autoenc_e

Creates an deep learning autoencoder to encode a sequence of observations.
It wraps the pytorch and reticulate libraries.

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Eduardo Ogasawara

daltoolbox

Leveraging Experiment Lines to Data Analytics

Antonio Castro

Janio Lima

Lucas Tavares

Diego Carvalho

Eduardo Bezerra

Rafaelli Coutinho

Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) 

autoenc_e function

<dl><dt>input_size</dt>
<dd>input size</dd>
<dt>encoding_size</dt>
<dd>encoding size</dd>
<dt>batch_size</dt>
<dd>size for batch learning</dd>
<dt>num_epochs</dt>
<dd>number of epochs for training</dd>
<dt>learning_rate</dt>
<dd>learning rate</dd></dl>

Arguments

Autoencoder - Encode — autoenc_e

<dl>

<dt>input_size</dt>
<dd>input size</dd>


<dt>encoding_size</dt>
<dd>encoding size</dd>


<dt>batch_size</dt>
<dd>size for batch learning</dd>


<dt>num_epochs</dt>
<dd>number of epochs for training</dd>


<dt>learning_rate</dt>
<dd>learning rate</dd>

</dl>

autoenc_e: Autoencoder - Encode

Description

Usage

Value

Arguments

Examples