How can the precision of a GPU influence the regression accuracy?

Hi, I am using PyTorch to do a regression task.
Mainly I am using the neural network to approximate some values given some inputs.
But I found that the order of magnitude of approximation accuracy is about 10^-5.
For a deeper neural network this stays the same.
I am wondering if this could be the result of training with single-precision floating-points?

This sounds like a float precision issue.
You’ll get the same for different orders of operator execution:

x = torch.randn(100, 10, 100)
x1 = x.sum()
x2 = x.sum(0).sum(0).sum(0)
> tensor(0.0001220703)
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Thanks a lot!
Yeah, I found that with “precision=16”, the error is still in 10^-5 order.
Is there anything I can do to make this more accurate?

You could try to use double instead of float, but your GPU performance will drop.

Thanks you! I am trying to train the network with double now. The training turned slower and harder. The loss was decreased, but it seemed that the loss still would not get lower while in the magnitude of 10^-6. Maybe the learning rate needs to be tuned more carefully.