class_docstr: The :class:`DropconnectDense` class is :class:`Dense` with DropConnect
  behaviour which randomly removes connections between this layer and the previous
  layer according to a keeping probability.
  
  Parameters
  ----------
  keep : float
      The keeping probability.
      The lower the probability it is, the more activations are set to zero.
  n_units : int
      The number of units of this layer.
  act : activation function
      The activation function of this layer.
  W_init : weights initializer
      The initializer for the weight matrix.
  b_init : biases initializer
      The initializer for the bias vector.
  in_channels: int
      The number of channels of the previous layer.
      If None, it will be automatically detected when the layer is forwarded for the first time.
  name : str
      A unique layer name.
  
  Examples
  --------
  >>> net = tl.layers.Input([None, 784], name='input')
  >>> net = tl.layers.DropconnectDense(keep=0.8,
  ...         n_units=800, act=tf.nn.relu, name='relu1')(net)
  >>> net = tl.layers.DropconnectDense(keep=0.5,
  ...         n_units=800, act=tf.nn.relu, name='relu2')(net)
  >>> net = tl.layers.DropconnectDense(keep=0.5,
  ...         n_units=10, name='output')(net)
  
  References
  ----------
  - `Wan, L. (2013). Regularization of neural networks using dropconnect <http://machinelearning.wustl.edu/mlpapers/papers/icml2013_wan13>`__
code: def forward(self, inputs):
      W_dropcon = tf.nn.dropout(self.W, 1 - (self.keep))
      outputs = tf.matmul(inputs, W_dropcon)
      if self.b_init:
          outputs = tf.nn.bias_add(outputs, self.b, name="bias_add")
      if self.act:
          outputs = self.act(outputs)
      return outputs
  
file_path: TensorLayer/tensorlayer/layers/dense/dropconnect.py
imports: import numbers
  import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 4 for calling the DropconnectDense obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs