reduction=reduction, label_smoothing=label_smoothing, ) if size_average is not None or reduce is not None: reduction = _Reduction.legacy_get_string(size_average, reduce) return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothin...
reduction= “mean” doesn’t return the true KL divergence value, please use reduction= “batchmean” which aligns with the mathematical definition. In a future release, “mean” will be changed to be the same as “batchmean”. 参数: size_average,reduceDeprecated (seereduction) reduction (str...
kl_sum = F.kl_div(logp_x, p_y, reduction='sum')kl_mean = F.kl_div(logp_x, p_y, reduction='mean')print(kl_sum, kl_mean)>>> tensor(3.4165) tensor(0.1708)补充：pytorch中的kl散度，为什么kl散度是负数？F.kl_div()或者nn.KLDivLoss()是pytroch中计算kl散度的函数，它的⽤法有...
loss = F.kl_div(pred.log_softmax(1), target, reduction='none').sum(1)# apply weights and do the reductionifweightisnotNone: weight = weight.float() loss = weight_reduce_loss( loss, weight=weight, reduction=reduction, avg_factor=avg_factor)returnloss 开发者ID:tascj,项目名称:kaggle-ku...
kl_div(output, model_prob, reduction='sum') Example #18Source File: snli_trainer.py From castor with Apache License 2.0 5 votes def train_epoch(self, epoch): self.model.train() total_loss = 0 for batch_idx, batch in enumerate(self.train_loader): self.optimizer.zero_grad() # ...
kl_div(input, target, reduction=self.reduction) pytorch中通过torch.nn.KLDivLoss类实现,也可以直接调用F.kl_div 函数,代码中的size_average与reduce已经弃用。reduction有四种取值mean,batchmean, sum, none,对应不同的返回。 默认为mean 例子: import torch import torch.nn as nn import math def validate...
class torch.nn.NLLLoss(weight=None,size_average=None,ignore_index=-100,reduce=None,reduction='mean') 代码解释得很详细 2. KLDivLoss —— 相对熵 Kullback-Leibler divergence_ is a useful distance measure for continuous distributions and is often useful when performing direct regression over the spa...
'none': no reduction will be applied. 'batchmean': the sum of the output will be divided by batchsize. 'sum': the output will be summed. 'mean': the output will be divided by the number of elements in the output. Default: 'mean' log_target (bool, optional)– Specifies whether ...
KLDivLoss(reduction="none"), ): """Construct an LabelSmoothingLoss object.""" super(LabelSmoothingLoss, self).__init__() self.criterion = criterion self.padding_idx = padding_idx self.confidence = 1.0 - smoothing self.smoothing = smoothing self.size = size self.true_dist = None self....
@Level2 @SKIP_ENV_GPU("精度概率性不达标,I5KDLL") def test_f_kldivloss_input_6d_reduction_sum(): fact = KLDivLossFactory((2, 3, 4, 5, 6, 7), (2, 3, 4, 5, 6, 7), dtype=np.float16, dtypex=np.float16, dtypey=np.float16, reduction='sum') > fact.forward_cmp() ....