Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Fix the bug in this code. Rewrite the entire code exactly as-is but just fix any bugs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data + alpha * perturbation_grad.sign() perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=True) target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
To fix the bug in the code, the key issue was the direction of the gradient step in the PGD function. The original code added the gradient's sign, which is suitable for an untargeted attack (maximizing the loss). However, given the target provided, it's clear the intention is a targeted attack (minimizing the loss to reach the target). Thus, the gradient step should subtract the gradient's sign. Additionally, the input tensor does not need gradients, so setting `requires_grad=False` optimizes the computation. Here is the corrected code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data - alpha * perturbation_grad.sign() # Fixed sign here perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=False) # Set requires_grad to False target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data - alpha * perturbation_grad.sign() # Fixed sign here perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=False) # Set requires_grad to False target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch import torch.nn as nn import torch.optim as optim class SimpleNet(nn.Module): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Linear(1, 1) def forward(self, x): return self.fc(x) def PGD(model, input, target, loss_fn, epsilon, alpha, num_iter): perturbation = torch.zeros_like(input, requires_grad=True) for i in range(num_iter): output = model(input + perturbation) loss = loss_fn(output, target) loss.backward() # Update perturbation perturbation_grad = perturbation.grad.data perturbation.data = perturbation.data - alpha * perturbation_grad.sign() # Fixed sign here perturbation.data = torch.clamp(perturbation.data, -epsilon, epsilon) perturbation.grad.data.zero_() return input + perturbation model = SimpleNet() input = torch.tensor([[1.0]], requires_grad=False) # Set requires_grad to False target = torch.tensor([[2.0]]) loss_fn = nn.MSELoss() epsilon = 0.1 alpha = 0.01 num_iter = 2 adversarial_input = PGD(model, input, target, loss_fn, epsilon, alpha, num_iter) print("Resulting advex", adversarial_input) |
Resulting advex tensor([[1.0200]], grad_fn=<AddBackward0>)
Testing if the previous output contains the string grad_fn=<Add
: True
Testing if the previous output contains the string tensor
: True