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Environment to train neural networks with backpropagation algorithms, including new weight compression algorithm. Software is also useful for developing new algorithms.
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NaHenn/nn_trainer_wc
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10/20/2017 James S. Smith, Auburn University Bo Wu, Auburn University Bogdan M. Wilamowski, PhD, Auburn University *main.m - train neural network with single run *parameter_sweep - run parameter sweep on training parameters to find best solution *demo_0 - shows nbn weight compression improvements compared to nbn on small parity-7 dataset *demo_1 - shows nbn weight compression improvements compared to nbn on spirals dataset *demo_2 - shows nbn weight compression improvements compared to nbn on ELEC 6240 final project Fall 2017 *demo_3 - shows nbn weight compression improvements compared to nbn on Boston housing market data Instructions for each file are included within file. User input only needed in "User Input" section.
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Environment to train neural networks with backpropagation algorithms, including new weight compression algorithm. Software is also useful for developing new algorithms.
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