00001 /*!@file Gist/trainUtils.H training utility functions (not limited to FFN) */ 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: Christian Siagian <siagian@usc.edu> 00034 // $HeadURL: svn://isvn.usc.edu/software/invt/trunk/saliency/src/Gist/trainUtils.H $ 00035 // $Id: trainUtils.H 12395 2009-12-25 02:34:13Z siagian $ 00036 // 00037 00038 #ifndef GIST_TRAINUTILS_H_DEFINED 00039 #define GIST_TRAINUTILS_H_DEFINED 00040 00041 #include "Image/Image.H" 00042 #include <string> 00043 00044 template <class T> class Image; 00045 00046 #define TUTILS_RW_RANGE 1.0 // range of initial random weights [-.5 ... .5] 00047 00048 // ###################################################################### 00049 // utilities for training feed forward network (FFN) 00050 00051 // ###################################################################### 00052 // to store information from the training file 00053 class FFNtrainInfo 00054 { 00055 public: 00056 //! Construct a FFN training params info 00057 //! if blank, need to call reset later on 00058 FFNtrainInfo(std::string fName = std::string("")); 00059 00060 //! Destructor 00061 virtual ~FFNtrainInfo(); 00062 00063 //! reset the Neural Network classifier parameter 00064 bool reset(std::string fName); 00065 00066 std::string trainFolder; //!< where the training data is 00067 std::string testFolder; //!< where the testing data is 00068 uint nOutput; //!< the number of output dimension 00069 bool isPCA; //!< has a dimension reduction step 00070 std::string evecFname; //!< the dimension reduction 00071 uint oriFeatSize; //!< the original number of features 00072 uint redFeatSize; //!< the reduced number of features 00073 uint h1size; //!< the number of hidden nodes in layer 1 00074 uint h2size; //!< the number of hidden nodes in layer 2 00075 std::string h1Name; //!< the weight file for hidden layer 1 00076 std::string h2Name; //!< the weight file for hidden layer 2 00077 std::string oName; //!< the weight file for output layer 00078 float learnRate; //!< the learning rate 00079 std::string trainSampleFile; //!< the training sample file list 00080 std::string testSampleFile; //!< the testing sample file list 00081 }; 00082 00083 // ###################################################################### 00084 // utilities for applying PCA/ICA dimension reduction 00085 00086 //! Setup the PCA/ICA unmixing matrix 00087 Image<double> setupPcaIcaMatrix(std::string inW, int oriSize, int redSize); 00088 00089 //! Get the PCA/ICA reduced feature vector 00090 //! in a form of an image 00091 Image<float> getPcaIcaFeatImage(Image<double> res, int w, int h, int s); 00092 00093 #endif // GIST_TRAINUTILS_H_DEFINED
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