Add new regression loss function type to FBLearner (#21080)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21080
Add Huber loss as a new option for regression training (refer to TensorFlow implementation: https://fburl.com/9va71wwo)
# huber loss
def huber(true, pred, delta):
error = abs(true-pred)
loss = 0.5 * min(error, delta)^2 + delta * max(error - delta, 0)
return mean(loss)
As a combination of MSE loss (`x < delta`) and MAE loss (`x >= delta`), the advantage of Huber loss is to reduce the training dependence on outlier.
One thing worth to note is that Huber loss is not 2nd differential at `x = delta`. To further address this problem, one could consider adopt the loss of `LOG(cosh(x))`.
Reviewed By: chintak
Differential Revision: D15524377
fbshipit-source-id: 73acbe2728ce160c075f9acc65a1c21e3eb64e84
