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+endstartsteps1,,start+(steps2)endstartsteps1,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.

  • start (float) – the starting value for the set of points

  • end (float) – the ending value for the set of points

  • steps (int) – size of the constructed tensor

Keyword Arguments
  • out (Tensor, optional) – the output tensor.

  • dtype (torch.dpython:type, 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_tensor_type()). 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.


>>> 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)


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