nn_utils_clip_grad_norm_

The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.

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

nn_utils_clip_grad_norm_ function

<dl><dt>parameters</dt>
<dd>(IterableTensor or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized</dd>
<dt>max_norm</dt>
<dd>(float or int): max norm of the gradients</dd>
<dt>norm_type</dt>
<dd>(float or int): type of the used p-norm. Can be <code>Inf</code> for
infinity norm.</dd></dl>

Arguments

Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_

<dl>

<dt>parameters</dt>
<dd>(IterableTensor or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized</dd>


<dt>max_norm</dt>
<dd>(float or int): max norm of the gradients</dd>


<dt>norm_type</dt>
<dd>(float or int): type of the used p-norm. Can be <code>Inf</code> for
infinity norm.</dd>

</dl>

nn_utils_clip_grad_norm_: Clips gradient norm of an iterable of parameters.

Description

Usage

Value

Arguments