Spice-MLP

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自己組織化地図とMLPニューラルネットワーク-実践編

• 英語
• ベトナム語

// MLP_Data_Modeling.cpp
// How to compiler
// g++ MLP_Data_Modeling.cpp mlpsom_source/spice_mlp_lib.cpp -lm -O3  -ffast-math -o s.x
// source /opt/intel/system_studio_2018/bin/compilervars.sh intel64
// icpc MLP_Data_Modeling.cpp mlpsom_source/spice_mlp_lib.cpp -lm -O3  -ffast-math -o s.x
// $ ./s.x 
#include 
#include 
#include 
#include 
#include  
#include 
#include 
#include 
#include 
#include 
#include 

#include "mlpsom_include/spice_mlp_lib.h"
using namespace std;
using namespace SpiceNeuro;
 struct MlpTrainingSet
 {
     double **fInputs;                           //input data
     double **fOutputs;                          //output data
     char  (*strLabel)[255];                    //label of the dataset
     int nDataSet;                              //number of datasets
     int nInputs;                               //number of Inputs
     int nOutputs;                              //number of Outputs
 };

void InitMlpDataSet(MlpTrainingSet *DataSets, int nTrainingDataSet, int nInputs, int nOutputs);
void FreeDataSet(MlpTrainingSet *DataSets);
int findMaxIndex(double *data, const int nData);

// find max in output of a neuron
int findMaxIndex(double *data, const int nData)
{
   float max = data[0];
   int ind = 0;
   for (int i = 1; i < nData; i++)
   {
      if (max < data[i]){ max = data[i]; ind = i; } } return ind; } //initialize new dataset with number of datasets = nTrainingDataSet void InitMlpDataSet(MlpTrainingSet *DataSets, int nTrainingDataSet, int nInputs, int nOutputs) { DataSets->fInputs = new double*[nTrainingDataSet];
   DataSets->fOutputs = new double*[nTrainingDataSet];
   DataSets->strLabel = new char[nTrainingDataSet][255]; 
   for (int i = 0; i < nTrainingDataSet; i++) { DataSets->fInputs[i] = new double[nInputs];
      DataSets->fOutputs[i] = new double[nOutputs];
   }
   DataSets->nDataSet = nTrainingDataSet;
   DataSets->nInputs = nInputs;
   DataSets->nOutputs = nOutputs;
   return;
}
void FreeDataSet(MlpTrainingSet *DataSets)
{
   for (int i = 0; i < DataSets->nDataSet; i++)
   {
      delete [] DataSets->fInputs[i];
      delete [] DataSets->fOutputs[i];
   }
   delete [] DataSets->fInputs; 
   delete [] DataSets->fOutputs;
   delete [] DataSets->strLabel;
   return;
}
int CreateTrainingDataFromCsv(char *txtFilename, int nData, int nInputs, int nOutputs)
{
   printf("creating %s\n", txtFilename);
   MlpTrainingSet newData;
   InitMlpDataSet(&newData, nData, nInputs, nOutputs); // initialize 
   FILE *f = fopen(txtFilename, "r");
   char tmp[255];
   /*
   // data without header? comment it
   for (int i = 0; i < nInputs + nOutputs + 2; i++){
      fscanf(f, "%s", tmp);
   }
   */
   for (int j = 0; j < nData; j++)
   {
	  // data without ID, comment ID 
      // fscanf(f, "%s", tmp); // ID
      for (int i = 0; i < nInputs; i++){
         fscanf(f, "%s", tmp);
         newData.fInputs[j][i] = atof(tmp);
      }
      for (int i = 0; i < nOutputs; i++){
         fscanf(f, "%s", tmp);
         newData.fOutputs[j][i] = atof(tmp);
      }
      fscanf(f, "%s", tmp); // label
      sprintf(newData.strLabel[j], tmp); // assign label for the i-th dataset, please remember length of the label is 255
   }
   fclose(f);
   char fname[255];
   sprintf(fname, "%s_read.txt", txtFilename);
   f = fopen(fname, "w");
   for (int j = 0; j < nData; j++){
      for (int i = 0; i < nInputs; i++){
         fprintf(f, "%f ", newData.fInputs[j][i]);
      }
      for (int i = 0; i < nOutputs; i++){
         fprintf(f, "%f ", newData.fOutputs[j][i]);
      }
      fprintf(f, "%s \n", newData.strLabel[j]);
   }
   fclose(f);
   sprintf(fname, "%s.dat", txtFilename);
   // write data to binary file

