class_docstr: A basic Adam optimizer that includes "correct" L2 weight decay.
  Copied from the google BERT repo.
  Except that in `apply_gradient` function, we add the support to increment
  the passed global step parameter, to make it more compatible to
  tf.train.Optimizer implementation.
class_docstr_tok: ["A","basic","Adam","optimizer","that","includes","``","correct","''","L2","weight","decay",".","Copied","from","the","google","BERT","repo",".","Except","that","in","`","apply_gradient","`","function",",","we","add","the","support","to","increment","the","passed","global","step","parameter",",","to","make","it","more","compatible","to","tf.train.Optimizer","implementation","."]
docstr: Constructs a AdamWeightDecayOptimizer.
docstr_tok: ["Constructs","a","AdamWeightDecayOptimizer","."]
code: def __init__(
      self,
      learning_rate,
      weight_decay_rate=0.0,
      beta_1=0.9,
      beta_2=0.999,
      epsilon=1e-6,
      exclude_from_weight_decay=None,
      name="AdamWeightDecayOptimizer",
  ):
      """Constructs a AdamWeightDecayOptimizer."""
      super(AdamWeightDecayOptimizer, self).__init__(False, name)
  
      self.learning_rate = learning_rate
      self.weight_decay_rate = weight_decay_rate
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.exclude_from_weight_decay = exclude_from_weight_decay
  
file_path: texar/texar/tf/core/optimization.py
imports: import re
  import tensorflow
  from texar.tf.hyperparams import HParams
  from texar.tf.utils import utils
label_desc: Use image node_id 1 to create a new AdamWeightDecayOptimizer object from inherited base classes: tf.train with example: obj = AdamWeightDecayOptimizer(learning_rate, weight_decay_rate, beta_1, beta_2, epsilon, exclude_from_weight_decay, name)