classCrossEntropyLoss(_WeightedLoss): __constants__ = ['ignore_index','reduction','label_smoothing'] ignore_index:int label_smoothing:float def __init__(self, weight: Optional[Tensor] = None, size_average=None, ignore_index:int= -100, reduce=None, reduction: str ='mean', label_smoothin...
) / 2. #θ(x)阶跃函数 class loss(nn.Module): def __init__(self, theta, num_classes=2, reduction='mean', margin=0.5): super().__init__() self.theta = theta self.num_classes = num_classes self.reduction = reduction self.m = margin # 置信度,>m的正样本 及 <1-m的负样本 ...
numpy 实现上式无 weight 多维loss, 默认 reduction = 'mean', 取平均 def myCrossEntropyLoss(x, label): loss = [] for i, cls in enumerate(label): # 对应上面公式的 -x[class] x_class = -x[i][cls] # 对应上面公式的 log(sum) log_x_j = np.log( sum([ np.exp(j) for j in x...
class_weight=[ 1, 1.1, 1, 1, 1, 1, ], loss_name='loss_ce', loss_weight=1.0, type='CrossEntropyLoss'), ], norm_cfg=dict(requires_grad=True, type='BN'), num_classes=6, type='SegformerHead'), pretrained=None, test_cfg=dict(mode='whole'), train_cfg=dict(), type='EncoderDe...
CLASS torch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=- 100,reduce=None, reduction=‘mean’, label_smoothing=0.0) 1. 类别的分类问题。参数weight给定时,其为分配给每一个类别的权重的一维张量(Tensor)。当数据集分布不均衡时,这是很有用的。
要减少假阴性样本的数量,可以增大 pos_weight;要减少假阳性样本的数量,可以减小 pos_weight。 三、focal loss 上面针对不同类别的像素数量不均衡提出了改进方法,但有时还需要将像素分为难学习和容易学习这两种样本。 容易学习的样本模型可以很轻松地将其预测正确,模型只要将大量容易学习的样本分类正确,loss就可以减小...
classtorch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean', label_smoothing=0.0) 参数: weight(Tensor,可选的) -给每个类一个手动重新调整的权重。如果给定,则必须是大小为C的张量 size_average(bool,可选的) -已弃用(请参阅reduction)。默认情况下...
classCrossEntropy2d(nn.Module): def __init__(self): super(CrossEntropy2d,self).__init__() self.criterion = nn.CrossEntropyLoss(weight=None,size_average=True) def forward(self,out,target): n, c, h, w = out.size() # n:batch_size, c:classout = out.view(-1,c) # (n*h*w,...
classCrossEntropyLoss(_WeightedLoss):__constants__=['ignore_index','reduction']ignore_index:intdef__init__(self,weight:Optional[Tensor]=None,size_average=None,ignore_index:int=-100,reduce=None,reduction:str='mean')->None:super(CrossEntropyLoss,self).__init__(weight,size_average,reduce,reduct...
406 407 return ret /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/loss.py in cross_entropy(input, label, weight, ignore_index, reduction, soft_label, axis, use_softmax, name) 1418 'Expected 0 <= label_value < class_dimension({}), but got {}...