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
ornumpy.ndarray
orcupy.ndarray
) – Input variable. The shape ofx
should be (\(N\), \(K\)). - t (
Variable
ornumpy.ndarray
orcupy.ndarray
) – Target variable for regression. The shape oft
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.
Example
>>> x = np.array([[-2.0, 3.0, 0.5], [5.0, 2.0, -0.5]]).astype('f') >>> x array([[-2. , 3. , 0.5], [ 5. , 2. , -0.5]], dtype=float32) >>> t = np.array([[-2.0, 3.0, 0.0], [10.0, 2.0, -0.5]]).astype('f') >>> t array([[-2. , 3. , 0. ], [10. , 2. , -0.5]], dtype=float32) >>> y = F.huber_loss(x, t, 1.0) >>> y.shape (2,) >>> y.data array([0.125, 4.5 ], dtype=float32) >>> y = F.huber_loss(x, t, 1.0, reduce='no') >>> y.shape (2, 3) >>> y.data array([[0. , 0. , 0.125], [4.5 , 0. , 0. ]], dtype=float32)
- x (