   FILE *fout = fopen(fname, "wb");
   fwrite(&newData.nDataSet, sizeof(int), 1, fout);
   fwrite(&newData.nInputs, sizeof(int),  1, fout);
   fwrite(&newData.nOutputs, sizeof(int), 1, fout);
   for (int j = 0; j < newData.nDataSet; j++) { fwrite(newData.fInputs[j], sizeof(double), newData.nInputs, fout); fwrite(newData.fOutputs[j], sizeof(double), newData.nOutputs, fout); fwrite(newData.strLabel[j], sizeof(char), 255, fout); }; fclose(fout); FreeDataSet(&newData); return 0; } // create data from csv file int main_CreateTrainingDataFromCsv() { CreateTrainingDataFromCsv("TrainingData_V3-sized/Boxes.txt", 474, 36*54, 33); return 0; } char label[33][24] = { "0-clear", "0-unclear", "1-clear", "1-unclear", "2-clear", "2-unclear", "7-clear", "7-unclear", "A-clear", "A-unclear", "B-clear", "BI-clear", "BI-unclear", "C-clear", "C-unclear", "D-clear", "D-unclear", "E-clear", "F-clear", "F-unclear", "G-clear", "G-unclear", "H-clear", "J-clear", "K-clear", "L-clear", "naka-clear", "naka-unclear", "RU-clear", "RU-unclear", "T-clear", "T-unclear", "Y-clear" }; int main()//_train_MLP() { int nHiddens = 33; char dataFile[255], modelingFile[255], fClassInClass[255], fClassInClassBrief[255]; char *folder = "data/"; sprintf(fClassInClass, "%s/ClassInClass.txt", folder); sprintf(fClassInClassBrief, "%s/ClassInClassBrief.txt", folder); sprintf(dataFile, "%s/Boxes.txt.dat", folder); printf("datafile = %s\n", dataFile); SpiceMLP *MLP = new SpiceMLP("Example of MLP", 36 * 54, nHiddens,33); // initialize a MLP with 36*54 input, 33 hiddens and 33 outputs printf("MLP->DataSets.nInputs = %i\n", MLP->DataSets.nInputs);
	printf("MLP->DataSets.nOutputs = %i\n", MLP->DataSets.nOutputs);
	printf("MLP->GetLayers.GetInputNeurons  = %i\n", MLP->GetLayers.GetInputNeurons());
	printf("MLP->GetLayers.GetHiddenNeurons = %i\n", MLP->GetLayers.GetHiddenNeurons());
	printf("MLP->GetLayers.GetOutputNeurons = %i\n", MLP->GetLayers.GetOutputNeurons());


    MLP->InitDataSet(474, 36 * 54, 33);           // initialize data structure
    MLP->DataSplitOptions.iMiddle = 20;

      // set training parameters
      MLP->TrainingParam.bAdaptiveLearning = false;
      MLP->TrainingParam.bRandomInput      = true;
      MLP->TrainingParam.bEnableTesting    = true;
      MLP->TrainingParam.fLearningRate     = 0.0005f;
      MLP->TrainingParam.fMseRequired      = 0.0;
      // AF_Gaussian->AF_Sigmoid is ??
      MLP->TrainingParam.HiddenActivatedFunction = SpiceNeuro::AF_HyperTanh;//AF_HyperTanh, AF_Sigmoid
      MLP->TrainingParam.OutputActivatedFunction = SpiceNeuro::AF_Sigmoid;

	  printf("MLP->LoadTrainingData(dataFile) = %s\n", dataFile);
	  
      MLP->LoadTrainingData(dataFile);
	  printf("MLP->LoadTrainingData(dataFile) = %s OK\n", dataFile);
      //MLP->NormalizeInputData(Normalization_Linear, 0, 1);

      //asign split option, then do split data
      
      //MLP->DataSplitOptions.iRandomPercent = 70;
      //MLP->DataSplitOptions.SplitOptions   = SpiceNeuro::Split_Random;
      //MLP->DataSplitOptions.SplitOptions   = SpiceNeuro::Split_Middle;
      //MLP->SplitData();
      MLP->AssignTrainingData(SpiceNeuro::Use_AllDataSets); // Use_FirstSplittedDataSets, ot Use_AllDataSets

      //Train the MLP in some iterations
      double fTimeStart, fTimeEnd, fTimeLast=0;
      fTimeStart = MLP->fGetTimeNow();
      // train mlp in 1000 iterations
      printf("start to train\n");

      int nIteration =  1000;

      MLP->ResetWeights();
      double fFinal = MLP->DoTraining(nIteration);

      fTimeEnd = MLP->fGetTimeNow();
      printf("1000 iteration take %f (%f)sec, fFinal = '%f'\n", fTimeEnd - fTimeLast, fTimeEnd - fTimeStart, fFinal);
      fTimeLast = fTimeEnd;


      //Now, test the modeling data
      double *dOneSampleInputs   = new double[MLP->GetLayers.GetInputNeurons()];
      double *dOneSampleReponse = new double[MLP->GetLayers.GetOutputNeurons()];
      double **newResponse       = new double*[MLP->DataSets.nDataSet];
      for (int i = 0; i < MLP->DataSets.nDataSet; i++){
            newResponse[i] = new double[MLP->GetLayers.GetOutputNeurons()];
       }

      for (int j = 0; j < MLP->DataSets.nDataSet; j++)
      {
           //Get inputs of one dataset
           for (int i = 0; i < MLP->GetLayers.GetInputNeurons(); i++){
               dOneSampleInputs[i] = MLP->DataSets.fInputs[j][i];
           }

