message: More documentation for keras adagrad and adadelta
diff: @@ -316,14 +316,21 @@ class RMSprop(Optimizer):
  class Adagrad(Optimizer):
  """Adagrad optimizer.
  + Adagrad is an optimizer with parameter-specific learning rates,
  + which are adapted relative to how frequently a parameter gets
  + updated during training. The more updates a parameter receives,
  + the smaller the updates.
  +
  It is recommended to leave the parameters of this optimizer
  at their default values.
  - Arguments:
  - lr: float >= 0. Learning rate.
  + # Arguments
  + lr: float >= 0. Initial learning rate.
  epsilon: float >= 0. If `None`, defaults to `K.epsilon()`.
  decay: float >= 0. Learning rate decay over each update.
  + # References
  + - [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
  """
  def __init__(self, lr=0.01, epsilon=None, decay=0., **kwargs):
  @@ -376,16 +383,27 @@ class Adagrad(Optimizer):
  class Adadelta(Optimizer):
  """Adadelta optimizer.
  + 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, initial learning rate and decay factor can
  + be set, as in most other Keras optimizers.
  +
  It is recommended to leave the parameters of this optimizer
  at their default values.
  - Arguments:
  - lr: float >= 0. Learning rate.
  + # Arguments
  + lr: float >= 0. Initial learning rate, defaults to 1.
  It is recommended to leave it at the default value.
  - rho: float >= 0.
  + rho: float >= 0. Adadelta decay factor, corresponding to fraction of
  + gradient to keep at each time step.
  epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
  - decay: float >= 0. Learning rate decay over each update.
  + decay: float >= 0. Initial learning rate decay.
  + # References
  + - [Adadelta - an adaptive learning rate method](http://arxiv.org/abs/1212.5701)
  """
  def __init__(self, lr=1.0, rho=0.95, epsilon=None, decay=0., **kwargs):
  