下面是基于PyTorch的Focal Loss代码实现: ```python import torch import torch.nn as nn import torch.nn.functional as F class FocalLoss(nn.Module): def __init__(self, gamma=2, alpha=0.25): super(FocalLoss, self).__init__ self.gamma = gamma self.alpha = alpha def forward(self, inputs...
网上有很多code都有问题,特将经测试无误的正确的code公布如下(pytorch): importtorchimporttorch.nnasnnfromtorch.nnimportfunctionalasFclassfocal_loss_multi(nn.Module):def__init__(self,alpha=[0.1,0.2,0.3],gamma=2,num_classes=3,size_average=True):super(focal_loss_multi,self).__init__()self.size...
首先需要加载pytorch的库 importtorchimporttorch.nnasnnimporttorch.optimasoptimfromtorch.utils.dataimportDataLoader,Datasetimporttorchvisionimporttorchvision.transformsasFfromIPython.displayimportdisplayclassFocalLoss(nn.Module):def__init__(self,weight=None,reduction='mean',gamma=0,eps=1e-7):super(FocalLoss,se...
代码 importtorchimporttorch.nnasnnimporttorch.nn.functionalasFfromtorch.autogradimportVariableclassFocalLoss(nn.Module):def__init__(self, class_num, alpha=None, gamma=2, size_average=True):super(FocalLoss, self).__init__()ifalphaisNone:# alpha 是平衡因子self.alpha = Variable(torch.ones(clas...
这是Focal loss在Pytorch中的实现。 代码语言:javascript 复制 classWeightedFocalLoss(nn.Module):"Non weighted version of Focal Loss"def__init__(self,alpha=.25,gamma=2):super(WeightedFocalLoss,self).__init__()self.alpha=torch.tensor([alpha,1-alpha]).cuda()self.gamma=gamma ...
绘制pytorch的loss曲线 pytorch focal loss,对于二分类问题,使用softmax或者sigmoid,在实验结果上到底有没有区别(知乎上相关问题讨论还不少)。最近做的相关项目也用到了这一块,从结果上来说应该是没什么区别,但是在模型上还是存在一定差异性的(可以应用于多模型融合、
FocalLoss的pytorch代码实现 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 classFocalLoss(nn.Module): def__init__(self, alpha=1, gamma=2, logits=False,reduce=True): super(FocalLoss,self).__init__() self.alpha=alpha...
时,Focal Loss就等于原来的交叉熵。 二、pytorch代码实现 """ 以二分类任务为例 """fromtorchimportnnimporttorchclassFocalLoss(nn.Module):def__init__(self,gama=1.5,alpha=0.25,weight=None,reduction="mean")->None:super().__init__()self.loss_fcn=torch.nn.CrossEntropyLoss(weight=weight,reduction...
SoftPool的pytorch代码实现 pytorch focal loss 从minst谈起 老规矩,我们继续从mnist开始 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5, 1) self.conv2 = nn.Conv2d(20, 50, 5, 1)...