Nov 27, 2019 · And keep track that PyTorch can create tensors by data and by dimension. import torch # by data t = torch.tensor ( [1., 1.]) # by dimension t = torch.zeros (2,2) Your case was to create tensor by data which is a scalar: t = torch.tensor (1) . But this also is a scalar: t = torch.tensor ( [1]) imho because it has a size and no direction.
26.11.2019 · And keep track that PyTorch can create tensors by data and by dimension. import torch # by data t = torch.tensor ( [1., 1.]) # by dimension t = torch.zeros (2,2) Your case was to create tensor by data which is a scalar: t = torch.tensor (1) . But this also is a scalar: t = torch.tensor ( [1]) imho because it has a size and no direction.
This question refers to latest version in master branch. When we define a Tensor object, what is the best way to retrieve one of element as scalar value ? x ...
11.04.2018 · When we define a Tensor object, what is the best way to retrieve one of element as scalar value ? x = torch.Tensor([2, 3]) x.data[0] still returns Tensor type x.numpy()[0] gives scalar value, but with type numpy.int64 which sometimes leads to problems x.tolist()[0] returns int type. Seems for now tolist() works well.
27.08.2019 · Hi, I need to know what is the best way (i.e. most efficient) to append a scalar value (i.e. tensor with empty size) to a tensor with multidimensional shape. I tried torch.cat and torch.stack but this requires the dimensions to be matched. I could use unsqueeze to the scalar value but I wonder if there is a better solution. Thanks
Apr 11, 2018 · This question refers to latest version in master branch. When we define a Tensor object, what is the best way to retrieve one of element as scalar value ? x = torch.Tensor([2, 3]) x.data[0] still returns Tensor type x.numpy()[0] gives scalar value, but with type numpy.int64 which sometimes leads to problems x.tolist()[0] returns int type. Seems for now tolist() works well. The question is, why ...
torch.Tensor.to — PyTorch 1.10.0 documentation torch.Tensor.to Tensor.to(*args, **kwargs) → Tensor Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to (*args, **kwargs). Note If the self Tensor already has the correct torch.dtype and torch.device, then self is returned.
04.05.2019 · Imagine I have a scalar T , this T is gonna be used as a threshold in my network. i.e. TensorA = torch.where(TensorB > T*Means, Ones, Zeros). Right now I have T = torch.tensor(1.0), but I want to give it the ability to…
How do I convert a PyTorch Tensor into a python list? ... Usually, the output is scalar and hence the scalar is passed to backward as a default choice.
25.05.2020 · IIRC, "Scalar"s are handled in specialized ops in c++, so they probably just end up as arguments to cuda kernel functions. And cuda automatically copies kernel arguments (pointers & scalars) to gpu. So maybe scalars are marginally faster than buffers, not sure.
Apr 28, 2018 · Hi, for Pytorch 0.4, it introduces a new scalar torch.tensor() with dim 0. I feel confused since all the function of scalar tensor can be replaced by dim=1 Tensor(1). Why need another new type making more complex for …
A tensor can be constructed from a Python list or sequence using the torch.tensor () constructor: torch.tensor () always copies data. If you have a Tensor data and just want to change its requires_grad flag, use requires_grad_ () or detach () to avoid a copy.
Parameters. data (array_like) – Initial data for the tensor.Can be a list, tuple, NumPy ndarray, scalar, and other types.. Keyword Arguments. dtype (torch.dtype, optional) – the desired data type of returned tensor.