Pytorch 遇到运行时错误：梯度计算所需的变量之一已被就地操作修改
调用.backward（）时出现以下错误： 遇到运行时错误：梯度计算所需的变量之一已被就地操作修改Pytorch 遇到运行时错误：梯度计算所需的变量之一已被就地操作修改,pytorch,backpropagation,autograd,Pytorch,Backpropagation,Autograd,调用.backward（）时出现以下错误： 遇到运行时错误：梯度计算所需的变量之一已被就地操作修改 A = tensor([[[0, 0, 0, 0, 0],
A = tensor([[[0, 0, 0, 0, 0],
[0, 0.1, 0, 0, 0],
[0, 0, 0.2, 0, 0],
[0, 0, 0, 0.3, 0]],
[[0, 0, 0, 0, 0],
[0, 0.4, 0, 0, 0],
[0, 0, 0.5, 0, 0],
[0, 0, 0, 0.6, 0]],
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 2.4, 0, 0, 0],
[0, 0, 0, 0, 0]]])
for i, j, k in zip(X, Y, Z):
A[:, i, j] = A[:, i, j] + k
请帮忙 在评论之后，我对你想要实现的目标有点困惑。您提供的代码使用您在注释中提供的维度时给了我一个错误
Traceback (most recent call last):
A[:, i, j] = A[:, i, j] + k
RuntimeError: The size of tensor a (32) must match the size of tensor b (200) at non-singleton dimension 0
XYZXYZAAXYZAXYZX = tensor([[1, 2, 3],
[1, 2, 3],
[2, 2, 2]])
Y = tensor([[1, 2, 3],
[1, 2, 3],
[1, 1, 1]])
Z = tensor([[[0.1], [0.2], [0.3]],
[[0.4], [0.5], [0.6]],
[[0.7], [0.8], [0.9]]])
AA = tensor([[[0, 0, 0, 0, 0],
[0, 0.1, 0, 0, 0],
[0, 0, 0.2, 0, 0],
[0, 0, 0, 0.3, 0]],
[[0, 0, 0, 0, 0],
[0, 0.4, 0, 0, 0],
[0, 0, 0.5, 0, 0],
[0, 0, 0, 0.6, 0]],
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 2.4, 0, 0, 0],
[0, 0, 0, 0, 0]]])
XYalayer_size = A.shape[1] * A.shape[2]
index_offset = torch.arange(0, A.shape[0] * layer_size, layer_size).unsqueeze(1)
indices = (X * A.shape[2] + Y) + index_offset
A = A.view(-1).index_add(0, indices.view(-1), Z.view(-1)).view(A.shape)
什么是
XYZ