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  • How to get gradients with respect to the inputs in pytorch

    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]]))
            
        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
    

      

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  • 原文地址:https://www.cnblogs.com/cxxszz/p/8974640.html
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