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HuberLoss#

classtorch.nn.modules.loss.HuberLoss(reduction='mean',delta=1.0)[source]#

Creates a criterion that uses a squared term if the absoluteelement-wise error falls below delta and a delta-scaled L1 term otherwise.This loss combines advantages of bothL1Loss andMSELoss; thedelta-scaled L1 region makes the loss less sensitive to outliers thanMSELoss,while the L2 region provides smoothness overL1Loss near 0. SeeHuber loss for more information.

For a batch of sizeNN, the unreduced loss can be described as:

ℓ(x,y)=L={l1,...,lN}T\ell(x, y) = L = \{l_1, ..., l_N\}^T

with

ln={0.5(xn−yn)2,if ∣xn−yn∣<deltadelta∗(∣xn−yn∣−0.5∗delta),otherwise l_n = \begin{cases}0.5 (x_n - y_n)^2, & \text{if } |x_n - y_n| < delta \\delta * (|x_n - y_n| - 0.5 * delta), & \text{otherwise }\end{cases}

Ifreduction is notnone, then:

ℓ(x,y)={mean⁡(L),if reduction=‘mean’;sum⁡(L),if reduction=‘sum’.\ell(x, y) =\begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.}\end{cases}

Note

When delta is set to 1, this loss is equivalent toSmoothL1Loss.In general, this loss differs fromSmoothL1Loss by a factor of delta (AKA betain Smooth L1).SeeSmoothL1Loss for additional discussion on the differences in behaviorbetween the two losses.

Parameters

  • reduction (str,optional) – Specifies the reduction to apply to the output:'none' |'mean' |'sum'.'none': no reduction will be applied,'mean': the sum of the output will be divided by the number ofelements in the output,'sum': the output will be summed. Default:'mean'

  • delta (float,optional) – Specifies the threshold at which to change between delta-scaled L1 and L2 loss.The value must be positive. Default: 1.0

Shape:

  • Input:(∗)(*) where∗* means any number of dimensions.

  • Target:(∗)(*), same shape as the input.

  • Output: scalar. Ifreduction is'none', then(∗)(*), same shape as the input.

forward(input,target)[source]#

Runs the forward pass.

Return type

Tensor