class_docstr: A VGG-style network for very small images.
  
  This model is based on the VGG-style model from
  http://torch.ch/blog/2015/07/30/cifar.html
  which is based on the network architecture from the paper:
  https://arxiv.org/pdf/1409.1556v6.pdf
  
  This model is intended to be used with either RGB or greyscale input
  images that are of size 32x32 pixels, such as those in the CIFAR10
  and CIFAR100 datasets.
  
  On CIFAR10, it achieves approximately 89% accuracy on the test set with
  no data augmentation.
  
  On CIFAR100, it achieves approximately 63% accuracy on the test set with
  no data augmentation.
  
  Args:
      class_labels (int): The number of class labels.
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code: def __init__(self, class_labels=10):
      super(VGG, self).__init__()
      with self.init_scope():
          self.block1_1 = Block(64, 3)
          self.block1_2 = Block(64, 3)
          self.block2_1 = Block(128, 3)
          self.block2_2 = Block(128, 3)
          self.block3_1 = Block(256, 3)
          self.block3_2 = Block(256, 3)
          self.block3_3 = Block(256, 3)
          self.block4_1 = Block(512, 3)
          self.block4_2 = Block(512, 3)
          self.block4_3 = Block(512, 3)
          self.block5_1 = Block(512, 3)
          self.block5_2 = Block(512, 3)
          self.block5_3 = Block(512, 3)
          self.fc1 = L.Linear(None, 512, nobias=True)
          self.bn_fc1 = L.BatchNormalization(512)
          self.fc2 = L.Linear(None, class_labels, nobias=True)
  
file_path: chainer/examples/cifar/models/VGG.py
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new VGG object from inherited base classes: chainer with example: obj = VGG(class_labels)