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# torch.linspace

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−start steps−1,…,start+(steps−2)∗end−start steps−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}) (start,start+steps−1 end−start​,…,start+(steps−2)∗steps−1 end−start​,end)

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

  Parameters:

    - 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.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_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`.

  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.])

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