torch_rand_like

(Tensor) the size of <code>input</code> will determine size of the output tensor.

input

(<code>torch.dtype</code>, optional) the desired data type of returned Tensor. Default: if <code>None</code>, defaults to the dtype of <code>input</code>.

dtype

(<code>torch.layout</code>, optional) the desired layout of returned tensor. Default: if <code>None</code>, defaults to the layout of <code>input</code>.

layout

(<code>torch.device</code>, optional) the desired device of returned tensor. Default: if <code>None</code>, defaults to the device of <code>input</code>.

device

(bool, optional) If autograd should record operations on the returned tensor. Default: <code>False</code>.

requires_grad

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

memory_format

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

 RStudio

torch_rand_like function

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

torch_rand_like: Rand_like

Description

Arguments

rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor

Description

Arguments

rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor

rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor