There could be several reasons why Pytorch's loss function is returning NaN:
Input data: If the input data to the loss function contains NaN or Inf values, the loss function will return NaN. Therefore, it is essential to ensure that the input data is free from NaN or Inf values.
Learning rate: If the learning rate is too high, the loss function may return NaN. Lowering the learning rate can help in resolving this issue.
Gradient explosion/ vanishing: If the gradients are too small or too large, the loss function may return NaN. Techniques like gradient clipping can help in addressing this issue.
Model architecture: If the model architecture is poorly designed, it may lead to NaN values in the loss function. In this case, reviewing or redesigning the model architecture can be helpful.
Loss function implementation: If the loss function has been implemented incorrectly, it may return NaN. In this case, carefully reviewing the loss function's implementation can help resolve the issue.
Please start posting anonymously - your entry will be published after you log in or create a new account. This space is reserved only for answers. If you would like to engage in a discussion, please instead post a comment under the question or an answer that you would like to discuss
Asked: 2022-02-10 11:00:00 +0000
Seen: 17 times
Last updated: Mar 18 '22
What is the reason for the loss of focus in TextField when new views are included around it?
If the take profit or stop loss is reached, do the entries exit on the next bar in Pine Script?
What is the condition for preserving every data entry per ID?
What is the reason for the appearance of both WinForms ComboBox DropDown and Autocomplete window?
What is the reason for websites adjusting the default font size to 14px?
What is the reason for the authentication failure in Azure GIT?
What is the reason for R returning a line that has no slope?
What could be the reason for my Material UI Tabs component to randomly override other styles?