class_docstr: Base (Stochastic) gradient descent optimizer
  
  Parameters
  ----------
  params : list, length = len(coefs_) + len(intercepts_)
      The concatenated list containing coefs_ and intercepts_ in MLP model.
      Used for initializing velocities and updating params
  
  learning_rate_init : float, optional, default 0.1
      The initial learning rate used. It controls the step-size in updating
      the weights
  
  Attributes
  ----------
  learning_rate : float
      the current learning rate
class_docstr_tok: ["Base","(","Stochastic",")","gradient","descent","optimizer","Parameters","--","--","--","--","--","params",":","list",",","length","=","len","(","coefs_",")","+","len","(","intercepts_",")","The","concatenated","list","containing","coefs_","and","intercepts_","in","MLP","model",".","Used","for","initializing","velocities","and","updating","params","learning_rate_init",":","float",",","optional",",","default","0.1","The","initial","learning","rate","used",".","It","controls","the","step-size","in","updating","the","weights","Attributes","--","--","--","--","--","learning_rate",":","float","the","current","learning","rate"]
code: def __init__(self, params, learning_rate_init=0.1):
      self.params = [param for param in params]
      self.learning_rate_init = learning_rate_init
      self.learning_rate = float(learning_rate_init)
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_stochastic_optimizers.py
imports: import numpy
label_desc: Use image node_id 1 to create a new BaseOptimizer object from inherited base classes: object with example: obj = BaseOptimizer(params, learning_rate_init)