message: Adadelta use case clarification.
  Contributed from by 	abhilash1910
diff: @@ -45,6 +45,19 @@ class Adadelta(optimizer_v2.OptimizerV2):
  don't have to set an initial learning rate. In this version, initial
  learning rate can be set, as in most other Keras optimizers.
  + According to section 4.3 ("Effective Learning rates"), near the end of
  + training step sizes converge to 1 which is effectively a high learning
  + rate which would cause divergence. This occurs only near the end of the
  + training as gradients and step sizes are small, and the epsilon constant
  + in the numerator and denominator dominate past gradients and parameter
  + updates which converge the learning rate to 1.
  +
  + According to section 4.4("Speech Data"),where a large neural network with
  + 4 hidden layers was trained on a corpus of US English data, ADADELTA was
  + used with 100 network replicas.The epsilon used is 1e-6 with rho=0.95
  + which converged faster than ADAGRAD, by the following construction:
  + def __init__(self, lr=1.0, rho=0.95, epsilon=1e-6, decay=0., **kwargs):
  +
  Args:
  learning_rate: A `Tensor`, floating point value, or a schedule that is a
  `tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
  