torch.range#

torch.range(start=0,end,step=1,*,out=None,dtype=None,layout=torch.strided,device=None,requires_grad=False)→Tensor#

Returns a 1-D tensor of size$\lfloor \frac{\text{end}-\text{start}}{\text{step}}\rfloor +1$⌊stepend−start​⌋+1with values fromstart toend with stepstep. Step isthe gap between two values in the tensor.

$${\text{out}}_{i+1}={\text{out}}_i+\text{step}\mathrm{.}$$outi+1​=outi​+step.

Warning

This function is deprecated and will be removed in a future release because its behavior is inconsistent withPython’s range builtin. Instead, usetorch.arange(), which produces values in [start, end).

Parameters

• start (float,optional) – the starting value for the set of points. Default:0.

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

• step (float,optional) – the gap between each pair of adjacent points. Default:1.

Keyword Arguments

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

• dtype (torch.dtype, optional) – the desired data type of returned tensor.Default: ifNone, uses a global default (seetorch.set_default_dtype()). Ifdtype is not given, infer the data type from the other inputarguments. If any ofstart,end, orstep are floating-point, thedtype is inferred to be the default dtype, seeget_default_dtype(). Otherwise, thedtype is inferred tobetorch.int64.

• 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.range(1,4)tensor([ 1.,  2.,  3.,  4.])>>>torch.range(1,4,0.5)tensor([ 1.0000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  4.0000])