class_docstr: Network-in-Network example model.
code: def __init__(self):
      super(NIN, self).__init__()
      conv_init = I.HeNormal()  # MSRA scaling
  
      with self.init_scope():
          self.mlpconv1 = L.MLPConvolution2D(
              None, (96, 96, 96), 11, stride=4, conv_init=conv_init
          )
          self.mlpconv2 = L.MLPConvolution2D(
              None, (256, 256, 256), 5, pad=2, conv_init=conv_init
          )
          self.mlpconv3 = L.MLPConvolution2D(
              None, (384, 384, 384), 3, pad=1, conv_init=conv_init
          )
          self.mlpconv4 = L.MLPConvolution2D(
              None, (1024, 1024, 1000), 3, pad=1, conv_init=conv_init
          )
  
file_path: chainer/examples/imagenet/nin.py
imports: import chainer
  import chainer.functions
  import chainer.initializers
  import chainer.links
label_desc: Use image node_id 1 to create a new NIN object from inherited base classes: chainer with example: obj = NIN()