Rate this Page
★★★★★

torch.arange#

torch.arange(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⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceilwith values from the interval[start,end) taken with common differencestep beginning fromstart.

Note: When using floating-point dtypes (especially reduced precision types likebfloat16),the results may be affected by floating-point rounding behavior. Some values in the sequencemight not be exactly representable in certain floating-point formats, which can lead torepeated values or unexpected rounding. For precise sequences, it is recommended to useinteger dtypes instead of floating-point dtypes.

Note that non-integerstep is subject to floating point rounding errors whencomparing againstend; to avoid inconsistency, we advise subtracting a small epsilon fromendin such cases.

outi+1=outi+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step}
Parameters

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

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

  • step (Number,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, orstop 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.arange(5)tensor([ 0,  1,  2,  3,  4])>>>torch.arange(1,4)tensor([ 1,  2,  3])>>>torch.arange(1,2.5,0.5)tensor([ 1.0000,  1.5000,  2.0000])