Commit Zero

.gitignore

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+# -- Mac OS files (from github's Global/OSX.gitignore) --
+.DS_Store
+.AppleDouble
+.LSOverride
+# Icon must end with two \r
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+# Thumbnails
+._*
+# Files that might appear on external disk
+.Spotlight-V100
+.Trashes
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
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+.apdisk
+
+# -- Eclipse files (from github's Global/Eclipse.gitignore) --
+*.pydevproject
+.metadata
+.gradle
+bin/
+tmp/
+*.tmp
+*.bak
+*.swp
+*~.nib
+local.properties
+.settings/
+.loadpath
+# External tool builders
+.externalToolBuilders/
+# Locally stored "Eclipse launch configurations"
+*.launch
+# CDT-specific
+.cproject
+# PDT-specific
+.buildpath
+# sbteclipse plugin
+.target
+# TeXlipse plugin
+.texlipse
+
+# -- Xcode files (from github's Global/Xcode.gitignore) --
+build/
+*.pbxuser
+!default.pbxuser
+*.mode1v3
+!default.mode1v3
+*.mode2v3
+!default.mode2v3
+*.perspectivev3
+!default.perspectivev3
+xcuserdata
+*.xccheckout
+*.moved-aside
+DerivedData
+*.xcuserstate
+
+# -- project specific ignores --
+lib*
+
+src/*.o
+
+debug/*.o
+debug/exe
+
+examples/*/a.out
+examples/*/*.o
+
+doc/*.aux
+doc/*.dvi
+doc/*.idx
+doc/*.log
+doc/*.out
+doc/*.ps
+doc/*.gz

LICENSE

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+FeedForwardNeuralNetwork is free software, released under the GNU General Public License.
+See http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt.
+
+If any work done using FeedForwardNeuralNetwork is released/published, I would greatly appreciate if:
+1) I am informed about it by email;
+2) an explicit acknowledgment with a link to the GitHub repository is included.

README.md

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+
+#FeedForwardNeuralNetwork
+
+C++ Library for building and using a Feed Forward Neural Network.
+It includes first and second derivatives in respect to the input values, and first derivatives in respect to the variational parameters.
+
+In `doc/` there is a user manual in pdf.
+
+In `examples/` there are several examples (STILL TO BE DONE).
+
+
+
+#Build the library
+
+Insert the system parameters in the `config.sh` and then simply execute the command
+
+   `./build.sh`
+

build.sh

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+#!/bin/bash
+
+source config.sh
+
+\rm -f *.so
+cd src/
+   \rm -f *.o *.so
+   echo "$CC $FLAGS $OPTFLAGS -fpic -c *.cpp"
+   $CC $FLAGS $OPTFLAGS -fpic -c *.cpp
+   echo "$CC $FLAGS $OPTFLAGS -shared -o lib${LIBNAME}.so *.o"
+   $CC $FLAGS $OPTFLAGS -shared -o lib${LIBNAME}.so *.o
+   mv lib${LIBNAME}.so ../
+cd ..
+
+echo
+echo "Library ready!"
+echo
+echo "Help, how can I use it?"
+echo "1)   $CC -I$(pwd)/src/ -c example.cpp"
+echo "     $CC -L$(pwd) example.o -l${LIBNAME}" 
+echo "2)   $CC -I$(pwd)/src/ -L$(pwd) example.cpp -l${LIBNAME}"

config.sh

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+#!/bin/bash
+
+# Library name
+LIBNAME="ffnn"
+
+# C++ compiler
+CC="g++"
+#CC="g++p-5"
+
+# C++ flags (std=c++11 is necessary)
+FLAGS="-std=c++11 -Wall -Werror"
+
+# Optimization flags
+OPTFLAGS="-O3"
+

debug/FFNN.txt

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+4
+id_actf 2 log_actf 5 log_actf 3 id_actf 2 
+1.27868 1.12922 -1.52124 -1.68274 -2.0485 -2.99628 0.58088 -1.28258 -0.443995 2.96736 -1.42395 1.09417 -2.77795 2.95594 -2.83344 0.566994 1.71308 0.454561 -2.68739 1.52762 0.74647 
+0 0 1

