text: # -*- coding:utf-8 -*-
  """
  Author:
      Weichen Shen,wcshen1994@163.com
  
  Reference:
      [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)
  """
  
  from tensorflow.python.keras.layers import Dense
  from tensorflow.python.keras.models import Model
  
  from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
  from ..layers.core import PredictionLayer, DNN
  from ..layers.utils import add_func, combined_dnn_input
  
  
  def WDL(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_linear=1e-5,
          l2_reg_embedding=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu',
          task='binary'):
      """Instantiates the Wide&Deep Learning architecture.
  
      :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
      :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
      :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 wide 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 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)
  
      sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                           l2_reg_embedding, seed)
  
      dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
      dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                    False, seed)(dnn_input)
      dnn_logit = Dense(
          1, use_bias=False, activation=None)(dnn_out)
  
      final_logit = add_func([dnn_logit, linear_logit])
  
      output = PredictionLayer(task)(final_logit)
  
      model = Model(inputs=inputs_list, outputs=output)
      return model
  