Hi! The following code:
h_ = torch.nn.Parameter(torch.ones([1,1]))
h = h_**2
optimizer = torch.optim.SGD([h_], lr=0.1)
for _ in range(2):
loss = h.sum()
RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed. Specify retain_graph=True when calling backward the first time.
Can anyone tell me whats the issue? Thanks!
Try this code
h_ = nn.Parameter(torch.ones([1,1]),requires_grad=True)
optimizer = torch.optim.SGD([h_], lr=0.01)
for _ in range(10):
hOut=h_**2 - h_*10
loss = hOut.sum()
What I have done is inserted the calculation of hOut within the loop itself. Let us take a look at the changes in the graphical manner
Since you were not recalculating H within each loop, it freed the buffer after the first LOSS Bakcward. The next loop it threw and error
We included the the recalculation of H within the loop so that the buffer is refilled. Hope this helps.
I see. That fixes the problem. Thanks alot!