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import torch | |
from torch.autograd import Variable | |
import torch.autograd as autograd | |
import torch.nn as nn | |
import torch.nn.functional as F | |
v_in = Variable(torch.Tensor([0.1, 0.1]).view(2, 1), requires_grad=True) | |
def forward(v_in): | |
f1 = lambda x: x * 2 | |
f2 = torch.nn.Linear(1, 1) | |
grad_out = Variable(torch.ones(2, 1)) | |
gradient = torch.autograd.grad(outputs=f2(f1(v_in)), inputs=v_in, | |
grad_outputs=grad_out, | |
create_graph=True, retain_graph=True, | |
only_inputs=True)[0] | |
out = gradient.sum() | |
return out | |
# returns false | |
autograd.gradcheck(forward, [v_in]) | |
forward(v_in).backward() | |
v_in.grad # these are non-existent? |
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