torch代码实现 torch中提供有两种不同的api用于计算KL散度,分别是torch.nn.functional.kl_div()和torch.nn.KLDivLoss(),两者计算效果类似,区别无非是直接计算和作为损失函数类。 先介绍一下torch.nn.functional.kl_div(): 注意,该方法的input和target与 中 、 的位置正好相反,从参数名称就可以看出来(target为目...
(delta_u, sigma_prior_matrix_inv), delta_u_transpose ).squeeze() term3 = - mu_poster.shape[-1] term4 = torch.log(sigma_prior_matrix_det + eps) - torch.log( sigma_poster_matrix_det + eps ) kl_loss = 0.5 * (term1 + term2 + term3 + term4) kl_loss = torch.mean(kl_loss...
import torch import torch.nn as nn loss_fn = nn.CrossEntropyLoss() # 方便理解,此处假设batch_size = 1 x_input = torch.randn(2, 3) # 预测2个对象,每个对象分别属于三个类别分别的概率 # 需要的GT格式为(2)的tensor,其中的值范围必须在0-2(0<value<C-1)之间。 x_target = torch.tensor([0...
KL 散度,是一个用来衡量两个概率分布的相似性的一个度量指标。 先附上官方文档说明:https://pytorch.org/docs/stable/nn.functional.html torch.nn.functional.kl_div(input, target, size_average=None, reduce=None, reduction='mean') Parameters
torch.nn.functional.kl_div(input, target, size_average=None, reduce=None, reduction=‘mean’, log_target=False) The Kullback-Leibler divergence Loss See KLDivLoss for details. Parameters input –Tensor of arbitrary shape target –Tensor of the same shape as input size_average (bool, optional...
KL散度,也叫做相对熵,计算公式如下: importtorch.nnasnnimporttorchimporttorch.nn.functionalasFif__name__=='__main__':x_o=torch.Tensor([[1,2],[3,4]])y_o=torch.Tensor([[0.1,0.2],[0.3,0.4]])# x = F.log_softmax(x)x=F.softmax(x_o,dim=1)y=F.softmax(y_o,dim=1)criterion=...
不用自己实现，PyTorch内部集成了概率库，直接使用即可：fromtorch.distributionsimportNormal,kl_divergencep...
https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html?highlight=bceloss#torch.nn.BCE...
import torch.nn.functional as F# 定义两个矩阵x = torch.randn((4, 5)) y = torch.randn((4, 5))# 因为要用y指导x,所以求x的对数概率,y的概率logp_x = F.log_softmax(x, dim=-1) p_y = F.softmax(y, dim=-1) kl_sum = F.kl_div(logp_x, p_y, reduction='sum') ...