# You can choose whether to use function "sum" and "mean" depending on your task p_loss = p_loss.sum() q_loss = q_loss.sum() loss = (p_loss + q_loss) / 2 return loss 这段代码定义了一个函数compute_kl_loss,它计算了两个概率分布p和q之间的KL散度的均值。KL散度是一种度量两个...
KD_loss = nn.KLDivLoss()(F.log_softmax(student_outputs/T, dim=1), F.softmax(teacher_outputs/T, dim=1)) * T * T loss_KL(output_mask, Variable(_ori.data, requires_grad = False)
KL散度,即Kullback-Leibler散度,相对熵,衡量概率分布差异 离散概率分布P和Q间的KL散度公式:[公式],连续概率分布间公式:[公式]KL散度总是非负值,全等分布时值为零,值越大表示分布差异越大 KL散度不对称,$D_{KL}(P||Q)$与$D_{KL}(Q||P)$不相等 在机器学习中,用于度量预测与真实概率...
klloss公式 KL散度公式有两种形式,一种适用于离散概率分布,另一种适用于连续概率分布。 对于离散概率分布P和Q,KL散度的公式为:D K L ( P∣∣Q ) = Σ P ( i ) l o g ( P ( i ) / Q ( i ) )D_{KL}(P||Q) = Σ P(i) log(P(i) / Q(i))DKL (P∣∣Q)=ΣP(i)log(P(i)/...
tensorflow 1.15 KL loss 代码 tensorflow.python.keras.utilsimport kl=tf.keras.losses.KLDivergence( reduction=losses_utils.ReductionV2.NONE, name='kullback_leibler_divergence') kl_loss=tf.reduce_mean(kl(logit1,logit2))
loss_kl(pi, targets): pi_var = Variable(pi, requires_grad=True) logsoftmax = nn.LogSoftmax(dim=1) kl = nn.KLDivLoss() if cuda: logsoftmax.cuda() kl.cuda() logpi = logsoftmax(pi_var) return kl(logpi, Variable(targets)) * logpi.size(1), pi_var def loss_plain(pi, ...
KL散度 KL散度,又叫相对熵,用于衡量两个分布(离散分布和连续分布)之间的距离。 设、 是离散随机变量的两个概率分布,则对的KL散度是: KLDivLoss 对于包含...
In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to ...
kl_loss=nn.KLDivLoss(reduction="batchmean") # input should be a distribution in the log space input=F.log_softmax(torch.randn(3,5, requires_grad=True)) # Sample a batch of distributions. Usually this would come from the dataset
在深度学习模型中,NLLLoss、KLDivLoss、CrossEntropyLoss是三个常见的损失函数,尤其在知识蒸馏领域常被比较。首先,我们来了解Softmax函数。它常用于多分类及归一化,公式如下:[公式]。Softmax函数的一个重要特性是使输出值之间差距显著,通过引入超参数温度T进行调整,公式为:[公式]。当T增大时,不...