chainer.functions.huber_loss¶
-
chainer.functions.
huber_loss
(x, t, delta, reduce='sum_along_second_axis')[source]¶ Loss function which is less sensitive to outliers in data than MSE.
\[a = x - t\]and
\[\begin{split}L_{\delta}(a) = \left \{ \begin{array}{cc} \frac{1}{2} a^2 & {\rm if~|a| \leq \delta} \\ \delta (|a| - \frac{1}{2} \delta) & {\rm otherwise,} \end{array} \right.\end{split}\]The output is a variable whose value depends on the value of the option
reduce
. If it is'no'
, it holds the elementwise loss values. If it is'sum_along_second_axis'
, loss values are summed up along the second axis (i.e.axis=1
).Parameters: - x (Variable) – Input variable.
The shape of
x
should be (\(N\), \(K\)). - t (Variable) – Target variable for regression.
The shape of
t
should be (\(N\), \(K\)). - delta (float) – Constant variable for huber loss function as used in definition.
- reduce (str) – Reduction option. Its value must be either
'sum_along_second_axis'
or'no'
. Otherwise,ValueError
is raised.
Returns: A variable object holding a scalar array of the huber loss \(L_{\delta}\). If
reduce
is'no'
, the output variable holds array whose shape is same as one of (hence both of) input variables. If it is'sum_along_second_axis'
, the shape of the array is same as the input variables, except the second axis is removed.Return type: - See:
- Huber loss - Wikipedia.
- x (Variable) – Input variable.
The shape of