d97e9cd0c30980647f31c1003d6367e6a41c7124,deepctr/models/pnn.py,,PNN,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,16

Before Change

    :param kernel_type: str,kernel_type used in outter-product,can be ``"mat"`` , ``"vec"`` or ``"num"``
    :return: A Keras model instance.
    
    if not isinstance(feature_dim_dict,
                      dict) or "sparse" not in feature_dim_dict or "dense" not in feature_dim_dict:
        raise ValueError(
            "feature_dim must be a dict like {"sparse":{"field_1":4,"field_2":3,"field_3":2},"dense":["field_5",]}")
    if kernel_type not in ["mat", "vec", "num"]:
        raise ValueError("kernel_type must be mat,vec or num")
    deep_emb_list, _, inputs_list = get_inputs_embedding(
        feature_dim_dict, embedding_size, l2_reg_embedding, 0, init_std, seed, False)

After Change

    :param kernel_type: str,kernel_type used in outter-product,can be ``"mat"`` , ``"vec"`` or ``"num"``
    :return: A Keras model instance.
    
    check_feature_config_dict(feature_dim_dict)

    if kernel_type not in ["mat", "vec", "num"]:
        raise ValueError("kernel_type must be mat,vec or num")
    deep_emb_list, _, inputs_list = get_inputs_embedding(

Instances