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 = ...
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...
def __init__(self, model): super(MSELoss, self).__init__() self.model = model Example #22Source File: CosineSimilarityLoss.py From sentence-transformers with Apache License 2.0 5 votes def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor): reps = [self....
CosineSimilarity PairwiseDistance Loss functions L1Loss MSELoss CrossEntropyLoss CTCLoss NLLLoss PoissonNLLLoss KLDivLoss BCELoss BCEWithLogitsLoss MarginRankingLoss HingeEmbeddingLoss MultiLabelMarginLoss SmoothL1Loss SoftMarginLoss MultiLabelSoftMarginLoss CosineEmbeddingLoss MultiMarginLoss TripletMarginLoss ...
cosine_embedding_loss cosine_similarity count_nonzero cpp cross ctc_loss ctypes cuda cuda_path cuda_version cudnn_affine_grid_generator cudnn_batch_norm cudnn_convolution cudnn_convolution_transpose cudnn_grid_sampler cudnn_is_acceptable cummax cummin cumprod cumsum default_generator deg2rad deg2rad_...
>>>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) ...
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) ...
()torch.nn.functional.cosine_embedding_loss()torch.nn.functional.cosine_similarity()torch.nn.functional.cross_entropy()torch.nn.functional.ctc_loss()torch.nn.functional.dropout()torch.nn.functional.dropout2d()torch.nn.functional.dropout3d()torch.nn.functional.elu()torch.nn.functional.elu_()torch....
>>> 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)...