text: Optimizer that implements the RMSProp algorithm.
  
  See the [paper](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
  
  - - -
  
  #### `tf.train.RMSPropOptimizer.__init__(learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, centered=False, name='RMSProp')` {#RMSPropOptimizer.__init__}
  
  Construct a new RMSProp optimizer.
  
  Note that in dense implement of this algorithm, m_t and v_t will
  update even if g is zero, but in sparse implement, m_t and v_t
  will not update in iterations g is zero.
  
  ##### Args:
  
  
  *  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning rate.
  *  <b>`decay`</b>: Discounting factor for the history/coming gradient
  *  <b>`momentum`</b>: A scalar tensor.
  *  <b>`epsilon`</b>: Small value to avoid zero denominator.
  *  <b>`use_locking`</b>: If True use locks for update operation.
  *  <b>`centered`</b>: If True, gradients are normalized by the estimated variance of
      the gradient; if False, by the uncentered second moment. Setting this to
      True may help with training, but is slightly more expensive in terms of
      computation and memory. Defaults to False.
  *  <b>`name`</b>: Optional name prefix for the operations created when applying
      gradients. Defaults to "RMSProp".
  
  
  