CRAN

autoencoder 1.1

Sparse Autoencoder for Automatic Learning of Representative Features from Unlabeled Data

Released Jul 2, 2015 by Yuriy Tyshetskiy

Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>autoencoder</artifactId>
    <version>1.1-b36</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:autoencoder')

Test Results

This package was last tested against Renjin 0.9.2644 on Jun 1, 2018.

• autoencode-examples
• autoencoder-package-examples
• autoencoder_Ninput=100_Nhidden=100_rho=1e-2-examples
• autoencoder_Ninput=100_Nhidden=25_rho=1e-2-examples
• predict.autoencoder-examples
• training_matrix_N=1e4_Ninput=100-examples
• visualize.hidden.units-examples

Source

View GitHub Mirror

Release History

• 1.1
• 1.0