使用pytorch实现focal loss

好的，使用 PyTorch 实现 Focal Loss 可以帮助解决分类问题中的类不平衡问题，提高模型的准确率和鲁棒性。具体实现可以参考以下代码：

import torch.nn as nn
import torch

class FocalLoss(nn.Module):
    def __init__(self, alpha=1, gamma=2):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma

    def forward(self, inputs, targets):
        BCE_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)
        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1-pt)**self.gamma * BCE_loss
        return F_loss.mean()

使用时，只需将该自定义 Loss 传入模型即可，例如：

model = Classifier()
loss_fn = FocalLoss(alpha=0.25, gamma=2)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for input, target in train_loader:
        output = model(input)
        loss = loss_fn(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

希望能对您有所帮助！