# Create tensors. x = torch.tensor(1., requires_grad=True) w = torch.tensor(2., requires_grad=True) b = torch.tensor(3., requires_grad=True) # Build a computational graph. y = w * x + b # y = 2 * x + 3 # Compute gradients. y.backward() # Print out the gradients. print(x.grad) # x.grad = 2 print(w.grad) # w.grad = 1 print(b.grad) # b.grad = 1

这段代码使用 PyTorch 创建了三个张量 x、w 和 b，并将它们的 `requires_grad` 属性设置为 True，以便计算梯度。然后，使用这些张量构建了一个计算图，其中 y 是 x 和 w 的乘积加上 b。接下来，使用 `y.backward()` 计算了 y 对每个需要梯度的张量的梯度。最后，使用 `print()` 打印了每个张量的梯度值。在这个例子中，x 的梯度值是 2，w 和 b 的梯度值都是 1。这是因为 y 对 w 和 b 的梯度都是 1，而 y 对 x 的梯度是 w，即 2。

相关问题

def calc_gradient_penalty(self, netD, real_data, fake_data): alpha = torch.rand(1, 1) alpha = alpha.expand(real_data.size()) alpha = alpha.cuda() interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = interpolates.cuda() interpolates = Variable(interpolates, requires_grad=True) disc_interpolates, s = netD.forward(interpolates) s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda() gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \ (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) return gradient_penalty，上述代码中(((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA)提示出现错误：RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.

这个错误提示是因为在计算梯度时，有些张量（tensors）没有被使用到，可能是因为你在计算梯度时定义了一些不必要的变量，或者是你在定义某些变量时出现了问题。根据错误提示建议你设置 allow_unused=True，这样可以忽略未使用的张量而不报错。你可以这样修改代码：

gradients2 = autograd.grad(outputs=s, inputs=interpolates,
                           grad_outputs=torch.ones(s.size()).cuda(),
                           create_graph=True, retain_graph=True,
                           only_inputs=True, allow_unused=True)[0]

改为：

gradients2 = autograd.grad(outputs=s, inputs=interpolates,
                           grad_outputs=torch.ones(s.size()).cuda(),
                           create_graph=True, retain_graph=True,
                           only_inputs=True, allow_unused=True)[0]
if gradients2 is None:
    return None

这样就可以避免报错了。

with torch.no_grad()

The `torch.no_grad()` context manager is used to turn off PyTorch's gradient calculation feature. It is often used during inference or evaluation when we do not need to compute gradients, and doing so would only waste computation time and memory.

When we wrap a block of code with `torch.no_grad()`, all operations inside that block will have their `requires_grad` flag set to `False`, even if they were defined with `requires_grad=True`. This means that gradients will not be computed for those operations and the associated tensors.

Here is an example of using `torch.no_grad()`:

import torch

# Define a tensor with requires_grad=True
x = torch.tensor([1., 2., 3.], requires_grad=True)

# Wrap a block of code with torch.no_grad()
with torch.no_grad():
    # Do some operations with x
    y = x * 2
    z = y.mean()

# Gradients will not be computed for y and z
print(y.grad_fn)  # None
print(z.grad_fn)  # None

# Gradients will still be computed for x
z.backward()
print(x.grad)  # tensor([0.6667, 0.6667, 0.6667])