https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/neural_networks_tutorial.ipynb 4
Hi I am trying to understand the NN with pytorch.
I have doubts in gradient calculations…
import torch.optim as optim
create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)
# in your training loop:
optimizer.zero_grad()   # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step()    # Does the update

From the about code, I understood loss.backward() calculates the gradients.
I am not sure, how these info shared with optimizer to update the gradient.
Can anyone explain this…
Thanks in advance !