Easy to use class balanced cross entropy and focal loss implementation for Pytorch
Easy-to-use, class-balanced, cross-entropy and focal loss implementation for Pytorch.
When training dataset labels are imbalanced, one thing to do is to balance the loss across sample classes.
pip install balanced-loss
import torch
from balanced_loss import Loss
# outputs and labels
logits = torch.tensor([[0.78, 0.1, 0.05]]) # 1 batch, 3 class
labels = torch.tensor([0]) # 1 batch
# focal loss
focal_loss = Loss(loss_type="focal_loss")
loss = focal_loss(logits, labels)
# cross-entropy loss
ce_loss = Loss(loss_type="cross_entropy")
loss = ce_loss(logits, labels)
# binary cross-entropy loss
bce_loss = Loss(loss_type="binary_cross_entropy")
loss = bce_loss(logits, labels)
import torch
from balanced_loss import Loss
# outputs and labels
logits = torch.tensor([[0.78, 0.1, 0.05]]) # 1 batch, 3 class
labels = torch.tensor([0]) # 1 batch
# number of samples per class in the training dataset
samples_per_class = [30, 100, 25] # 30, 100, 25 samples for labels 0, 1 and 2, respectively
# class-balanced focal loss
focal_loss = Loss(
loss_type="focal_loss",
samples_per_class=samples_per_class,
class_balanced=True
)
loss = focal_loss(logits, labels)
# class-balanced cross-entropy loss
ce_loss = Loss(
loss_type="cross_entropy",
samples_per_class=samples_per_class,
class_balanced=True
)
loss = ce_loss(logits, labels)
# class-balanced binary cross-entropy loss
bce_loss = Loss(
loss_type="binary_cross_entropy",
samples_per_class=samples_per_class,
class_balanced=True
)
loss = bce_loss(logits, labels)
import torch
from balanced_loss import Loss
# outputs and labels
logits = torch.tensor([[0.78, 0.1, 0.05]]) # 1 batch, 3 class
labels = torch.tensor([0])
# number of samples per class in the training dataset
samples_per_class = [30, 100, 25] # 30, 100, 25 samples for labels 0, 1 and 2, respectively
# class-balanced focal loss
focal_loss = Loss(
loss_type="focal_loss",
beta=0.999, # class-balanced loss beta
fl_gamma=2, # focal loss gamma
samples_per_class=samples_per_class,
class_balanced=True
)
loss = focal_loss(logits, labels)
What is the difference between this repo and vandit15's?
torch.nn.Module
https://arxiv.org/abs/1901.05555