torch_randint_like

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <doi:10.48550/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

Christophe Regouby

Krzysztof Joachimiak

Hamada S. Badr

Sebastian Fischer

Maximilian Pichler

 RStudio

torch_randint_like function

<dl><dt>input</dt>
<dd>(Tensor) the size of <code>input</code> will determine size of the output tensor.</dd>
<dt>low</dt>
<dd>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</dd>
<dt>high</dt>
<dd>(int) One above the highest integer to be drawn from the distribution.</dd>
<dt>dtype</dt>
<dd>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor. Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</dd>
<dt>layout</dt>
<dd>(<code>torch.layout</code>, optional) the desired layout of returned tensor. Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</dd>
<dt>device</dt>
<dd>(<code>torch.device</code>, optional) the desired device of returned tensor. Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</dd>
<dt>requires_grad</dt>
<dd>(bool, optional) If autograd should record operations on the returned tensor. Default: <code>FALSE</code>.</dd></dl>

Arguments

memory_format=torch.preserve_format) -&gt; Tensor
Returns a tensor with the same shape as Tensor <code>input</code> filled with
random integers generated uniformly between <code>low</code> (inclusive) and
<code>high</code> (exclusive).
.. note:
With the global dtype default (<code>torch_float32</code>), this function returns
a tensor with dtype <code>torch_int64</code>.

randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, 

Randint_like — torch_randint_like

<dl>

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


<dt>low</dt>
<dd>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</dd>


<dt>high</dt>
<dd>(int) One above the highest integer to be drawn from the distribution.</dd>


<dt>dtype</dt>
<dd>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor. Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</dd>


<dt>layout</dt>
<dd>(<code>torch.layout</code>, optional) the desired layout of returned tensor. Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</dd>


<dt>device</dt>
<dd>(<code>torch.device</code>, optional) the desired device of returned tensor. Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</dd>


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

</dl>

memory_format=torch.preserve_format) -&gt; Tensor
Returns a tensor with the same shape as Tensor <code>input</code> filled with
random integers generated uniformly between <code>low</code> (inclusive) and
<code>high</code> (exclusive).
.. note:
With the global dtype default (<code>torch_float32</code>), this function returns
a tensor with dtype <code>torch_int64</code>.

torch_randint_like: Randint_like

Description

Usage

Arguments

randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,