Supposing that your predictions are always different from zero, since you want to be invariant to the scale, you can optimize the ratio: $$ loss(out, target) = |\frac{pred - target}{target}| $$ or if you want it to be differentiable, you can consider the square: $$ loss(out, target) = \left(\frac{pred - target}{target}\right)^2 $$

Now, those loss are symmetric, therefore overshooting or undershooting is equally penalized

Supposing that your predictions are always different from zero, since you want to be invariant to the scale, you can optimize the ratio: $$ loss(out, target) = |\frac{pred - target}{target}| $$ or if you want it to be differentiable, you can consider the square: $$ loss(out, target) = \left(\frac{pred - target}{target}\right)^2 $$

Now, those loss are symmetric, therefore overshooting or undershooting is equally penalized

If you have targets very close to 0, you might want to add a coefficient on the bottom to avoid division by zero, and their relative inaccuracies