d97e9cd0c30980647f31c1003d6367e6a41c7124,deepctr/models/deepfm.py,,DeepFM,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,17

Before Change

    if not isinstance(feature_dim_dict["sparse"], dict):
        raise ValueError("feature_dim_dict["sparse"] must be a dict,cur is", type(
            feature_dim_dict["sparse"]))
    if not isinstance(feature_dim_dict["dense"], list):
        raise ValueError("feature_dim_dict["dense"] must be a list,cur is", type(
            feature_dim_dict["dense"]))

    deep_emb_list, linear_logit, inputs_list = get_inputs_embedding(
        feature_dim_dict, embedding_size, l2_reg_embedding, l2_reg_linear, init_std, seed)

    fm_input = concat_fun(deep_emb_list,axis=1)

After Change

    :param use_bn: bool. Whether use BatchNormalization before activation or not.in deep net
    :return: A Keras model instance.
    
    check_feature_config_dict(feature_dim_dict)

    deep_emb_list, linear_logit, inputs_list = get_inputs_embedding(
        feature_dim_dict, embedding_size, l2_reg_embedding, l2_reg_linear, init_std, seed)

Instances