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 sizesteps whose values are evenlyspaced fromstart toend, inclusive. That is, the value are:

$$(\text{start},\text{start}+\frac{\text{end}-\text{start}}{\text{steps}-1},\dots ,\text{start}+(\text{steps}-2)\ast \frac{\text{end}-\text{start}}{\text{steps}-1},\text{end})$$(start,start+steps−1end−start​,…,start+(steps−2)∗steps−1end−start​,end)

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

Parameters

• start (float orTensor) – the starting value for the set of points. IfTensor, it must be 0-dimensional

• end (float orTensor) – the ending value for the set of points. IfTensor, 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 bothstart andend 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: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool,optional) – If autograd should record operations on thereturned 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.])