class_docstr: Optimizer that implements the Adadelta algorithm.
  
  Adadelta optimization is a stochastic gradient descent method that is based on
  adaptive learning rate per dimension to address two drawbacks:
  
  - The continual decay of learning rates throughout training.
  - The need for a manually selected global learning rate.
  
  Adadelta is a more robust extension of Adagrad that adapts learning rates
  based on a moving window of gradient updates, instead of accumulating all
  past gradients. This way, Adadelta continues learning even when many updates
  have been done. Compared to Adagrad, in the original version of Adadelta you
  don't have to set an initial learning rate. In this version, the initial
  learning rate can be set, as in most other Keras optimizers.
  
  Args:
    learning_rate: Initial value for the learning rate:
      either a floating point value,
      or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
      Defaults to 0.001.
      Note that `Adadelta` tends to benefit from higher initial learning rate
      values compared to other optimizers.
      To match the exact form in the original paper, use 1.0.
    rho: A `Tensor` or a floating point value. The decay rate.
    epsilon: Small floating point value used to maintain numerical stability.
    name: Optional name prefix for the operations created when applying
      gradients.  Defaults to `"Adadelta"`.
    **kwargs: Keyword arguments. Allowed to be one of
      `"clipnorm"` or `"clipvalue"`.
      `"clipnorm"` (float) clips gradients by norm and represents
      the maximum norm of each parameter;
      `"clipvalue"` (float) clips gradient by value and represents the
      maximum absolute value of each parameter.
  
  Reference:
    - [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
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code: def set_weights(self, weights):
      params = self.weights
      # Override set_weights for backward compatibility of Keras V1 optimizer
      # since it does not include iteration at head of the weight list. Set
      # iteration to 0.
      if len(params) == len(weights) + 1:
          weights = [np.array(0)] + weights
      super(Adadelta, self).set_weights(weights)
  
file_path: tensorflow-upstream/tensorflow/python/keras/optimizer_v2/adadelta.py
imports: import numpy
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras import backend_config
  from tensorflow.python.keras.optimizer_v2 import optimizer_v2
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_training_ops
label_desc: Use image node_id 4 for calling the Adadelta obj's underlying member method code with example usage: obj.set_weights(weights) without return types