torch.argsort(output, 1)[0][-num_candidates:]
时间: 2023-03-14 07:08:02 浏览: 63
这里的 num_candidates 指定了返回的最大候选结果数量,torch.argsort(output, 1)[0][-num_candidates:] 就是根据给定的输出(output),对其进行排序,并返回最后 num_candidates 个结果。
相关问题
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()
```
class sampler (Sampler): def u (self, train size, batch_ size): num_ data = train_ size self .num_ per batch = int(num_ data 1 batch_ size) self .batch size = batch_ size self .range = torch.arange(0, batch_ size) .view(1, batch_ size).long() self.leftover flag = False if num_ data % batch_ size: self.leftover = torch.arange(self .num_ per batch * batch_ size, num_ data) . long( )self.leftover flag = True def_ iter_a (self): rand_ num = torch.randperm(self .num_ per_ batch) .view(-1, 1) * self .batch size self .rand_ num = rand_ num. expand(self .num_ per_ batch, self .batch_size) + self .range self .rand num_view = self .rand_ num. view(-1) if self.leftover_ flag: self .rand_ num_ view = torch.cat((self.rand_ num_ view, self.leftover), 0 return iter(self .rand_ num_ view) def Len_ (self): return num_ data 分析上述代码中的错误
代码中的错误在于使用了错误的语法和变量命名。以下是需要修正的错误:
1. 函数 `u()` 的定义中,应该将 `train size` 改为 `train_size`。同样的,`batch_ size` 改为 `batch_size`。
2. 在 `u()` 函数中,`int(num_ data 1 batch_ size)` 的语法是错误的,应该改为 `int(num_data / batch_size)`。
3. 在 `u()` 函数中,`self.leftover flag` 的语法也是错误的,应该改为 `self.leftover_flag`。
4. 在 `u()` 函数中,`long( )self.leftover flag = True` 的语法是错误的,应该将其分成两行,并且添加缺少的冒号。
5. 在 `def_ iter_a()` 函数中,`rand_ num = torch.randperm(self .num_ per_ batch) .view(-1, 1) * self .batch size` 的语法是错误的,应该将 `self .batch size` 改为 `self.batch_size`。
6. 在 `def_ iter_a()` 函数中,`self .rand num_view = self .rand_ num. view(-1)` 的语法是错误的,应该将 `self .rand num_view = self .rand_ num. view(-1)` 改为 `self.rand_num_view = self.rand_num.view(-1)`。
7. 在 `def_ iter_a()` 函数中,`if self.leftover_ flag:` 的语法是错误的,应该将其改为 `if self.leftover_flag:`。
8. 在 `def_ iter_a()` 函数中,`self .rand_ num_ view = torch.cat((self.rand_ num_ view, self.leftover), 0` 的语法是错误的,应该将其改为 `self.rand_num_view = torch.cat((self.rand_num_view, self.leftover), 0)`。
9. 在 `def Len_()` 函数中,`return num_ data` 的语法是错误的,应该将其改为 `return num_data`。
修正以上错误后的代码如下:
```python
class Sampler:
def u(self, train_size, batch_size):
num_data = train_size
self.num_per_batch = int(num_data / batch_size)
self.batch_size = batch_size
self.range = torch.arange(0, batch_size).view(1, batch_size).long()
self.leftover_flag = False
if num_data % batch_size:
self.leftover = torch.arange(self.num_per_batch * batch_size, num_data).long()
self.leftover_flag = True
def iter_a(self):
rand_num = torch.randperm(self.num_per_batch).view(-1, 1) * self.batch_size
self.rand_num = rand_num.expand(self.num_per_batch, self.batch_size) + self.range
self.rand_num_view = self.rand_num.view(-1)
if self.leftover_flag:
self.rand_num_view = torch.cat((self.rand_num_view, self.leftover), 0)
return iter(self.rand_num_view)
def Len(self):
return num_data
```
以上是修正后的代码。修正后代码中的变量命名和语法都是正确的。