pyabsa.networks.losses.ClassImblanceCE
Module Contents
Classes
Reference: |
- class pyabsa.networks.losses.ClassImblanceCE.ClassBalanceCrossEntropyLoss[source]
Bases:
torch.nn.Module
Reference: Cui et al., Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019.
Equation: Loss(x, c) = frac{1-beta}{1-beta^{n_c}} * CrossEntropy(x, c)
Class-balanced loss considers the real volumes, named effective numbers, of each class, rather than nominal numeber of images provided by original datasets.
- Parameters:
beta (float, double) – hyper-parameter for class balanced loss to control the cost-sensitive weights.