class_docstr: Optimizer that implements the Adam algorithm.
  
  Adam optimization is a stochastic gradient descent method that is based on
  adaptive estimation of first-order and second-order moments.
  
  According to
  [Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
  the method is "*computationally
  efficient, has little memory requirement, invariant to diagonal rescaling of
  gradients, and is well suited for problems that are large in terms of
  data/parameters*".
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.001`.
      beta_1: A float value or a constant float tensor, or a callable
          that takes no arguments and returns the actual value to use. The
          exponential decay rate for the 1st moment estimates. Defaults to
          `0.9`.
      beta_2: A float value or a constant float tensor, or a callable
          that takes no arguments and returns the actual value to use. The
          exponential decay rate for the 2nd moment estimates. Defaults to
          `0.999`.
      epsilon: 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. Defaults
          to `1e-7`.
      amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm
          from the paper "On the Convergence of Adam and beyond". Defaults
          to `False`.
      {{base_optimizer_keyword_args}}
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "beta_1": self.beta_1,
              "beta_2": self.beta_2,
              "epsilon": self.epsilon,
              "amsgrad": self.amsgrad,
          }
      )
      return config
  
file_path: keras/keras/optimizers/adam.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.optimizers import optimizer
label_desc: Use image node_id 4 for calling the Adam obj's underlying member method code with example usage: obj.get_config() and returns: config