gradients的一些注意点

Each variable has a [.grad_fn] attribute that references a Function that has created the Variable(except for Variables created by the user their grad_fn is None).

out.backward()=out.backward(torch.Tensor([1.0]))

 import torch
 from torch.autograd import Variable

 # part 1
 x = Variable(torch.ones(2, 2), requires_grad=True)
 y = x + 2
 print(y.grad_fn)    # <torch.autograd.function.AddConstantBackward object at 0x000001D8A156E048>
 print(y.grad)       # None
 z = y * y * 3
 out = z.mean()
 out.backward()
 print(out.grad)     # None
 print(y.grad)       # None
 print(x.grad)       # d(out)/dx
 '''
 Variable containing:
  4.5000  4.5000
  4.5000  4.5000
 [torch.FloatTensor of size 2x2]
 '''
 print(x.grad_fn)    # None
 print(x.grad_output)

 # part 2
 x = torch.randn(3)
 x = Variable(x, requires_grad=True)
 y = x * 2
 # print(type(y))          # <class 'torch.autograd.variable.Variable'>
 # print(type(y.data))     # <class 'torch.FloatTensor'>
 # print(y.data.norm())    # 4.076032686856067
 while y.data.norm() < 1000:
     y = y * 2

 # print(x.grad)           # None
 gradients = torch.FloatTensor([0.1, 1.0, 0.0001])
 # print(y)  # Variable containing: 377.3516 997.8206 11.2558 [torch.FloatTensor of size 3]
 y.backward(gradients)
 # y.backward()            # y.backward()=y.backward(torch.Tensor([1.0]))
 # RuntimeError: grad can be implicitly created only for scalar outputs

 print(x.grad)
 # print(x.grad_fn)          # None
 # print(x.grad_output)      # AttributeError: 'Variable' object has no attribute 'grad_output'