This is one way to find adversarial examples of CNN.
The boilerplate:
import torch from torch.autograd import Variable import torch.nn as nn import torch.optim as optim import numpy as np
Define a simple network:
class lolnet(nn.Module):
def __init__(self):
super(lolnet,self).__init__()
self.a=nn.Linear(in_features=1,out_features=1,bias=False)
self.a.weight = nn.Parameter(torch.FloatTensor([[0.6]]))
self.b=nn.Linear(in_features=1,out_features=1,bias=False)
self.b.weight=nn.Parameter(torch.FloatTensor([[0.6]]))
self.a.requires_grad=False
self.b.requires_grad=False
def forward(self, inputs):
return self.b(
self.a(inputs)
)
The inputs
inputs=np.array([[5]]) inputs=torch.from_numpy(inputs).float() inputs=Variable(inputs) inputs.requires_grad=True net=lolnet()
The optimizer
opx=optim.SGD(
params=[
{"params":inputs}
],lr=0.5
)
The optimization process
for i in range(50):
x=net(inputs)
loss=(x-1)**2
opx.zero_grad()
loss.backward()
opx.step()
print(net.a.weight.data.numpy()[0][0],inputs.data.numpy()[0][0],loss.data.numpy()[0][0])
The results are as below:
0.6 4.712 0.6400001 0.6 4.4613247 0.4848616 0.6 4.243137 0.36732942 0.6 4.0532265 0.27828723 0.6 3.8879282 0.2108294 0.6 3.7440526 0.15972354 0.6 3.6188233 0.1210059 0.6 3.5098238 0.09167358 0.6 3.4149506 0.069451585 0.6 3.332373 0.052616227 0.6 3.2604973 0.039861854 0.6 3.1979368 0.030199187 0.6 3.143484 0.022878764 0.6 3.0960886 0.017332876 0.6 3.0548356 0.013131317 0.6 3.0189288 0.00994824 0.6 2.9876754 0.0075367615 0.6 2.9604726 0.005709796 0.6 2.9367952 0.0043257284 0.6 2.9161866 0.003277142 0.6 2.8982487 0.0024827516 0.6 2.8826356 0.0018809267 0.6 2.869046 0.001424982 0.6 2.8572176 0.0010795629 0.6 2.8469222 0.0008178701 0.6 2.837961 0.00061961624 0.6 2.830161 0.00046941772 0.6 2.8233721 0.000355627 0.6 2.8174632 0.0002694209 0.6 2.81232 0.00020411481 0.6 2.8078432 0.0001546371 0.6 2.8039467 0.00011715048 0.6 2.8005552 8.875507e-05 0.6 2.7976031 6.724081e-05 0.6 2.7950337 5.093933e-05 0.6 2.7927973 3.8591857e-05 0.6 2.7908509 2.9236677e-05 0.6 2.7891567 2.2150038e-05 0.6 2.7876818 1.6781378e-05 0.6 2.7863982 1.2713146e-05 0.6 2.785281 9.631679e-06 0.6 2.7843084 7.296927e-06 0.6 2.783462 5.527976e-06 0.6 2.7827253 4.1880226e-06 0.6 2.782084 3.1727632e-06 0.6 2.7815259 2.4034823e-06 0.6 2.78104 1.821013e-06 0.6 2.7806172 1.3793326e-06 0.6 2.780249 1.044933e-06 0.6 2.7799287 7.9170513e-07 Process finished with exit code 0