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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. But for too large xit outputs inf because of the exponentiation:. But when doing this, I still get nan for the gradient of too large values.

Do you know why this happens and if there is another way to make it work?

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A workaround I've found is to manually implement a Log1PlusExp function with its backward counterpart. Yet it does not explain the bad behavior of torch. This is why x should never be too large. It should be ideally in range [-1, 1]. If this is not the case you should normalize your inputs. Use PyTorch method torch. It helps the issue. How are we doing? Please help us improve Stack Overflow. Take our short survey.

Learn more. How to replace infs to avoid nan gradients in PyTorch Ask Question. Asked 9 months ago. Active 4 months ago. Viewed 1k times. Active Oldest Votes. Function The behavior of torch.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. Copyright c Facebook, Inc. All Rights Reserved import math from typing import Tuple import torch Value for clamping large dw and dh predictions. The heuristic is that we clamp such that dw and dh are no larger than what would transform a 16px box into a px box based on a small anchor, 16px, and a typical image size, px.

The transformation is parameterized by 4 deltas: dx, dy, dw, dh. In Fast R-CNN, these were originally set such that the deltas have unit variance; now they are treated as hyperparameters of the system. The transformation is parameterized by 5 deltas: dx, dy, dw, dh, da. Note: angles of deltas are in radians while angles of boxes are in degrees. These are treated as hyperparameters of the system. Args: deltas Tensor : transformation deltas of shape N, 5. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window. All Rights Reserved. Value for clamping large dw and dh predictions. The heuristic is that we clamp. The box-to-box transform defined in R-CNN. The transformation is parameterized. The transformation scales the box's width and height. Get box regression transformation deltas dx, dy, dw, dh that can be used.

That is, the relation. Prevent sending too large values into torch.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account.

After performing some computation that results in a NaN gradient, no amount of reassignment will result in a non-NaN gradient. OS: Ubuntu Python version: 3. Nvidia driver version: This is the same idea as and Short of keeping a mask around for all backward steps, it's pretty hard to solve as it doesn't fit floating point arithmetic. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. New issue. Jump to bottom. Copy link Quote reply. This comment has been minimized. Sign in to view. Pruning off NaN values in the gradient graph still produces NaN gradients. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. Linked pull requests.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm trying to do simple linear regression with 1 feature. It's a simple 'predict salary given years experience' problem. The NN trains on years experience X and a salary Y. For some reason the loss is exploding and ultimately returns inf or nan. As I mentioned, once it starts training the loss gets super big and ends up showing inf after like the 10th epoch.

I suspect it may have something to do with how i'm loading the data? This is what is in salaries. Once the loss becomes inf after a certain pass, your model gets corrupted after backpropagating. This probably happens because the values in "Salary" column are too big. Alternatively, you could try to initialize the parameters by hand rather than letting it be initialized randomlyletting the bias term be the average of salaries, and the slope of the line be 0 for instance.

That way the initial model would be close enough to the optimal solution, so that the loss does not blow up. Here is an example how this all happens. You may try to run this program which basically represents r-deep layer network. Learn more. Pytorch loss inf nan Ask Question. Asked 1 year, 9 months ago. Active 9 months ago. Viewed 7k times. For some reason the loss is exploding and ultimately returns inf or nan This is the code I have: import torch import torch.

SGD model.

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FloatTensor [[8. JAbrams JAbrams 1 1 gold badge 5 5 silver badges 12 12 bronze badges. Can you post the link to the salaries csv?

I would start by getting the average loss, instead of a sum why did not avoid averaging in the first place? Finally, you would make the problem more sensible for MSE by downscaling the output values I'd suggest a factor of 10so the values stay readable. Active Oldest Votes.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Could someone post a simple use case of BCELoss? The BCELoss function did not use to be numerically stable. You might want to use a sigmoid layer at the end of the network. In that way the number would represent probabilities.

Also make sure that the targets are binary numbers. If you post your complete code we might help more. Learn more. Ask Question. Asked 2 years, 11 months ago. Active 8 months ago. Viewed 24k times. Qubix Qubix 2, 6 6 gold badges 16 16 silver badges 43 43 bronze badges.

Have you added the sigmoid function for the last layer in your network? Active Oldest Votes. Otherwise, you can use the following function contributed by yzgao in the above issue : class StableBCELoss nn. Roger Trullo Roger Trullo 2 2 gold badges 6 6 silver badges 15 15 bronze badges. Sign up or log in Sign up using Google.

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Already on GitHub? Sign in to your account. During a simple educational reimpl of CTC I found that torch. Zero gradient is much better in this case since zero accumulates fine with other non-nan gradients. Previous related issue: One argument by apaszke there is that inf outputs are often bad anyway, but in the case of HMM-like algorithms in log-space they are natural. I agree this is not a very big issue since CTC problems are gone if float '-inf' is replaced by torch.

However, if it's an easy fix it would be nice to have, since -inf is naturally a neutral addition element for Log-space. Line in c96c. In the custom reimpl nan gradient also occurs if clamp is removed because of dividing by zero sum of exps of inputs happens to be zero.

One intermediate but practical way could be implementing a hack like F. So maybe a whole namespace torch. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. Labels module: autograd module: operators needs research topic: NaNs and Infs triaged. Copy link Quote reply. One argument by apaszke there is that inf outputs are often bad anyway, but in the case of HMM-like algorithms in log-space they are natural I agree this is not a very big issue since CTC problems are gone if float '-inf' is replaced by torch.

This comment has been minimized. Sign in to view.A torch. Tensor is a multi-dimensional matrix containing elements of a single data type. Tensor is an alias for the default tensor type torch. A tensor can be constructed from a Python list or sequence using the torch. If you have a numpy array and want to avoid a copy, use torch.

A tensor of specific data type can be constructed by passing a torch.

## PyTorchで絶対nanを出したいマン

Use torch. Each tensor has an associated torch. Storagewhich holds its data. The tensor class provides multi-dimensional, strided view of a storage and defines numeric operations on it.

For more information on the torch. Tensorsee Tensor Attributes. Methods which mutate a tensor are marked with an underscore suffix. For example, torch.

Current implementation of torch. Tensor introduces memory overhead, thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors. If this is your case, consider using one large structure.

To create a tensor with pre-existing data, use torch. To create a tensor with specific size, use torch. To create a tensor with the same size and similar types as another tensor, use torch. To create a tensor with similar type but different size as another tensor, use tensor. Returns a new Tensor with data as the tensor data.

**Torch Activity**

By default, the returned Tensor has the same torch. If you have a Tensor data and want to avoid a copy, use torch. Therefore tensor. The equivalents using clone and detach are recommended. Default: if None, same torch. Default: False. Returns a Tensor of size size filled with uninitialized data.

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Returns a Tensor of size size filled with 1. Size of integers defining the shape of the output tensor. Returns a Tensor of size size filled with 0. Is the torch.

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