get_layernr_trainable

Function to return layer numbers with trainable weights

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

get_layernr_trainable function

<dl><dt>mod</dt>
<dd>deepregression model</dd>
<dt>logic</dt>
<dd>logical; TRUE: return logical vector; FALSE (default) index</dd></dl>

Arguments

Function to return layer numbers with trainable weights — get_layernr_trainable

<dl>

<dt>mod</dt>
<dd>deepregression model</dd>


<dt>logic</dt>
<dd>logical; TRUE: return logical vector; FALSE (default) index</dd>

</dl>

get_layernr_trainable: Function to return layer numbers with trainable weights

Description

Usage

Arguments