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 endstartstep+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 with values from start to end with step step. Step is the gap between two values in the tensor.

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


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

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

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

  • step (float) – 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: if None, uses a global default (see torch.set_default_tensor_type()). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see get_default_dtype(). Otherwise, the dtype is inferred to be torch.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: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). 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.


>>> 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])


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