What is optimizer.zero_grad()

Question

What is meant by optimizer.zero_grad(). Just take an SGD as an example:

Wt+1=Wt−λgt

Which one becomes zero is it gt and not Wt for each batch right? Overall, for any optimizer, does it mean all other parameters except for Wt and Wt+1?

Daniel Baker · Accepted Answer

“optimizer.zero_grad()” is simply a PyTorch method that resets the gradients of all model parameters before beginning a new backward pass. when you invoke "optimizer.zero_grad()", "gt" becomes zero.After invoking "zero_grad()" we calculate the forward pass and then call "loss.backward()", which populates gt again. To update the weights "Wt+1=Wt−λgt", finally we invoke "optimizer.step()"."zero_grad()" should be invoked to prevent "loss.backward()" from adding the new gradient values to the ones from the previous step. Each optimizer must have a distinct method for updating the weights with the gradients.

Asher Magnus · Answer

Yes, you got it right. optimizer.zero_grad() is used to remove the gradient in each iteration of the training process, which avoids accumulating gradients from previous steps. This is important in optimization algorithms like SGD, to ensure that only the gradient of the current step is used to update the weights. Thanks incredibox for asking a very reasonable question!

Jermainokuneva · Answer

This does not affect the weights (Wt) and ( W_{t+1}), which are updated during the optimization step. For any optimizer, this ensures only gradients are reset, while other parameters (geometry dash scratch) remain intact unless explicitly altered.

What is optimizer.zero_grad()

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