| nn_smooth_l1_loss {torch} | R Documentation |
Smooth L1 loss
Description
Creates a criterion that uses a squared term if the absolute
element-wise error falls below 1 and an L1 term otherwise.
It is less sensitive to outliers than the MSELoss and in some cases
prevents exploding gradients (e.g. see Fast R-CNN paper by Ross Girshick).
Also known as the Huber loss:
Usage
nn_smooth_l1_loss(reduction = "mean")
Arguments
reduction |
(string, optional): Specifies the reduction to apply to the output:
|
Details
\mbox{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i}
where z_{i} is given by:
z_{i} =
\begin{array}{ll}
0.5 (x_i - y_i)^2, & \mbox{if } |x_i - y_i| < 1 \\
|x_i - y_i| - 0.5, & \mbox{otherwise }
\end{array}
x and y arbitrary shapes with a total of n elements each
the sum operation still operates over all the elements, and divides by n.
The division by n can be avoided if sets reduction = 'sum'.
Shape
Input:
(N, *)where*means, any number of additional dimensionsTarget:
(N, *), same shape as the inputOutput: scalar. If
reductionis'none', then(N, *), same shape as the input