nn_init_no_grad_constant_deepreg

custom nn_linear module to overwrite reset_parameters
# nn_init_constant works only if value is scalar; so warmstarts for gam does'not work

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

nn_init_no_grad_constant_deepreg function

<dl><dt>tensor</dt>
<dd>scalar or vector</dd>
<dt>value</dt>
<dd>value used for constant initialization</dd></dl>

Arguments

custom nn_linear module to overwrite reset_parameters
# nn_init_constant works only if value is scalar; so warmstarts for gam does'not work — nn_init_no_grad_constant_deepreg

<dl>

<dt>tensor</dt>
<dd>scalar or vector</dd>


<dt>value</dt>
<dd>value used for constant initialization</dd>

</dl>

nn_init_no_grad_constant_deepreg: custom nn_linear module to overwrite reset_parameters # nn_init_constant works only if value is scalar; so warmstarts for gam does'not work

Description

Usage

Value

Arguments