Jun 09, 2019 · I want to convert this to one hot encoded tensor, using the nn.fucntional.one_hot function. n = 24 one_hot = torch.nn.functional.one_hot (indices, n) but this expects a tensor of indices, honestly, I am not sure how to get those. The only tensor I have is the label tensor of the shape described above and it contains values ranging from 1-24 ...
23.07.2021 · RuntimeError: one_hot is only applicable to index tensor. #454. Unanswered. talhaanwarch asked this question in Q&A. RuntimeError: one_hot is only applicable to index tensor. #454. talhaanwarch. Jul 23, 2021 · 1 answers ...
26.08.2019 · n = 24 one_hot = torch.nn.functional.one_hot(indices, n) 但是这需要一个指数的张量，老实说，我不知道如何获得这些指数.我唯一的张量是上面描述的形状的标签张量，它包含1-24 ... File "<stdin>", line 1, in <module> RuntimeError: one_hot is only applicable to index tensor.
14.10.2020 · Tensor one_hot(const Tensor &self, int64_t num_classes) { **TORCH_CHECK(self.dtype() == kLong, "one_hot is only applicable to index tensor.");** The SampleBatch reader defaults to int32, so my action list ends up looking list this
08.06.2019 · I want to convert this to one hot encoded tensor, using the nn.fucntional.one_hot function. n = 24 one_hot = torch.nn.functional.one_hot (indices, n) but this expects a tensor of indices, honestly, I am not sure how to get those. The only tensor I have is the label tensor of the shape described above and it contains values ranging from 1-24 ...
03.12.2020 · 首先，报错原因，我认为是数据类型错误， 在文档中表示， 第一个tensor参数的数据类型为LongTensor，也就是torch.int64类型 的，如果你有报这个错：“one_hot is only applicable to index tensor”，可以查看一下你传入的参数是不是int32或者其他类型的，如果是的话，强制类型转换更改一下就好了，也就是说 改成int64的。 例如下面的代码：第一行进行了强制类型转换，后面就不报错了 …
Oct 14, 2020 · sven1977 changed the title "RuntimeError: one_hot is only applicable to index tensor" in dqn_torch_policy using offline sample batches [RLlib] "RuntimeError: one_hot is only applicable to index tensor" in dqn_torch_policy using offline sample batches Nov 4, 2020
In calltorch.nn.functional.one_hot()At the time. RuntimeError: one_hot is only applicable to index tensor the reason: torch.from_numpy(np.array([],dtype=np.int32)) There is such a sentence in my code, using from_numpy converted Array is specified by Dype, which transformed Tensor, Pytorch does not build indexes. solve: Remove DTYPE
Nov 19, 2021 · RuntimeError: one_hot is only applicable to index tensor. pytorch one-hot-encoding. Share. Follow asked Nov 19 '21 at 6:09. Code_B Code_B. 31 2 2 bronze badges.
Traceback (most recent call last): File "<stdin>", line 1, in <module> RuntimeError: one_hot is only applicable to index tensor. たぶんあなたはただ変換する必要があります int64 ：
Dec 03, 2020 · 在文档中表示，第一个tensor参数的数据类型为LongTensor，也就是torch.int64类型的，如果你有报这个错：“one_hot is only applicable to index tensor”，可以查看一下你传入的参数是不是int32或者其他类型的，如果是的话，强制类型转换更改一下就好了，也就是说改成int64的。
torch.nn.functional. one_hot (tensor, num_classes=- 1) → LongTensor. Takes LongTensor with index values of shape (*) and returns a tensor of shape (* ...
problem: In calltorch.nn.functional.one_hot()At the time. RuntimeError: one_hot is only applicable to index tensor the reason: torch.from_numpy(np.array([],dtype=np.int32)) There is such a sentence in my code, using from_numpy converted Array is specified by Dype, which transformed Tensor, Pytorch does not build indexes
PyTorch has a one_hot() function for converting class indices to one-hot encoded targets: import torch import torch.nn.functional as F x = torch.tensor([4, ...
02.02.2021 · If you don’t pass the num_classes argument in, one_hot() will infer the number of classes to be the largest class index plus one.. If you have more than one dimension in your class index tensor, one_hot() will encode labels along the last axis:
23.11.2021 · # First look at the size of the incoming tensor, the incoming tensor's dimension is not enough, then you need to add dimensionality # Here the size of the labels is tensor([n]), pass in the need to add dimensionality # If the above problem occurs, just add to.(torch.int64) at the end to solve it # n is the kind to be divided labels = …
Nov 23, 2021 · # First look at the size of the incoming tensor, the incoming tensor's dimension is not enough, then you need to add dimensionality # Here the size of the labels is tensor([n]), pass in the need to add dimensionality # If the above problem occurs, just add to.(torch.int64) at the end to solve it # n is the kind to be divided labels = torch.nn.functional.one_hot(labels.unsqueeze(0).to(torch ...