debug/main.cpp

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+#include <iostream>
+#include <cmath>
+#include <fstream>
+
+#include "FeedForwardNeuralNetwork.hpp"
+
+
+void printNNStructure(FeedForwardNeuralNetwork &nn)
+{
+   using namespace std;
+
+   for (int i=0; i<nn.getNLayers(); ++i)
+   {
+      cout << "Layer " << i << " has " << nn.getLayerSize(i) << " units" << endl;
+   }
+   cout << endl;
+}
+
+void printNNValues(FeedForwardNeuralNetwork &nn)
+{
+   using namespace std;
+
+   for (int i=0; i<nn.getNLayers(); ++i)
+   {
+      cout << "--- Layer " << i << endl;
+      cout << "Value:              ";
+      for (int j=0; j<nn.getLayer(i)->getNUnits(); ++j)
+      {
+         cout << nn.getLayer(i)->getUnit(j)->getValue() << "   ";
+      }
+      cout << endl;
+      cout << "First Derivative:   ";
+      for (int j=0; j<nn.getLayer(i)->getNUnits(); ++j)
+      {
+         cout << nn.getLayer(i)->getUnit(j)->getFirstDerivativeValue(0) << "   ";
+      }
+      cout << endl << endl;
+   }
+
+   cout << "Variational parameters:   ";
+   for (int j=0; j<nn.getNVariationalParameters(); ++j)
+   {
+      cout << nn.getVariationalParameter(j) << "   ";
+   }
+   cout << endl << endl;
+}
+
+
+int main(){
+   using namespace std;
+
+   cout << "Declare the FFANN" << endl;
+   FeedForwardNeuralNetwork * ffnn = new FeedForwardNeuralNetwork(4,6,2);
+   printNNStructure(*ffnn);
+
+   cout << "Change size of Layer 0 to 2" << endl;
+   ffnn->setLayerSize(0,2);
+   printNNStructure(*ffnn);
+
+   cout << "Pop a hidden layer" << endl;
+   ffnn->popHiddenLayer();
+   printNNStructure(*ffnn);
+
+   cout << "Add a hidden layer with size 5" << endl;
+   ffnn->pushHiddenLayer(5);
+   printNNStructure(*ffnn);
+
+   cout << "Add a hidden layer with size 3" << endl;
+   ffnn->pushHiddenLayer(3);
+   printNNStructure(*ffnn);
+
+   cout << "Connect the FFNN" << endl;
+   ffnn->connectFFNN();
+
+   ffnn->addFirstDerivativeSubstrate();
+   cout << "Add the first derivative to the computations" << endl;
+   ffnn->addSecondDerivativeSubstrate();
+   cout << "Add the second derivative to the computations" << endl;
+   ffnn->addVariationalFirstDerivativeSubstrate();
+   cout << "Add the variational first derivative to the computations" << endl;
+   cout << " -> Number of variational parameters: " << ffnn->getNVariationalParameters() << endl;
+   cout << endl;
+
+   double x=3.;
+   ffnn->setInput(1, &x);
+   ffnn->FFPropagate();
+   printNNValues(*ffnn);
+
+   // Plot of the 1-dimensional function
+   const double L=10.;
+   double xh;
+   const int N=150;
+   double dL=L/N;
+   const double h=0.00001;
+   double z, zh, zmh;
+   double z1d;
+   double z2d;
+   const int ivar=2;
+   double vp, vph;
+   // nn value
+   ofstream file;
+   file.open("randomNN.txt");
+   // first derivative
+   ofstream file1d;
+   file1d.open("randomNN-1d.txt");
+   // second derivative
+   ofstream file2d;
+   file2d.open("randomNN-2d.txt");
+   // variational first derivative
+   ofstream filev1d;
+   filev1d.open("randomNN-v1d.txt");
+   // numerical first derivative
+   ofstream filen1d;
+   filen1d.open("randomNN-n1d.txt");
+   // numerical second derivative
+   ofstream filen2d;
+   filen2d.open("randomNN-n2d.txt");
+   // numerical variational first derivative
+   ofstream filenv1d;
+   filenv1d.open("randomNN-nv1d.txt");
+   x=-L*0.5;
+   for (int i=0; i<N; ++i)
+   {
+      x+=dL;
+      ffnn->setInput(1, &x);
+      ffnn->FFPropagate();
+      z=ffnn->getOutput(1);
+      file << x << "   " << z << endl;
+
+      // first derivative
+      z1d=ffnn->getFirstDerivative(1,0);
+      file1d << x << "   " << z1d << endl;
+
+      // second derivative
+      z2d=ffnn->getSecondDerivative(1,0);
+      file2d << x << "   " << z2d << endl;
+
+      // variational first derivative
+      z1d=ffnn->getVariationalFirstDerivative(1,ivar);
+      filev1d << x << "   " << z1d << endl;
+
+      // numerical first derivative
+      xh=x+h;
+      ffnn->setInput(1, &xh);
+      ffnn->FFPropagate();
+      zh=ffnn->getOutput(1);
+      filen1d << x << "   " << (zh-z)/h << endl;
+      ffnn->setInput(1, &x);
+
+      // numerical second derivative
+      xh=x-h;
+      ffnn->setInput(1, &xh);
+      ffnn->FFPropagate();
+      zmh=ffnn->getOutput(1);
+      filen2d << x << "   " << (zh-2.*z+zmh)/(h*h) << endl;
+      ffnn->setInput(1, &x);
+
+      // numerical variational first derivative
+      vp=ffnn->getVariationalParameter(ivar);
+      vph=vp+h;
+      ffnn->setVariationalParameter(ivar,vph);
+      ffnn->FFPropagate();
+      zh=ffnn->getOutput(1);
+      filenv1d << x << "   " << (zh-z)/h << endl;
+      ffnn->setVariationalParameter(ivar,vp);
+
+   }
+   file.close();
+   file1d.close();
+   file2d.close();
+   filev1d.close();
+   filen1d.close();
+   filen2d.close();
+   filenv1d.close();
+
+   // store NN in a file
+   ffnn->storeOnFile("FFNN.txt");
+
+   // create a new NN reading it from the file
+   cout << endl << endl << "CREATE A COPY OF THE PREVIOUS FFNN USING THE FILE IN WHICH IT WAS STORED ..." << endl;
+   FeedForwardNeuralNetwork * ffnn_copy =new FeedForwardNeuralNetwork("FFNN.txt");
+   x=3.;
+   ffnn->setInput(1, &x);
+   ffnn->FFPropagate();
+   printNNValues(*ffnn);
+
+   // free memory
+   delete ffnn_copy;
+   delete ffnn;
+
+   return 0;
+}
+
+