以下是完整的代码示例: importtorchimporttorch.nn.functionalasF# 创建随机向量vector1=torch.randn(5)vector2=torch.randn(5)# 定义模型distance=F.pairwise_distance(vector1,vector2)# 计算距离distance=distance.item()# 打印结果print("Pairwise Distance: ",distance) 1. 2. 3. 4. 5. 6. 7. 8. 9....
nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)参数: p(真实的) -规范度。默认值:2 eps(float,可选的) -小值以避免被零除。默认值:1e-6 keepdim(bool,可选的) -确定是否保留向量维度。默认值:假使用p-norm 计算向量 、 之间的成对距离:形状:...
self.margin = margindefforward(self, output1, output2, label): euclidean_distance = F.pairwise_distance(output1, output2, keepdim =True) loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance,2) + (label) * torch.pow(torch.clamp(self.margin - euclidean_distance,min=0...
distance=F.pairwise_distance(rep_a,rep_b,p2) 1. 2. 其中rep_a和rep_b为[batch_size,hidden_dim]
distance_negative = F.pairwise_distance(anchor, negative, p=2) loss = torch.relu(distance_positive - distance_negative + margin) return loss.mean() 数据准备: import glob import os import random from concurrent.futures import ThreadPoolExecutor ...
pow(F.pairwise_distance(anchor, negative), 2) part_1 = torch.clamp(matched - mismatched, min=self.t1) part_2 = torch.clamp(matched, min=self.t2) dist_hinge = part_1 + self.beta * part_2 loss = torch.mean(dist_hinge) return loss # 简单介绍一下用法,跟定义网络一样,继承nn.Module...
dist2 = F.pairwise_distance(c, d, p=2)#pytorch求欧氏距离 time_end=time.time() print(time_end-time_start) 1|0计算tensor在cuda上的计算速度 time_start=time.time() dist2 = F.pairwise_distance(e, f, p=2) time_end=time.time() ...
Currently F.pairwise_distance and F.cosine_similarity accept two sets of vectors of the same size and compute similarity between corresponding vectors. However, it's often useful to compute pairwise similarities or distances between all ...
pairwise_distance 根据Pytorch的文档,它其实在计算的时候调用了abs绝对值,来避免最后负数出现,从而保证运算的合理性 Norm文档 KLDivLoss 该损失函数是计算KL散度(即相对熵),它可以用于衡量两个分布的差异 KL散度基本定义 当p和q分布越接近,则趋近于1,经过log运算后,loss...
torch.nn.functional.pairwise_distance(x1, x2, p=2, eps=1e-06) 计算向量v1、v2之间的距离(成次或者成对,意思是可以计算多个,可以参看后面的参数) 参数: x1 - 第一个输入的张量, x2 - 第二个输入的张量 p - 矩阵范数的维度。默认值是2,即二范数。