class_docstr: Optimizer that implements the Proximal Adagrad algorithm.
  
  References:
    Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
      [Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
      ([pdf](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf))
    Efficient Learning using Forward-Backward Splitting:
      [Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting)
      ([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
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docstr: Construct a new ProximalAdagrad optimizer.
  
  Args:
    learning_rate: A `Tensor` or a floating point value.  The learning rate.
    initial_accumulator_value: A floating point value.
      Starting value for the accumulators, must be positive.
    l1_regularization_strength: A float value, must be greater than or
      equal to zero.
    l2_regularization_strength: A float value, must be greater than or
      equal to zero.
    use_locking: If `True` use locks for update operations.
    name: Optional name prefix for the operations created when applying
      gradients.  Defaults to "Adagrad".
  
  Raises:
    ValueError: If the `initial_accumulator_value` is invalid.
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code: def __init__(
      self,
      learning_rate,
      initial_accumulator_value=0.1,
      l1_regularization_strength=0.0,
      l2_regularization_strength=0.0,
      use_locking=False,
      name="ProximalAdagrad",
  ):
      """Construct a new ProximalAdagrad optimizer.
  
      Args:
        learning_rate: A `Tensor` or a floating point value.  The learning rate.
        initial_accumulator_value: A floating point value.
          Starting value for the accumulators, must be positive.
        l1_regularization_strength: A float value, must be greater than or
          equal to zero.
        l2_regularization_strength: A float value, must be greater than or
          equal to zero.
        use_locking: If `True` use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "Adagrad".
  
      Raises:
        ValueError: If the `initial_accumulator_value` is invalid.
      """
      if initial_accumulator_value <= 0.0:
          raise ValueError(
              "initial_accumulator_value must be positive: %s"
              % initial_accumulator_value
          )
      super(ProximalAdagradOptimizer, self).__init__(use_locking, name)
      self._learning_rate = learning_rate
      self._initial_accumulator_value = initial_accumulator_value
      self._l1_regularization_strength = l1_regularization_strength
      self._l2_regularization_strength = l2_regularization_strength
      # Created in Initialize.
      self._l1_regularization_strength_tensor = None
      self._l2_regularization_strength_tensor = None
      self._learning_rate_tensor = None
  
file_path: tensorflow-upstream/tensorflow/python/training/proximal_adagrad.py
imports: from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new ProximalAdagradOptimizer object from inherited base classes: optimizer with example: obj = ProximalAdagradOptimizer(learning_rate, initial_accumulator_value, l1_regularization_strength, l2_regularization_strength, use_locking, name)