When use the loss.backward with L1 loss


I have a short question about “torch.nn.L1Loss()”.

As we know, it is not possible to differentiate |x| when x is zero (because the derivative of |x| is x/|x|).

However, when I used torch.nn.L1Loss() with network, loss.backward() worked…

How is it possible?

Also, would you explain how it is possible to calculate gradient of L1 Loss in pytorch when x is zero?


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What is x in your example? Is it the input of the model?

Let’s say x your input (with requires_grad=True) and the first layer of the model in a Linear with no bias, parameters w, doing y = w*x.

The last step of the backprop would be dL/dx = dy/dx * dL/dy
and dy/dx = w, no matter what is the value of x.

Thanks for reply. And sorry for confusing.

x is just a variable of the equation ‘y = |x|’, not the input or output of network.

I just wonder when try to differentiate the y = |x|, how the torch handle when x is 0 although it is impossible to differentiate at that point.

Ok, my bad, I mistook the pipes in |x| for l.

I think it uses an approximate gradient on 0. And as you can see here, it considers 0 as a positive input.

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Thank you very much!

Then, I need to find the implementation of approximate.