torch.numel(input) → int
Returns the total number of elements in the input tensor. Document
torch.from_numpy(ndarray) → Tensor
Creates a Tensor from a numpy.ndarray.
torch.range(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
torch.range(1, 4) -> tensor([ 1., 2., 3., 4.])
torch.heaviside(input, values, *, out=None) → Tensor
>>> values = torch.tensor([0.5])
>>> torch.heaviside(input, values)
tensor([0.0000, 0.5000, 1.0000])
>>> values = torch.tensor([1.2, -2.0, 3.5])
>>> torch.heaviside(input, values)
tensor([0., -2., 1.])
torch.cat
torch.chunk
torch.stack
torch.gather
torch.index_select
torch.masked_select
torch.narrow(input, dim, start, length) → Tensor
torch.split(tensor, split_size_or_sections, dim=0)
torch.t
torch.take(input, index)
torch.transpose(input, dim0, dim1)
torch.unbind(input, dim=0)
torch.unsqueeze(input, dim)
torch.where(condition, x, y) → Tensor
>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620, 0.3139],
[ 0.3898, -0.7197],
[ 0.0478, -0.1657]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000, 0.3139],
[ 0.3898, 1.0000],
[ 0.0478, 1.0000]])
>>> x = torch.randn(2, 2, dtype=torch.double)
>>> x
tensor([[ 1.0779, 0.0383],
[-0.8785, -1.1089]], dtype=torch.float64)
>>> torch.where(x > 0, x, 0.)
tensor([[1.0779, 0.0383],
[0.0000, 0.0000]], dtype=torch.float64)
數學運算
abs/absolute | acos/arccos | add | bitwise_not | bitwise_and | bitwise_or | bitwise_xor | ceil | clamp/clip | div/divide | exp
| trunk/fix | floor | fmod | logical_and/logical_or/logical_xor | mul/multiply | lerp | neg/negative | pow | round | sign | sqrt |...
其他
隨機數生成器