I wanted to train an AlexNet model on cifar with the architecture from:
“Understanding deep learning requires rethinking generalization” 21
Is the following the recommended way to do it:

  
      github.com
  
  
    bearpaw/pytorch-classification/blob/master/models/cifar/alexnet.py 86
'''AlexNet for CIFAR10. FC layers are removed. Paddings are adjusted.
Without BN, the start learning rate should be 0.01
(c) YANG, Wei 
'''
import torch.nn as nn


__all__ = ['alexnet']


class AlexNet(nn.Module):

    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=5),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),


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or is there a standard way to do this in pytorch for cifar?