readme: # vgg19_bn
  Implementation of VGG proposed in [Very Deep Convolutional Networks For
  Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
  
   ``` python
   VGG.vgg11()
   VGG.vgg13()
   VGG.vgg16()
   VGG.vgg19()
   VGG.vgg11_bn()
   VGG.vgg13_bn()
   VGG.vgg16_bn()
   VGG.vgg19_bn()
   ```
  
   Please be aware that the [bn]{.title-ref} models uses BatchNorm but
   they are very old and people back then don\'t know the bias is
   superfluous in a conv followed by a batchnorm.
  
   Examples:
  
    ``` python
    # change activation
    VGG.vgg11(activation = nn.SELU)
    # change number of classes (default is 1000 )
    VGG.vgg11(n_classes=100)
    # pass a different block
    from nn.models.classification.senet import SENetBasicBlock
    VGG.vgg11(block=SENetBasicBlock)
    # store the features tensor after every block
    ```
  
   