torch.linspace — PyTorch 2.7 documentation (original) (raw)

torch.linspace(start, end, steps, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶

Creates a one-dimensional tensor of size steps whose values are evenly spaced from start to end, inclusive. That is, the value are:

(start,start+end−startsteps−1,…,start+(steps−2)∗end−startsteps−1,end)(\text{start}, \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, \ldots, \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, \text{end})

From PyTorch 1.11 linspace requires the steps argument. Use steps=100 to restore the previous behavior.

Parameters

• start (float or Tensor) – the starting value for the set of points. If Tensor, it must be 0-dimensional
• end (float or Tensor) – the ending value for the set of points. If Tensor, it must be 0-dimensional
• steps (int) – size of the constructed tensor

Keyword Arguments

• out (Tensor, optional) – the output tensor.
• dtype (torch.dtype, optional) – the data type to perform the computation in. Default: if None, uses the global default dtype (see torch.get_default_dtype()) when both start and end are real, and corresponding complex dtype when either is complex.
• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

torch.linspace(3, 10, steps=5) tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000]) torch.linspace(-10, 10, steps=5) tensor([-10., -5., 0., 5., 10.]) torch.linspace(start=-10, end=10, steps=5) tensor([-10., -5., 0., 5., 10.]) torch.linspace(start=-10, end=10, steps=1) tensor([-10.])