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torch.tensor
torch.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor
-
Constructs a tensor with no autograd history (also known as a “leaf tensor”, see Autograd mechanics) by copying
data
.Warning
When working with tensors prefer using
torch.Tensor.clone()
,torch.Tensor.detach()
, andtorch.Tensor.requires_grad_()
for readability. Lettingt
be a tensor,torch.tensor(t)
is equivalent tot.clone().detach()
, andtorch.tensor(t, requires_grad=True)
is equivalent tot.clone().detach().requires_grad_(True)
.See also
torch.as_tensor()
preserves autograd history and avoids copies where possible.torch.from_numpy()
creates a tensor that shares storage with a NumPy array.- 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. Default: ifNone
, infers data type fromdata
. - device (
torch.device
, optional) – the device of the constructed tensor. If None and data is a tensor then the device of data is used. If None and data is not a tensor then the result tensor is constructed on the current device. - requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
False
. - pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default:
False
.
- dtype (
Example:
>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]]) tensor([[ 0.1000, 1.2000], [ 2.2000, 3.1000], [ 4.9000, 5.2000]]) >>> torch.tensor([0, 1]) # Type inference on data tensor([ 0, 1]) >>> torch.tensor([[0.11111, 0.222222, 0.3333333]], ... dtype=torch.float64, ... device=torch.device('cuda:0')) # creates a double tensor on a CUDA device tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') >>> torch.tensor(3.14159) # Create a zero-dimensional (scalar) tensor tensor(3.1416) >>> torch.tensor([]) # Create an empty tensor (of size (0,)) tensor([])
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