torch.randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) Tensor

Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).

outiN(0,1)\text{out}_{i} \sim \mathcal{N}(0, 1)

For complex dtypes, the tensor is i.i.d. sampled from a complex normal distribution with zero mean and unit variance as

outiCN(0,1)\text{out}_{i} \sim \mathcal{CN}(0, 1)

This is equivalent to separately sampling the real (Re)(\operatorname{Re}) and imaginary (Im)(\operatorname{Im}) part of outi\text{out}_i as

Re(outi)N(0,12),Im(outi)N(0,12)\operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2})

The shape of the tensor is defined by the variable argument size.


size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.

Keyword Arguments
  • generator (torch.Generator, optional) – a pseudorandom number generator for sampling

  • 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_dtype()).

  • 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_device()). 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.

  • pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.


>>> torch.randn(4)
tensor([-2.1436,  0.9966,  2.3426, -0.6366])
>>> torch.randn(2, 3)
tensor([[ 1.5954,  2.8929, -1.0923],
        [ 1.1719, -0.4709, -0.1996]])


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