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.
- 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
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