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[D] Relationship between Learning Rate and Gradient Clipping

In a recent project I’ve found that I need to use gradient clipping to stop my model from suddenly NaNing out after training for a while, and it also seems to have a positive regularizing effect. I’ve read that the clipping cutoff should be trained as a hyperparameter, but is there any rule of thumb or results on setting the learning rate when using gradient clipping? It seems like if the gradient clipping cutoff is too low, most gradient steps will be of the same magnitude.

submitted by /u/x_wolfman_x
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.