How to bypass non-differentiable modules while loss.backward()


So I have a CNN, with a softmax output, and a mask() function. The masks are like tensor([0,1,1]), tensor([1,1,0]) etc. The CNN has a softmaxed output over three classes.

pred = cnn(           #Returns PDF over three classes
mask = get_mask(*args)
final_pred = pred *

Now, the mask returned by get_mask() is a deterministically mapped mask, and doesnt require any training/gradient. However, when i use,

loss = crtierion(final_pred, labels)

I get the error

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

I even tried making mask = autograd.Variable(mask, requries_grad=False), but still the same error. Please help!



In general, you should avoid pinging people directly as it just adds more noise…

For your issue, are you sure that you don’t set your cnn’s weights not to require gradients?
You should make sure that pred requires grad as well.