BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize and interact in the learning process of a Multi-Layer Perceptron on tasks which have a 2D character. Tasks like the famous two-spirals task or classification of satellite image data.

Features

  • Reset of weights connected to a neuron
  • Freeze/Unfreeze of weights connected to a neuron
  • Basic support for scaling of RFPs, preview of RFP in its native size
  • Preview of conflicts between output layer neurons
  • Preview of error energy on output layer
  • Preview of Training/Testing patterns
  • Sigmoidal/Softmax activation on output layer
  • Logging of MSE and of the sum of output layer responses
  • Perl script to prepare bnns patterns from PGM data
  • Perl script to prepare bnns patterns from Boston Remote Sensing Testbed data
  • Maintained User Manual

Project Samples

Project Activity

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License

GNU General Public License version 3.0 (GPLv3)

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Additional Project Details

Operating Systems

BSD, Cygwin, Linux

Languages

English

Intended Audience

Science/Research

User Interface

Console/Terminal, X Window System (X11)

Programming Language

C, Perl

Related Categories

Perl Data Visualization Software, Perl Artificial Intelligence Software, Perl Machine Learning Software, C Data Visualization Software, C Artificial Intelligence Software, C Machine Learning Software

Registered

2009-12-18