import torch
from torch.autograd import Variable
x = Variable(torch.FloatTensor([11.2]), requires_grad=True)
y = 2 * x
print(x)
print(y)
print(x.data)
print(y.data)
print(x.grad_fn)
print(y.grad_fn)
y.backward() # Calculates the gradients
print(x.grad)
print(y.grad)
错误
C:\Users\donhu\AppData\Local\Temp\ipykernel_9572\106071707.py:2: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten\src\ATen/core/TensorBody.h:485.)
print(y.grad)源代码https://github.com/donhuvy/Deep-learning-with-PyTorch-video/blob/master/1.5.variables.ipynb
如何修复？
1条答案
按热度按时间n7taea2i1#
在调用
y.backward()之前先调用y.retain_grad()。原因是默认情况下PyTorch只为叶子变量（不是运算结果的变量）填充
.grad，在我们的例子中是x，为了确保.grad也为非叶子变量填充，比如y，我们需要调用它们的.retain_grad()方法。同样值得注意的是，这是一个警告，而不是错误。