tensorplay.functional.linspace

tensorplay.functional.linspace(start, end, steps, *, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False) → Tensor[source]

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

$$(\text{start}, \text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1}, \ldots, \text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1}, \text{end})$$

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 (tensorplay.dtype, optional) – the data type to perform the computation in. Default: if None, uses the global default dtype (see tensorplay.get_default_dtype()) when both start and end are real, and corresponding complex dtype when either is complex.

• layout (tensorplay.layout, optional) – the desired layout of returned Tensor. Default: tensorplay.strided.

• device (tensorplay.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see tensorplay.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:

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