
1], 'Number of features of users and items must be the same!'
num_features = u_features[2][1]
u_features_nonzero = u_features[1].shape[0]
# create model
model = RecommenderGAE(placeholders,
input_dim=u_features.shape[1],
num_classes=NUMCLASSES,
num_support=num_support,
self_connections=SELFCONNECTIONS,
num_basis_functions=BASES,
hidden=HIDDEN,
num_users=num_users,
num_items=num_items,
accum=ACCUM,
learning_rate=LR,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
assert u_features[2][1] == v_features[2][
1], 'Number of features of users and items must be the same!'
num_features = u_features[2][1]
u_features_nonzero = u_features[1].shape[0]
v_features_nonzero = v_features[1].shape[0]
# Feed_dicts for validation and test set stay constant over different update steps
