Hands-On Generative Adversarial Networks with PyTorch 1.x

Breaking the classifier with advGAN

The GAN model we'll use for generating adversarial examples is largely borrowed from https://github.com/mathcbc/advGAN_pytorch. Let's create two files named advGAN.py and models.py and put the following code in these files:

1. advGAN.py: Within this file, you will see the following:

import torch.nn as nnimport torchimport numpy as npimport modelsimport torch.nn.functional as Fimport torchvisionimport osdef weights_init(m):    classname = m.__class__.__name__    if classname.find('Conv') != -1:        nn.init.normal_(m.weight.data, 0.0, 0.02)    elif classname.find('BatchNorm') != -1:        nn.init.normal_(m.weight.data, 1.0, 0.02)        nn.init.constant_(m.bias.data, 0)class AdvGAN_Attack:    def __init__(self,                 device,                 model, model_num_labels, ...