def DeepFM(linear_feature_columns, dnn_feature_columns, fm_group=(DEFAULT_GROUP_NAME,), dnn_hidden_units=(256, 128, 64),
           l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0,
           dnn_activation='relu', dnn_use_bn=False, task='binary'):
    """Instantiates the DeepFM Network architecture.

    :param linear_feature_columns: An iterable containing all the features used by the linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by the deep part of the model.
    :param fm_group: list, group_name of features that will be used to do feature interactions.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)

    group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
                                                                        seed, support_group=True)

    fm_logit = add_func([FM()(concat_func(v, axis=1))
                         for k, v in group_embedding_dict.items() if k in fm_group])

    dnn_input = combined_dnn_input(list(chain.from_iterable(
        group_embedding_dict.values())), dense_value_list)
    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
    dnn_logit = Dense(1, use_bias=False)(dnn_output)

    final_logit = add_func([linear_logit, fm_logit, dnn_logit])

    output = PredictionLayer(task)(final_logit)
    model = Model(inputs=inputs_list, outputs=output)
    return model
Instantiates the DeepFM Network architecture.

    :param linear_feature_columns: An iterable containing all the features used by the linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by the deep part of the model.
    :param fm_group: list, group_name of features that will be used to do feature interactions.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    
deepctr/models/deepfm.py