torch.nn.CosineSimilarity(dim=None,eps=1e-6) ``` 其中,`dim`参数指定了计算相似度的维度,`eps`参数是一个小的常数,用于防止除数为零。默认情况下,`dim`参数为None,表示在所有维度上进行相似度计算。 在使用cosinesimilarityloss时,通常需要将模型输出和目标值作为输入,并指定损失函数的类型为cosinesimilaritylo...
hinge_loss = nn.HingeEmbeddingLoss(margin=0.2) a = torch.randn(100, 128, requires_grad=True) b = torch.randn(100, 128, requires_grad=True) x = 1 - torch.cosine_similarity(a, b) # 定义a与b之间的距离为x print(x.size()) y = 2 * torch.empty(100).random_(2) - 1 output = ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importcosine_similarity[as 别名]defforward_gmmn(self, visual_features, semantic_features, class_id, words, metrics):loss = mmd(real=visual_features, fake=semantic_features, **self.gmmn_config["mmd"])ifself.gmmn_config.get("old_mmd...
10、torch的自定义损失函数用nn module继承然后像写numpy一样来写就行,这个对于metric 的 learning非常的方便,比如cosine similarity,可以很快写出cosine loss的形式,基本上只有有loss function的公式,注意一些维度之类的匹配问题,很难写不出来。 11、torch的显存重计算技巧,这个不用多说了,应该已经有很多人写过了,确...
>>>input1 = autograd.Variable(torch.randn(100,128))>>>input2 = autograd.Variable(torch.randn(100,128))>>>output = F.cosine_similarity(input1, input2)>>>print(output) 损失函数(Loss functions) torch.nn.functional.nll_loss(input, target, weight=None, size_average=True) ...
10、torch的自定义损失函数用nn module继承然后像写numpy一样来写就行,这个对于metric 的 learning非常的方便,比如cosine similarity,可以很快写出cosine loss的形式,基本上只有有loss function的公式,注意一些维度之类的匹配问题,很难写不出来。 11、torch的显存重计算技巧,这个不用多说了,应该已经有很多人写过了,确...
torch.nn.functional.cosine_embedding_loss(input1, input2, target, margin=0, size_average=True, reduce=True) → Tensor source 详细可见CosineEmbeddingLoss torch.nn.functional.cross_entropy(input, target, weight=None, size_average=True, ignore_index=-100, reduce=True) ...
torch.nn.CosineSimilarity Unsupported. 161 torch.nn.PairwiseDistance Supported 162 torch.nn.L1Loss Supported 163 torch.nn.MSELoss Supported 164 torch.nn.CrossEntropyLoss Supported 165 torch.nn.CTCLoss Unsupported. 166 torch.nn.NLLLoss Supported ...
inst_eq = (instance_t[:,None].expand_as(cos_sim) == instance_t[None, :].expand_as(cos_sim)).float()# Rescale to be between 0 and 1cos_sim = (cos_sim +1) /2# If they're the same instance, use cosine distance, else use cosine similarityloss = (1- cos_sim) * inst_eq ...
CosineSimilarity torch.nn.CosineSimilarity(dim = 1, eps = 1e-08) 1. 参数说明: dim:计算余弦相似度的维度,默认为1 eps:小值以避免被0除,默认1e-8 input1 = Variable(torch.randn(5, 12)) input2 = Variable(torch.randn(5, 12)) cos = nn.CosineSimilarity(dim=1, eps=1e-6) ...