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"]
docstr: Update parameters with given gradients
  
  Parameters
  ----------
  grads : list, length = len(params)
      Containing gradients with respect to coefs_ and intercepts_ in MLP
      model. So length should be aligned with params
docstr_tok: ["Update","parameters","with","given","gradients","Parameters","--","--","--","--","--","grads",":","list",",","length","=","len","(","params",")","Containing","gradients","with","respect","to","coefs_","and","intercepts_","in","MLP","model",".","So","length","should","be","aligned","with","params"]
code: def update_params(self, grads):
      """Update parameters with given gradients
  
      Parameters
      ----------
      grads : list, length = len(params)
          Containing gradients with respect to coefs_ and intercepts_ in MLP
          model. So length should be aligned with params
      """
      updates = self._get_updates(grads)
      for param, update in zip(self.params, updates):
          param += update
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_stochastic_optimizers.py
imports: import numpy
label_desc: Use image node_id 2 for calling the BaseOptimizer obj's underlying member method code with example usage: obj.update_params(grads) without return types