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torch.logspace#

torch.logspace(start,end,steps,base=10.0,*,out=None,dtype=None,layout=torch.strided,device=None,requires_grad=False)→Tensor#

Creates a one-dimensional tensor of sizesteps whose values are evenlyspaced frombasestart{{\text{{base}}}}^{{\text{{start}}}} tobaseend{{\text{{base}}}}^{{\text{{end}}}}, inclusive, on a logarithmic scalewith basebase. That is, the values are:

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

From PyTorch 1.11 logspace 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

  • base (float,optional) – base of the logarithm function. Default:10.0.

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.logspace(start=-10,end=10,steps=5)tensor([ 1.0000e-10,  1.0000e-05,  1.0000e+00,  1.0000e+05,  1.0000e+10])>>>torch.logspace(start=0.1,end=1.0,steps=5)tensor([  1.2589,   2.1135,   3.5481,   5.9566,  10.0000])>>>torch.logspace(start=0.1,end=1.0,steps=1)tensor([1.2589])>>>torch.logspace(start=2,end=2,steps=1,base=2)tensor([4.0])