1 / 10

Neural Network training

Neural Network training. Morten Nielsen, CBS, BioCentrum, DTU. Neural network programs. How Classification neural network Howlin Real value neural network Nnlinplayer Neural network player i.e. no training. How2doit. How and howlin clumsy but very fast and efficient Fortran programs

uma
Download Presentation

Neural Network training

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Neural Network training Morten Nielsen, CBS, BioCentrum, DTU

  2. Neural network programs • How • Classification neural network • Howlin • Real value neural network • Nnlinplayer • Neural network player i.e. no training

  3. How2doit • How and howlin clumsy but very fast and efficient Fortran programs • Three important files • Parameter file; howlin.dat • Data file • Synaps (weight) file

  4. Output • Format of output file • Plotting training and test performance • howlinplot fileout

  5. Neural networks • Neural networks can learn higher order correlations! • What does this mean? 0 0 => 0 0 1 => 1 1 0 => 1 1 1 => 0 No linear function can learn this pattern

  6. Neural networks w21 w12 w11 w22 v1 v2 w11=1, w12=-1 w21=1 w22=-1 V1 = 0.5 v2= -0.5

  7. nnlinplayer • Use weight file(s) to generate neural network predictions • Format • nnlinplayer synapsfilelistinputfile • Makes consensus prediction over N neural networks • Input file must be generated separately • seq2inp data • Using pipes • seq2inp data | nnlinplayet synlist --

  8. how • Classification network • Generates input data directly from sequence • RIISSIEQKEENKGGEDKLKMIREYRQMVE • Input is how files

  9. Useful programs • fasta2pep • seq2inp • ranlines • splitfile • balanceset • xycorr • Examples fasta2pep ex.fsa | grep -v # | seq2inp -- | grep -v # | ranlines -- | grep -v # | splitfile -nc 4 -- seq2inp data | nnlinplayer synlist -- | grep -v # | args 1,3 | xycorr

  10. Exercises • Copy all files from • /usr/opt/www/pub/CBS/researchgroups/immunology/intro/NeuralNetworks/exercise/* to some directory • Open the file doit • What does the program do? • Run the program and save the output to a file named datafile • Make a howlin neural network training • Set the number of hidden neurons in the howlin2002.dat file to 0 • Run the training typing • howlin2002 < howlin2002.dat > output • Plot the training/test performance using the howlinplot program • Redo the training using 2 hidden neurons • Check the synaps file. What are the weight values? • Do the prediction of T cell epitopes exercise www.cbs.dtu.dk/courses/27485.imm/exercise5/index.php

More Related