class_docstr: The :class:`Dense` class is a fully connected layer.
  
  Parameters
  ----------
  n_units : int
      The number of units of this layer.
  act : activation function
      The activation function of this layer.
  W_init : initializer
      The initializer for the weight matrix.
  b_init : initializer or None
      The initializer for the bias vector. If None, skip biases.
  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 : None or str
      A unique layer name. If None, a unique name will be automatically generated.
  
  Examples
  --------
  With TensorLayer
  
  >>> net = tl.layers.Input([100, 50], name='input')
  >>> dense = tl.layers.Dense(n_units=800, act=tf.nn.relu, in_channels=50, name='dense_1')
  >>> print(dense)
  Dense(n_units=800, relu, in_channels='50', name='dense_1')
  >>> tensor = tl.layers.Dense(n_units=800, act=tf.nn.relu, name='dense_2')(net)
  >>> print(tensor)
  tf.Tensor([...], shape=(100, 800), dtype=float32)
  
  Notes
  -----
  If the layer input has more than two axes, it needs to be flatten by using :class:`Flatten`.
code: def __init__(
      self,
      n_units,
      act=None,
      W_init=tl.initializers.truncated_normal(stddev=0.05),
      b_init=tl.initializers.constant(value=0.0),
      in_channels=None,
      name=None,  # 'dense',
  ):
      super(Dense, self).__init__(name, act=act)
  
      self.n_units = n_units
      self.W_init = W_init
      self.b_init = b_init
      self.in_channels = in_channels
  
      if self.in_channels is not None:
          self.build(self.in_channels)
          self._built = True
  
      logging.info(
          "Dense  %s: %d %s"
          % (
              self.name,
              self.n_units,
              self.act.__name__
              if self.act is not None
              else "No Activation",
          )
      )
  
file_path: TensorLayer/tensorlayer/layers/dense/base_dense.py
imports: import numpy
  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 1 to create a new Dense object from inherited base classes: Layer with example: obj = Dense(n_units, act, W_init, b_init, in_channels, name)