kl_loss=nn.KLDivLoss(reduction="batchmean", log_target=True) log_target=F.log_softmax(torch.rand(3,5)) output=kl_loss(input, log_target)
🐛 Describe the bug Quesition I meet “TypeError: forward() got an unexpected keyword argument 'log_target'” when running the demo of "nn.KLDivLoss". Code Snippet import torch import torch.nn as nn import torch.nn.functional as F kl_loss =...
若p(x)是index-target,即每个target是类别下标,则q(x)不需归一化和log,直接使用ce;若p(x)是未归一的概率分布,则ce≠KL,按需取用,若要用KL,则需对q(x)进行log_softmax,p(x)需要softmax 2023-03-23 21:462回复 习惯慢半拍吧 西电很强谢谢up 2023-03-22 08:072回复 UP主觉得很赞...
nn.L1Loss https://pytorch.org/docs/stable/generated/torch.nn.L1Loss.html#torch.nn.L1Loss 例子: input=[1,3,4] target=[2,3,7] 则loss=(|2-1|+|3-3|+|7-4|)/3=4/3=1.333 其中可以加入参数,求和,默认是求平均 nn.MSELoss 均方差损失函数 ht...训练...
softmax(target / self.temperature, dim=1) loss = F.kl_div(input, target, reduction='none', log_target=False) loss = loss * self.temperature**2 batch_size = input.shape[0] if self.reduction == 'sum': # Change view to calculate instance-wise sum loss = loss.view(batch_...
(1)export CONTEXT_DEVICE_TARGET=Ascend export CONTEXT_MODE=PYNATIVE_MODE export MS_DISABLE_KERNEL_BACKOFF=1 export CONTEXT_JIT_LEVEL=O0(910b需设置,910A不用设置) (2)cd MindSporeTest/operations (3)pytest -s -v test_f_kl_div.py::test_f_kldivloss_input_6d_reduction_sum --disable-warnings...
def__init__(self,size_average=None,reduce=None,reduction:str='mean',log_target:bool=False)->None:super(KLDivLoss,self).__init__(size_average,reduce,reduction)self.log_target=log_target 3. CrossEntropyLoss —— 交叉熵 This criterion combines :class:~torch.nn.LogSoftmaxand :class:~torch...
log(y, math.e) - x) val += loss_val return val / output.nelement() torch.manual_seed(20) loss = nn.KLDivLoss() input = torch.Tensor([[-2, -6, -8], [-7, -1, -2], [-1, -9, -2.3], [-1.9, -2.8, -5.4]]) target = torch.Tensor([[0.8, 0.1, 0.1], [0.1, 0.7...
softmax(target / self.temperature, dim=1) loss = F.kl_div(input, target, reduction='none', log_target=False) loss = loss * self.temperature**2 batch_size = input.shape[0] if self.reduction == 'sum': # Change view to calculate instance-wise sum loss = loss.view(batch_...