BPnnet.H

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00001 /*!@file BPnnet/BPnnet.H header for Back Prop Neural Net */
00002 
00003 // //////////////////////////////////////////////////////////////////// //
00004 // The iLab Neuromorphic Vision C++ Toolkit - Copyright (C) 2001 by the //
00005 // University of Southern California (USC) and the iLab at USC.         //
00006 // See http://iLab.usc.edu for information about this project.          //
00007 // //////////////////////////////////////////////////////////////////// //
00008 // Major portions of the iLab Neuromorphic Vision Toolkit are protected //
00009 // under the U.S. patent ``Computation of Intrinsic Perceptual Saliency //
00010 // in Visual Environments, and Applications'' by Christof Koch and      //
00011 // Laurent Itti, California Institute of Technology, 2001 (patent       //
00012 // pending; application number 09/912,225 filed July 23, 2001; see      //
00013 // http://pair.uspto.gov/cgi-bin/final/home.pl for current status).     //
00014 // //////////////////////////////////////////////////////////////////// //
00015 // This file is part of the iLab Neuromorphic Vision C++ Toolkit.       //
00016 //                                                                      //
00017 // The iLab Neuromorphic Vision C++ Toolkit is free software; you can   //
00018 // redistribute it and/or modify it under the terms of the GNU General  //
00019 // Public License as published by the Free Software Foundation; either  //
00020 // version 2 of the License, or (at your option) any later version.     //
00021 //                                                                      //
00022 // The iLab Neuromorphic Vision C++ Toolkit is distributed in the hope  //
00023 // that it will be useful, but WITHOUT ANY WARRANTY; without even the   //
00024 // implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR      //
00025 // PURPOSE.  See the GNU General Public License for more details.       //
00026 //                                                                      //
00027 // You should have received a copy of the GNU General Public License    //
00028 // along with the iLab Neuromorphic Vision C++ Toolkit; if not, write   //
00029 // to the Free Software Foundation, Inc., 59 Temple Place, Suite 330,   //
00030 // Boston, MA 02111-1307 USA.                                           //
00031 // //////////////////////////////////////////////////////////////////// //
00032 //
00033 // Primary maintainer for this file: Philip Williams <plw@usc.edu>
00034 // $HeadURL: svn://isvn.usc.edu/software/invt/trunk/saliency/src/BPnnet/BPnnet.H $
00035 // $Id: BPnnet.H 4786 2005-07-04 02:18:56Z itti $
00036 //
00037 #ifndef BPNNET_H_DEFINED
00038 #define BPNNET_H_DEFINED
00039 
00040 #include "BPnnet/BPneuron.H"
00041 #include "Image/Image.H"
00042 #include "BPnnet/KnowledgeBase.H"
00043 #include "BPnnet/SimpleVisualObject.H"
00044 
00045 template <class T> class Jet;
00046 
00047 //! describes structure of a 3 layer back prop neural net
00048 class BPnnet {
00049 public:
00050   //! Constructor
00051   BPnnet(const int numInput, const int numHidden, const KnowledgeBase *kb);
00052 
00053   //! Destructor
00054   ~BPnnet();
00055 
00056   //! Initialize all weights to (small) random values
00057   void randomizeWeights();
00058 
00059   //! Normalize weights
00060   void normalizeWeights();
00061 
00062   //! Do one training iteration
00063   /*! returns rms1 = sum of (target output - actual output)^2/n_outputs
00064     for all output layer neurons */
00065   double train(const Image<float> &in, const SimpleVisualObject& target,
00066                const double learnRate);
00067 
00068   //! Attempt to recognize given jet as a certain visual object
00069   bool recognize(const Image<float> &in, SimpleVisualObject &vo);
00070 
00071   //! Store 2 matrices of weights to the file "filename"
00072   /*! use this after training a net */
00073   bool save(const char* filename) const;
00074 
00075   //! Assign all weights based on data stored in the file "filename"
00076   /*! returns false if the # of units in each layer do not match the
00077     matrix sizes in the file
00078   */
00079   bool load(const char* filename);
00080 
00081 private:
00082 
00083   // Performs forward propogation tasks common to recognition and training
00084   // Used to avoid repetition of code within recognize() and train() methods
00085   void forwardProp(const Image<float> &in);
00086 
00087   // layers - 1 dimensional, should these be vectors instead of images?
00088   std::vector<BPneuron> inputLayer;
00089   std::vector<BPneuron> hiddenLayer;
00090   std::vector<BPneuron> outputLayer;
00091 
00092   // Weight (i,j) from input neuron i to hidden neuron j
00093   Image<double> weightFromInput;
00094   // Weight (i,j) from hidden neuron i to output neuron j
00095   Image<double> weightToOutput;
00096 
00097   int numInputUnits;        // number of features in input
00098   int numHiddenUnits;        // variable
00099   int numOutputUnits;        // number of known visual objects
00100 
00101   const KnowledgeBase *itsKb;
00102 };
00103 
00104 #endif
00105 
00106 // ######################################################################
00107 /* So things look consistent in everyone's emacs... */
00108 /* Local Variables: */
00109 /* indent-tabs-mode: nil */
00110 /* End: */
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