choose_kernel_initializer_torch

Function to choose a kernel initializer for a torch layer

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

choose_kernel_initializer_torch function

<dl><dt>kernel_initializer</dt>
<dd>string; initializer</dd>
<dt>value</dt>
<dd>numeric; value used for a constant initializer</dd></dl>

Arguments

Function to choose a kernel initializer for a torch layer — choose_kernel_initializer_torch

<dl>

<dt>kernel_initializer</dt>
<dd>string; initializer</dd>


<dt>value</dt>
<dd>numeric; value used for a constant initializer</dd>

</dl>

choose_kernel_initializer_torch: Function to choose a kernel initializer for a torch layer

Description

Usage

Value

Arguments