class_docstr: NAML model(Neural News Recommendation with Attentive Multi-View Learning)
  
  Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang and Xing Xie,
  Neural News Recommendation with Attentive Multi-View Learning, IJCAI 2019
  
  Attributes:
      word2vec_embedding (numpy.ndarray): Pretrained word embedding matrix.
      hparam (object): Global hyper-parameters.
class_docstr_tok: ["NAML","model","(","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",")","Chuhan","Wu",",","Fangzhao","Wu",",","Mingxiao","An",",","Jianqiang","Huang",",","Yongfeng","Huang","and","Xing","Xie",",","Neural","News","Recommendation","with","Attentive","Multi-View","Learning",",","IJCAI","2019","Attributes",":","word2vec_embedding","(","numpy.ndarray",")",":","Pretrained","word","embedding","matrix",".","hparam","(","object",")",":","Global","hyper-parameters","."]
docstr: Initialization steps for NAML.
  Compared with the BaseModel, NAML need word embedding.
  After creating word embedding matrix, BaseModel's __init__ method will be called.
  
  Args:
      hparams (object): Global hyper-parameters. Some key setttings such as filter_num are there.
      iterator_creator_train (object): NAML data loader class for train data.
      iterator_creator_test (object): NAML data loader class for test and validation data
docstr_tok: ["Initialization","steps","for","NAML",".","Compared","with","the","BaseModel",",","NAML","need","word","embedding",".","After","creating","word","embedding","matrix",",","BaseModel","'s","__init__","method","will","be","called",".","Args",":","hparams","(","object",")",":","Global","hyper-parameters",".","Some","key","setttings","such","as","filter_num","are","there",".","iterator_creator_train","(","object",")",":","NAML","data","loader","class","for","train","data",".","iterator_creator_test","(","object",")",":","NAML","data","loader","class","for","test","and","validation","data"]
code: def __init__(self, hparams, iterator_creator, seed=None):
      """Initialization steps for NAML.
      Compared with the BaseModel, NAML need word embedding.
      After creating word embedding matrix, BaseModel's __init__ method will be called.
  
      Args:
          hparams (object): Global hyper-parameters. Some key setttings such as filter_num are there.
          iterator_creator_train (object): NAML data loader class for train data.
          iterator_creator_test (object): NAML data loader class for test and validation data
      """
  
      self.word2vec_embedding = self._init_embedding(
          hparams.wordEmb_file
      )
      self.hparam = hparams
  
      super().__init__(hparams, iterator_creator, seed=seed)
  
file_path: recommenders/recommenders/models/newsrec/models/naml.py
imports: import numpy
  import tensorflow.keras
  from tensorflow.keras import layers
  from recommenders.models.newsrec.models.base_model import BaseModel
  from recommenders.models.newsrec.models.layers import AttLayer2
label_desc: Use image node_id 1 to create a new NAMLModel object from inherited base classes: BaseModel with example: obj = NAMLModel(hparams, iterator_creator, seed)