class_docstr: VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
  <https://arxiv.org/abs/1409.1556>`_ paper.
  
  Parameters
  ----------
  layers : list of int
      Numbers of layers in each feature block.
  filters : list of int
      Numbers of filters in each feature block. List length should match the layers.
  classes : int, default 1000
      Number of classification classes.
  batch_norm : bool, default False
      Use batch normalization.
class_docstr_tok: ["VGG","model","from","the","`","``","Very","Deep","Convolutional","Networks","for","Large-Scale","Image","Recognition","''","<","https",":","\/\/arxiv.org\/abs\/1409.1556",">","`","_","paper",".","Parameters","--","--","--","--","--","layers",":","list","of","int","Numbers","of","layers","in","each","feature","block",".","filters",":","list","of","int","Numbers","of","filters","in","each","feature","block",".","List","length","should","match","the","layers",".","classes",":","int",",","default","1000","Number","of","classification","classes",".","batch_norm",":","bool",",","default","False","Use","batch","normalization","."]
code: def __init__(
      self, layers, filters, classes=1000, batch_norm=False, **kwargs
  ):
      super(VGG, self).__init__(**kwargs)
      assert len(layers) == len(filters)
      with self.name_scope():
          self.features = self._make_features(
              layers, filters, batch_norm
          )
          self.features.add(
              nn.Dense(
                  4096,
                  activation="relu",
                  weight_initializer="normal",
                  bias_initializer="zeros",
              )
          )
          self.features.add(nn.Dropout(rate=0.5))
          self.features.add(
              nn.Dense(
                  4096,
                  activation="relu",
                  weight_initializer="normal",
                  bias_initializer="zeros",
              )
          )
          self.features.add(nn.Dropout(rate=0.5))
          self.output = nn.Dense(
              classes,
              weight_initializer="normal",
              bias_initializer="zeros",
          )
  
file_path: gluon-cv/gluoncv/model_zoo/vgg.py
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.initializer import Xavier
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new VGG object from inherited base classes: HybridBlock with example: obj = VGG(layers, filters, classes, batch_norm)