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torch

torch_rand_like

Rand_like

Description

Rand_like

Usage

torch_rand_like(
  input,
  dtype = NULL,
  layout = torch_strided(),
  device = NULL,
  requires_grad = FALSE,
  memory_format = torch_preserve_format()
)

Arguments

input

(Tensor) the size of input will determine size of the output tensor.
dtype

(torch.dtype, optional) the desired data type of returned Tensor. Default: if NULL, defaults to the dtype of input.
layout

(torch.layout, optional) the desired layout of returned tensor. Default: if NULL, defaults to the layout of input.
device

(torch.device, optional) the desired device of returned tensor. Default: if NULL, defaults to the device of input.
requires_grad

(bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.
memory_format

(torch.memory_format, optional) the desired memory format of returned Tensor. Default: torch_preserve_format.

rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor

Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0, 1). torch_rand_like(input) is equivalent to torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0
MIT + file LICENSE
Authors
Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]
Initial release

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