x = torch.tensor([1, float('inf'), float('-inf'), float('nan')]) y = torch.isnan(x) print(y) # tensor([False, False, False, True]) 替换特殊元素: nan_to_num() 默认替换 x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14]) y = torch.nan_to_num(...
报错原因 我这里报错是因为loss中出现了nan值,所以导致该问题。 解决方法 直接做法:给loss最终的结果加上torch.nan_to_num()函数,可以避免nan值。 但是最好应当查明为什么出现nan值,针对性解决才能真正解决bug。 参考文章: AssertionError:在Pytorch的AutomaticMixedPrecision中未记录此优化器的i...
importtorchsigma=torch.tensor([1,2,3])p=1/(torch.tile(sigma,(3,1)).T-sigma)p.nan_to_n...
torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1, torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1, torch._native_batch_norm_legit: lambda input, weight, bias, training, momentum, eps: ...
I am sorry to interrupt again. But I ran the Pretrain model in 08_Self_Supervised_TSBERT.ipynb. An error came. # Unlabeled 100% learn = ts_learner(udls100, InceptionTimePlus, cbs=[ShowGraph(), MVP(target_dir='./data/MVP', fname=f'{dsid}_...
The 'torch' module lacks the 'nan_to_num' attribute You will need to update your torch package to access it: pip install --upgrade torch, \torch\csrc\utils\tensor_numpy.cpp:67.), > import torch File "C:\Users\Emil\AppData\Roaming\Python\Python38\site-packages\torch\__init, \Python...
tensor.mvlgamma_ tensor.nansum 否 可以走cpu实现。 tensor.narrow 是 tensor.narrow_copy tensor.ndimension tensor.nan_to_num 否 tensor.nan_to_num_ tensor.ne 否 tensor.ne_ tensor.not_equal 是 tensor.not_equal_ tensor.neg 是 tensor.neg_ tensor.negative 是 tensor.negative_ tensor.nelement ...
num_classes (int): The number of classes. :rtype: :class:`Tensor` """ tp = true_positive(pred, target, num_classes).to(torch.float) fn = false_negative(pred, target, num_classes).to(torch.float) out = tp / (tp + fn) out[torch.isnan(out)] = 0 return out ...
nan_to_num nan_to_num_ nanmedian nanquantile nansum narrow narrow_copy native_batch_norm native_group_norm native_layer_norm native_norm ne neg neg_ negative negative_ nextafter nn no_grad nonzero norm norm_except_dim normal not_equal nuclear_norm numel nvtoolsext_dll_path ones ones_like ...
>>> a = torch.randn(4) >>> a -0.6366 0.2718 0.4469 1.3122 [torch.FloatTensor of size 4] >>> torch.acos(a) 2.2608 1.2956 1.1075 nan [torch.FloatTensor of size 4]torch.add()torch.add(input, value, out=None)对输入张量input逐元素加上标量值value,并返回结果到一个新的张量out,即 \( ...