CRAN

TeachNet 0.7.1

Fits Neural Networks to Learn About Backpropagation

Released Nov 27, 2018 by Georg Steinbuss

This package is available for Renjin and there are no known compatibility issues.

Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.

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>TeachNet</artifactId>
    <version>0.7.1-b1</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:TeachNet')

Test Results

This package was last tested against Renjin 0.9.2716 on Dec 19, 2018.

Source

R

View GitHub Mirror

Release History