make_generator

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

make_generator function

<dl><dt>input_x</dt>
<dd>list of matrices</dd>
<dt>input_y</dt>
<dd>list of matrix</dd>
<dt>batch_size</dt>
<dd>integer</dd>
<dt>sizes</dt>
<dd>sizes of the image including colour channel</dd>
<dt>shuffle</dt>
<dd>logical for shuffling data</dd>
<dt>seed</dt>
<dd>seed for shuffling in generators</dd></dl>

Arguments

creates a generator for training — make_generator

<dl>

<dt>input_x</dt>
<dd>list of matrices</dd>


<dt>input_y</dt>
<dd>list of matrix</dd>


<dt>batch_size</dt>
<dd>integer</dd>


<dt>sizes</dt>
<dd>sizes of the image including colour channel</dd>


<dt>shuffle</dt>
<dd>logical for shuffling data</dd>


<dt>seed</dt>
<dd>seed for shuffling in generators</dd>

</dl>

creates a generator for training

make_generator: creates a generator for training

Description

Usage

Value

Arguments