class_docstr: AdaGrad optimizer.
  
  See: http://jmlr.org/papers/v12/duchi11a.html
  
  Args:
      lr (float): Learning rate.
      eps (float): Small value for the numerical stability.
class_docstr_tok: ["AdaGrad","optimizer",".","See",":","http",":","\/\/jmlr.org\/papers\/v12\/duchi11a.html","Args",":","lr","(","float",")",":","Learning","rate",".","eps","(","float",")",":","Small","value","for","the","numerical","stability","."]
code: def __init__(
      self, lr=_default_hyperparam.lr, eps=_default_hyperparam.eps
  ):
      super(AdaGrad, self).__init__()
      self.hyperparam.lr = lr
      self.hyperparam.eps = eps
  
file_path: chainer/chainer/optimizers/ada_grad.py
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 1 to create a new AdaGrad object from inherited base classes: optimizer with example: obj = AdaGrad(lr, eps)