           MLP->ModelingOneDataSet(dOneSampleInputs, dOneSampleReponse);

           //Get modeling value of the one dataset
           for (int i = 0; i < MLP->GetLayers.GetOutputNeurons(); i++){
               newResponse[j][i] = dOneSampleReponse[i];
           }
      }
     //print modeling data to screen and file
	 sprintf(modelingFile, "%s/Boxes.modeling_%i-iter-%i-hiddens.csv", folder, nIteration, nHiddens);

     FILE *fc = fopen(modelingFile, "w");
	 fprintf(fc, "strLabel[j], label[indDesired], indDesired, indModeling, label[indModeling], newResponse[][indModeling]\n");
	 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
	 {
		 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
		 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
		 fprintf(fc, "%s,%s,%i,%i,%s,%f,", MLP->DataSets.strLabel[j], label[indDesired], indDesired, indModeling, label[indModeling], newResponse[j][indModeling]);
		 for (int i = 0; i < MLP->GetLayers.GetOutputNeurons(); i++) {
			 fprintf(fc, "%f, ", newResponse[j][i]);
		 }
		 fprintf(fc, "\n");
	 }
     fclose(fc);
	 // save training & testing error
	 sprintf(modelingFile, "%s/Boxes-%i-iter-%i-hiddens-error.csv", folder, nIteration, nHiddens);
	 fc = fopen(modelingFile, "w");
	 for (int i = 0; i < nIteration; i++) { fprintf(fc, "%i,%f,%f\n", i, MLP->GetErrors.GetTrainingMSE(i), MLP->GetErrors.GetTestingMSE(i));
	 }
	 fclose(fc);
	 //*********************************
	 /*
	 // find the matched output
	 // search for the output
	 const int nOutputs = 10;
	 // match of output[op] on all classes
	 int nMatched_I_in_J[nOutputs][nOutputs];
	 int nSampleOfClass[nOutputs];
	 for (int j = 0; j < nOutputs; j++) {
		 nSampleOfClass[j] = 0;
		 for (int i = 0; i < nOutputs; i++) {
			 nMatched_I_in_J[j][i] = 0;
		 }
	 }
	 // calculate nMatched class in class
	 for (int op = 0; op < MLP->DataSets.nOutputs; op++)
	 {
		 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
		 {
			 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
			 if (op != indDesired)
				 continue;
			 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
			 nSampleOfClass[op] += 1;
			 nMatched_I_in_J[indDesired][indModeling] += 1;
		 }
	 }

	 // save class in class to file
	 fc = fopen(fClassInClass, "w");
	 fprintf(fc, "Class In Class\n");
	 for (int class1 = 0; class1 < MLP->DataSets.nOutputs; class1++)
	 {
		 for (int class2 = 0; class2 < MLP->DataSets.nOutputs; class2++)
		 {
			 if (class1 == class2)
				 fprintf(fc, "Class %i on Class %i (correct)\n", class1, class2);
			 else
				 fprintf(fc, "Class %i on Class %i (mistake)\n", class1, class2);
			 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
			 {
				 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
				 if (indDesired != class1)
					 continue;
				 // class1 on class2
				 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
				 if (indModeling != class2)
					 continue;
				 fprintf(fc, "%s\n", MLP->DataSets.strLabel[j]);
			 }
		 }
	 }
	 fclose(fc);

	 FILE *f = fopen(fClassInClassBrief, "w");
	 printf("modeling for %s\n", dataFile);
	 fprintf(f, "Class,Total,Correct,");
	 for (int j = 0; j < nOutputs; j++)
		fprintf(f, "on_Class_%i,", j);
	 fprintf(f, "\n");
	 for (int j = 0; j < nOutputs; j++) {
		 fprintf(f, "Class_%i,%i,", j, nSampleOfClass[j]);
		 fprintf(f, "%i,", nMatched_I_in_J[j][j]);
		 for (int i = 0; i < nOutputs; i++) {
			 fprintf(f, "%i,", nMatched_I_in_J[j][i]);
			 printf("%i,", nMatched_I_in_J[j][i]);
		 }
		 fprintf(f, "\n");
		 printf("\n");
	 }

	 fclose(f);
	 */

	 //*********************************
   
     delete [] dOneSampleInputs;
     delete [] dOneSampleReponse;
     for (int i = 0; i < MLP->DataSets.nDataSet; i++)
        delete [] newResponse[i];
     delete [] newResponse;
     delete MLP;
	 printf("Press Enter to exit\n");
	 getchar();
     return 0;
}