Constructs a tensor with no autograd history (also known as a “leaf tensor”, see Autograd mechanics) by copying data
.
data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray
, scalar, and other types.
dtype (torch.dtype
, optional) – the desired data type of returned tensor. Default: if None
, infers data type from data
.
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
.
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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