torch.randn(iter_adv.shape)

This line of code generates a tensor of random numbers from a standard normal distribution (mean=0, standard deviation=1) with the same shape as the tensor `iter_adv`. The `torch.randn` function is part of the PyTorch library and is commonly used for initializing weights or generating noise in deep learning models.

相关问题

torch.randn_like（）

`torch.randn_like()` 是 PyTorch（一个开源的机器学习库）中的一个函数，它用于生成一个新的张量，其形状和数据类型与给定的输入张量 (`like` 参数) 完全相同，但元素值是随机从标准正态分布（mean=0, std=1）中抽取的。这个函数在深度学习中常用于初始化模型参数或创建随机噪声输入。

使用示例：

import torch

# 假设你有一个张量 x
x = torch.tensor([[1, 2], [3, 4]])
# 使用 torch.randn_like(x) 生成与 x 同样大小和类型的随机张量
random_tensor = torch.randn_like(x)

def adversarial(x, model, loss_func, c=1e-4, kappa=0, num_iter=100, lr=0.01): """ Create adversarial examples using CW algorithm Args: - x: input image - model: the neural network model - loss_func: the loss function to use - c: the weight for the L2 regularization term (default=1e-4) - kappa: the confidence parameter (default=0) - num_iter: number of iterations for the algorithm (default=100) - lr: learning rate for the optimization (default=0.01) Returns: - x_adv: adversarial example """ x_adv = x.clone().detach().requires_grad_(True) for i in range(num_iter): output = model(x_adv) loss = loss_func(output, torch.tensor([kappa]), x, x_adv, c) model.zero_grad() loss.backward() with torch.no_grad(): x_adv += lr * x_adv.grad x_adv = torch.max(torch.min(x_adv, x + 0.35), x - 0.35) x_adv = torch.clamp(x_adv, 0, 1) x_adv.requires_grad_() return x_adv.detach()上述代码出现TypeError: ce_loss() takes 2 positional arguments but 5 were given错误，请改正

根据代码，`loss_func`应该是一个函数，且它的输入参数应该是模型的输出 `output`和目标标签 `target`。因此，需要修改 `loss_func` 函数的输入参数，将 `x`、`x_adv` 和 `c` 作为函数的参数，而不是在函数调用时传入。修改后的代码如下：

def adversarial(x, model, loss_func, c=1e-4, kappa=0, num_iter=100, lr=0.01):
    """Create adversarial examples using CW algorithm
    Args:
    - x: input image
    - model: the neural network model
    - loss_func: the loss function to use
    - c: the weight for the L2 regularization term (default=1e-4)
    - kappa: the confidence parameter (default=0)
    - num_iter: number of iterations for the algorithm (default=100)
    - lr: learning rate for the optimization (default=0.01)
    Returns:
    - x_adv: adversarial example
    """
    x_adv = x.clone().detach().requires_grad_(True)
    for i in range(num_iter):
        output = model(x_adv)
        loss = loss_func(output, kappa, target=x, adversary=x_adv, c=c)
        model.zero_grad()
        loss.backward()
        with torch.no_grad():
            x_adv += lr * x_adv.grad
            x_adv = torch.max(torch.min(x_adv, x + 0.35), x - 0.35)
            x_adv = torch.clamp(x_adv, 0, 1)
            x_adv.requires_grad_()
    return x_adv.detach()