Gradient clipping is one technique that can help keep gradients from exploding. You can keep an eye on the gradient norm by logging it in your LightningModule:.

To log the parameter norms or grad norm you can do something like - grads = {n:p.grad.cpu() for n, p in model.named_parameters()} and then calculate the norm.

11 апр. 2020 г. · I am tracking my model's gradient norms by setting the flag track_grad_norm=2. However, this logs the individual norms of all the gradients.

Inspect gradient norms. Logs (to a logger), the norm of each weight matrix. (See: track_grad_norm argument of Trainer ). # the 2-norm trainer = Trainer ...

Gradient clipping can be enabled to avoid exploding gradients. By default, this will clip the gradient norm by calling torch.nn.utils.clip_grad_norm_() computed ...

Compute each parameter's gradient's norm and their overall norm. The overall norm is computed over all gradients together, as if they were concatenated into a ...

27 авг. 2020 г. · I'd like to log gradients obtained during training to a file to analyze/replicate the training later. What's a convenient way of doing this in PyTorch?

28 мар. 2023 г. · I want to perform some operations on the gradients while using Pytorch Lightning. I know that the model weights are getting updated (weights change every step, ...

Продолжительность: 0:43
Опубликовано: 17 авг. 2021 г.

If you want to customize gradient clipping, consider using configure_gradient_clipping() method. For manual optimization ( self.automatic_optimization = False ) ...