Randn — torch_randn • torch

Randn

Usage

torch_randn(
  ...,
  names = NULL,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE
)

Arguments

...: (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.
names: optional names for the dimensions
dtype: (torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type).
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 NULL, 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.

randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=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).

$$ \mbox{out}_{i} \sim \mathcal{N}(0, 1) $$ The shape of the tensor is defined by the variable argument size.

Examples

if (torch_is_installed()) {

torch_randn(c(4))
torch_randn(c(2, 3))
}
#> torch_tensor
#>  1.0960 -1.3672  0.6847
#> -0.6942 -0.0322 -0.4413
#> [ CPUFloatType{2,3} ]