class_docstr: Adam.
  
  The default value of 1e-8 for epsilon might not be a good default in
  general. For example, when training an Inception network on ImageNet a
  current good choice is 1.0 or 0.1.
  
  Examples:
      ```python
      # With TFLearn estimators
      adam = Adam(learning_rate=0.001, beta1=0.99)
      regression = regression(net, optimizer=adam)
  
      # Without TFLearn estimators (returns tf.Optimizer)
      adam = Adam(learning_rate=0.01).get_tensor()
  
      ```
  
  Arguments:
      learning_rate: `float`. Learning rate.
      beta1: `float`. The exponential decay rate for the 1st moment
          estimates.
      beta2: `float`. The exponential decay rate for the 2nd moment
          estimates.
      epsilon: `float`. A small constant for numerical stability.
      use_locking: `bool`. If True use locks for update operation.
      name: `str`. Optional name prefix for the operations created when
          applying gradients. Defaults to "Adam".
  
  References:
      Adam: A Method for Stochastic Optimization. Diederik Kingma,
      Jimmy Ba. ICLR 2015.
  
  Links:
      [Paper](http://arxiv.org/pdf/1412.6980v8.pdf)
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code: def __init__(
      self,
      learning_rate=0.001,
      beta1=0.9,
      beta2=0.999,
      epsilon=1e-8,
      use_locking=False,
      name="Adam",
  ):
      super(Adam, self).__init__(learning_rate, use_locking, name)
      self.beta1 = beta1
      self.beta2 = beta2
      self.epsilon = epsilon
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 1 to create a new Adam object from inherited base classes: Optimizer with example: obj = Adam(learning_rate, beta1, beta2, epsilon, use_locking, name)