class_docstr: Collaborative Deep Ranking.
  
  Parameters
  ----------
  k: int, optional, default: 50
      The dimension of the latent factors.
  
  max_iter: int, optional, default: 100
      Maximum number of iterations or the number of epochs for SGD.
  
  autoencoder_structure: list, default: None
      The number of neurons of encoder/decoder layer for SDAE.
      For example, autoencoder_structure = [200], the SDAE structure will be [vocab_size, 200, k, 200, vocab_size]
  
  act_fn: str, default: 'relu'
      Name of the activation function used for the auto-encoder.
      Supported functions: ['sigmoid', 'tanh', 'elu', 'relu', 'relu6', 'leaky_relu', 'identity']
  
  learning_rate: float, optional, default: 0.001
      The learning rate for AdamOptimizer.
  
  lambda_u: float, optional, default: 0.1
      The regularization parameter for users.
  
  lambda_v: float, optional, default: 10
      The regularization parameter for items.
  
  lambda_w: float, optional, default: 0.1
      The regularization parameter for SDAE weights.
  
  lambda_n: float, optional, default: 1000
      The regularization parameter for SDAE output.
  
  corruption_rate: float, optional, default: 0.3
      The corruption ratio for SDAE.
  
  dropout_rate: float, optional, default: 0.1
      The probability that each element is removed in dropout of SDAE.
  
  batch_size: int, optional, default: 128
      The batch size for SGD.
  
  name: string, optional, default: 'CDR'
      The name of the recommender model.
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model already
      pre-trained (U and V are not None).
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'U':U, 'V':V}
  
      U: ndarray, shape (n_users,k)
          The user latent factors, optional initialization via init_params.
      V: ndarray, shape (n_items,k)
          The item latent factors, optional initialization via init_params.
  
  seed: int, optional, default: None
      Random seed for weight initialization.
  
  References
  ----------
  Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback
  Ying H., Chen L., Xiong Y., Wu J. (2016)
class_docstr_tok: ["Collaborative","Deep","Ranking",".","Parameters","--","--","--","--","--","k",":","int",",","optional",",","default",":","50","The","dimension","of","the","latent","factors",".","max_iter",":","int",",","optional",",","default",":","100","Maximum","number","of","iterations","or","the","number","of","epochs","for","SGD",".","autoencoder_structure",":","list",",","default",":","None","The","number","of","neurons","of","encoder\/decoder","layer","for","SDAE",".","For","example",",","autoencoder_structure","=","[","200","]",",","the","SDAE","structure","will","be","[","vocab_size",",","200",",","k",",","200",",","vocab_size","]","act_fn",":","str",",","default",":","'relu'","Name","of","the","activation","function","used","for","the","auto-encoder",".","Supported","functions",":","[","'sigmoid","'",",","'tanh","'",",","'elu","'",",","'relu","'",",","'relu6","'",",","'leaky_relu","'",",","'identity","'","]","learning_rate",":","float",",","optional",",","default",":","0.001","The","learning","rate","for","AdamOptimizer",".","lambda_u",":","float",",","optional",",","default",":","0.1","The","regularization","parameter","for","users",".","lambda_v",":","float",",","optional",",","default",":","10","The","regularization","parameter","for","items",".","lambda_w",":","float",",","optional",",","default",":","0.1","The","regularization","parameter","for","SDAE","weights",".","lambda_n",":","float",",","optional",",","default",":","1000","The","regularization","parameter","for","SDAE","output",".","corruption_rate",":","float",",","optional",",","default",":","0.3","The","corruption","ratio","for","SDAE",".","dropout_rate",":","float",",","optional",",","default",":","0.1","The","probability","that","each","element","is","removed","in","dropout","of","SDAE",".","batch_size",":","int",",","optional",",","default",":","128","The","batch","size","for","SGD",".","name",":","string",",","optional",",","default",":","'CDR'","The","name","of","the","recommender","model",".","trainable",":","boolean",",","optional",",","default",":","True","When","False",",","the","model","is","not","trained","and","Cornac","assumes","that","the","model","already","pre-trained","(","U","and","V","are","not","None",")",".","init_params",":","dictionary",",","optional",",","default",":","None","List","of","initial","parameters",",","e.g.",",","init_params","=","{","'","U","'",":","U",",","'","V","'",":","V","}","U",":","ndarray",",","shape","(","n_users",",","k",")","The","user","latent","factors",",","optional","initialization","via","init_params",".","V",":","ndarray",",","shape","(","n_items",",","k",")","The","item","latent","factors",",","optional","initialization","via","init_params",".","seed",":","int",",","optional",",","default",":","None","Random","seed","for","weight","initialization",".","References","--","--","--","--","--","Collaborative","Deep","Ranking",":","A","Hybrid","Pair-Wise","Recommendation","Algorithm","with","Implicit","Feedback","Ying","H.",",","Chen","L.",",","Xiong","Y.",",","Wu","J",".","(","2016",")"]
docstr: Getting a valid choice of vector measurement in ANNMixin._measures.
  
  Returns
  -------
  measure: MEASURE_DOT
      Dot product aka. inner product
docstr_tok: ["Getting","a","valid","choice","of","vector","measurement","in","ANNMixin._measures",".","Returns","--","--","--","-","measure",":","MEASURE_DOT","Dot","product","aka",".","inner","product"]
code: def get_vector_measure(self):
      """Getting a valid choice of vector measurement in ANNMixin._measures.
  
      Returns
      -------
      measure: MEASURE_DOT
          Dot product aka. inner product
      """
      return MEASURE_DOT
  
file_path: cornac/cornac/models/cdr/recom_cdr.py
imports: import numpy
  from tqdm.auto import trange
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
  from ...utils import get_rng
  from ...utils.init_utils import xavier_uniform
label_desc: Use image node_id 6 for calling the CDR obj's underlying member method code with example usage: obj.get_vector_measure() and returns: MEASURE_DOT