Shulou(Shulou.com)06/01 Report--

In this issue, Xiaobian will bring you about what is the attention point of gradient backpropagation in PyTorch. The article is rich in content and analyzed and described from a professional perspective. After reading this article, I hope you can gain something.

In an iteration loop,optimizer.zero_grad() statements can be placed arbitrarily, as long as they precede loss.backward(), which returns the gradient to zero, otherwise it accumulates in each iteration,loss.backward() is used to backpropagate and calculate the gradient, and optimizer.step() is used to automatically update the parameters of the optimizer.

optimizer.zero_grad()loss.backward()optimizer.step() The above is what the attention point of gradient reverse propagation in PyTorch shared by Xiaobian is. If there is a similar doubt, please refer to the above analysis for understanding. If you want to know more about it, please pay attention to the industry information channel.

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