avgPool

creates a 'avgPool' 'citolayer' object that is used by <code>create_architecture</code>.

The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems.  DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package.

Maximilian Pichler

cito

Building and Training Neural Networks

Christian Amesöder

Florian Hartig

Armin Schenk

avgPool function

<dl><dt>kernel_size</dt>
<dd>(int or tuple) size of the kernel in this layer. Use a tuple if the kernel size isn't equal in all dimensions</dd>
<dt>stride</dt>
<dd>(int or tuple) stride of the kernel in this layer. NULL sets the stride equal to the kernel size. Use a tuple if the stride isn't equal in all dimensions</dd>
<dt>padding</dt>
<dd>(int or tuple) zero-padding added to both sides of the input. Use a tuple if the padding isn't equal in all dimensions</dd></dl>

Arguments

Author

Average pooling layer — avgPool

<dl>

<dt>kernel_size</dt>
<dd>(int or tuple) size of the kernel in this layer. Use a tuple if the kernel size isn't equal in all dimensions</dd>


<dt>stride</dt>
<dd>(int or tuple) stride of the kernel in this layer. NULL sets the stride equal to the kernel size. Use a tuple if the stride isn't equal in all dimensions</dd>


<dt>padding</dt>
<dd>(int or tuple) zero-padding added to both sides of the input. Use a tuple if the padding isn't equal in all dimensions</dd>

</dl>

avgPool: Average pooling layer

Description

Usage

Value

Arguments

Author

Details

See Also