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