class_docstr: Fully-connected layer with simplified dropconnect regularization.
  
  Notice:
  This implementation cannot be used for reproduction of the paper.
  There is a difference between the current implementation and the
  original one.
  The original version uses sampling with gaussian distribution before
  passing activation function, whereas the current implementation averages
  before activation.
  
  Args:
      in_size (int): Dimension of input vectors. If ``None``, parameter
          initialization will be deferred until the first forward data pass
          at which time the size will be determined.
      out_size (int): Dimension of output vectors.
      nobias (bool): If ``True``, then this link does not use the bias term.
      initialW (:ref:`initializer <initializer>`): Initializer to
          initialize the weight. When it is :class:`numpy.ndarray`,
          its ``ndim`` should be 3.
      initial_bias (:ref:`initializer <initializer>`): Initializer to
          initialize the bias. If ``None``, the bias will be initialized to
          zero. When it is :class:`numpy.ndarray`, its ``ndim`` should be 2.
  
  Attributes:
      W (~chainer.Variable): Weight parameter.
      b (~chainer.Variable): Bias parameter.
  
  .. seealso:: :func:`~chainer.functions.simplified_dropconnect`
  
  .. seealso::
      Li, W., Matthew Z., Sixin Z., Yann L., Rob F. (2013).
      Regularization of Neural Network using DropConnect.
      International Conference on Machine Learning.
      `URL <https://cs.nyu.edu/~wanli/dropc/>`_
code: def __init__(
      self,
      in_size,
      out_size,
      ratio=0.5,
      nobias=False,
      initialW=None,
      initial_bias=None,
  ):
      super(SimplifiedDropconnect, self).__init__()
  
      self.out_size = out_size
      self.ratio = ratio
  
      if initialW is None:
          initialW = initializers.HeNormal(1.0 / numpy.sqrt(2))
  
      with self.init_scope():
          W_initializer = initializers._get_initializer(initialW)
          self.W = variable.Parameter(W_initializer)
          if in_size is not None:
              self._initialize_params(in_size)
  
          if nobias:
              self.b = None
          else:
              if initial_bias is None:
                  initial_bias = initializers.Constant(0)
              bias_initializer = initializers._get_initializer(
                  initial_bias
              )
              self.b = variable.Parameter(bias_initializer, out_size)
  
file_path: chainer/chainer/links/activation/simplified_dropconnect.py
imports: import numpy
  from chainer.functions.noise import simplified_dropconnect
  from chainer import initializers
  from chainer import link
  from chainer import variable
label_desc: Use image node_id 1 to create a new SimplifiedDropconnect object from inherited base classes: link with example: obj = SimplifiedDropconnect(in_size, out_size, ratio, nobias, initialW, initial_bias)