class_docstr: *** Adaptive Moment Estimation (ADAM) *** 
  
  Computes adaptive learning rates for each parameter. Has an EWMA of past gradients and squared gradients.
code: def __init__(
      self,
      starting_parameters,
      starting_variance,
      learning_rate,
      ewma_1,
      ewma_2,
  ):
      self.parameters = starting_parameters
      self.f_gradient = 0.0
      self.variance = starting_variance
      self.learning_rate = learning_rate
      self.ewma_1 = ewma_1
      self.ewma_2 = ewma_2
  
      self.epsilon = np.power(10.0, -8)
      self.t = 1
  
file_path: pyflux/pyflux/inference/stoch_optim.py
imports: import numpy
label_desc: Use image node_id 1 to create a new ADAM object from inherited base classes: object with example: obj = ADAM(starting_parameters, starting_variance, learning_rate, ewma_1, ewma_2)