class_docstr: Implementation of the AMSGrad optimization algorithm.
  
  See: `On the Convergence of Adam and Beyond - [Reddi et al., 2018] <https://openreview.net/pdf?id=ryQu7f-RZ>`__.
  
  Parameters
  ----------
  learning_rate: float
      A Tensor or a floating point value.  The learning rate.
  beta1: float
      A float value or a constant float tensor.
      The exponential decay rate for the 1st moment estimates.
  beta2: float
      A float value or a constant float tensor.
      The exponential decay rate for the 2nd moment estimates.
  epsilon: float
      A small constant for numerical stability.
      This epsilon is "epsilon hat" in the Kingma and Ba paper
      (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
  use_locking: bool
      If True use locks for update operations.
  name: str
      Optional name for the operations created when applying gradients.
      Defaults to "AMSGrad".
docstr: Construct a new Adam optimizer.
docstr_tok: ["Construct","a","new","Adam","optimizer","."]
code: def __init__(
      self,
      learning_rate=0.01,
      beta1=0.9,
      beta2=0.99,
      epsilon=1e-8,
      use_locking=False,
      name="AMSGrad",
  ):
      """Construct a new Adam optimizer."""
      super(AMSGrad, self).__init__(use_locking, name)
      self._lr = learning_rate
      self._beta1 = beta1
      self._beta2 = beta2
      self._epsilon = epsilon
  
      self._lr_t = None
      self._beta1_t = None
      self._beta2_t = None
      self._epsilon_t = None
  
      self._beta1_power = None
      self._beta2_power = None
  
file_path: TensorLayer/tensorlayer/optimizers/amsgrad.py
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import control_flow_ops, math_ops, resource_variable_ops, state_ops, variable_scope
  from tensorflow.python.training import optimizer
label_desc: Use image node_id 1 to create a new AMSGrad object from inherited base classes: optimizer with example: obj = AMSGrad(learning_rate, beta1, beta2, epsilon, use_locking, name)