megengine.functional.nn.hinge_loss¶
- hinge_loss(pred, label, norm='L1', reduction='mean')[源代码]¶
Caculates the hinge loss which is often used in SVM.
The hinge loss can be described as:
\[loss(x, y) = \frac{1}{N}\sum_i\sum_j(max(0, 1 - x_{ij}*y_{ij}))\]- 参数
pred (
Tensor) – input tensor representing the predicted probability, shape is (N, C).label (
Tensor) – input tensor representing the binary classification label, shape is (N, C).norm (
str) – specify the norm to caculate the loss, should be “L1” or “L2”.reduction (
str) – the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. Default: ‘mean’
- 返回类型
- 返回
loss value.
实际案例
from megengine import tensor import megengine.functional as F pred = tensor([[0.5, -0.5, 0.1], [-0.6, 0.7, 0.8]], dtype="float32") label = tensor([[1, -1, -1], [-1, 1, 1]], dtype="float32") loss = F.nn.hinge_loss(pred, label) print(loss.numpy())
Outputs:
1.5