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]))
1 import torch 2 from torch.autograd import Variable 3 4 # part 1 5 x = Variable(torch.ones(2, 2), requires_grad=True) 6 y = x + 2 7 print(y.grad_fn) # <torch.autograd.function.AddConstantBackward object at 0x000001D8A156E048> 8 print(y.grad) # None 9 z = y * y * 3 10 out = z.mean() 11 out.backward() 12 print(out.grad) # None 13 print(y.grad) # None 14 print(x.grad) # d(out)/dx 15 ''' 16 Variable containing: 17 4.5000 4.5000 18 4.5000 4.5000 19 [torch.FloatTensor of size 2x2] 20 ''' 21 print(x.grad_fn) # None 22 print(x.grad_output) 23 24 25 # part 2 26 x = torch.randn(3) 27 x = Variable(x, requires_grad=True) 28 y = x * 2 29 # print(type(y)) # <class 'torch.autograd.variable.Variable'> 30 # print(type(y.data)) # <class 'torch.FloatTensor'> 31 # print(y.data.norm()) # 4.076032686856067 32 while y.data.norm() < 1000: 33 y = y * 2 34 35 # print(x.grad) # None 36 gradients = torch.FloatTensor([0.1, 1.0, 0.0001]) 37 # print(y) # Variable containing: 377.3516 997.8206 11.2558 [torch.FloatTensor of size 3] 38 y.backward(gradients) 39 # y.backward() # y.backward()=y.backward(torch.Tensor([1.0])) 40 # RuntimeError: grad can be implicitly created only for scalar outputs 41 42 print(x.grad) 43 # print(x.grad_fn) # None 44 # print(x.grad_output) # AttributeError: 'Variable' object has no attribute 'grad_output'