nnf_softmin

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

Christophe Regouby

Krzysztof Joachimiak

Hamada S. Badr

 RStudio

nnf_softmin function

<dl><dt>input</dt>
<dd>(Tensor) input</dd>
<dt>dim</dt>
<dd>(int) A dimension along which softmin will be computed
(so every slice along dim will sum to 1).</dd>
<dt>dtype</dt>
<dd>(<code>torch.dtype</code>, optional) the desired data type of returned tensor. If specified, the input tensor is casted to <code>dtype</code> before the operation is performed.
This is useful for preventing data type overflows. Default: NULL.</dd></dl>

Arguments

Softmin — nnf_softmin

<dl>

<dt>input</dt>
<dd>(Tensor) input</dd>


<dt>dim</dt>
<dd>(int) A dimension along which softmin will be computed
(so every slice along dim will sum to 1).</dd>


<dt>dtype</dt>
<dd>(<code>torch.dtype</code>, optional) the desired data type of returned tensor. If specified, the input tensor is casted to <code>dtype</code> before the operation is performed.
This is useful for preventing data type overflows. Default: NULL.</dd>

</dl>

Softmin

nnf_softmin: Softmin

Description

Usage

Arguments

Details