93.

c++ neural networks and fuzzy logic C++ Neural Networks and Fuzzy Logic
by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526   Pub Date: 06/01/95
  

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The New and Final backprop.cpp File

The last file to present is the backprop.cpp file. This is shown in Listing 13.3.

Listing 13.3 Implementation file for the backpropagation simulator, with noise and momentum backprop.cpp

 // backprop.cpp      V. Rao, H. Rao #include “layer.cpp” #define TRAINING_FILE   “training.dat” #define WEIGHTS_FILE “weights.dat” #define OUTPUT_FILE   “output.dat” #define TEST_FILE   “test.dat” void main() { float error_tolerance=0.1; float total_error=0.0; float avg_error_per_cycle=0.0; float error_last_cycle=0.0; float avgerr_per_pattern=0.0; // for the latest cycle float error_last_pattern=0.0; float learning_parameter=0.02; float alpha; // momentum parameter float NF; // noise factor float new_NF; unsigned temp, startup, start_weights; long int vectors_in_buffer; long int max_cycles; long int patterns_per_cycle=0; long int total_cycles, total_patterns; int i; // create a network object network backp; FILE * training_file_ptr, * weights_file_ptr, * output_file_ptr; FILE * test_file_ptr, * data_file_ptr; // open output file for writing if ((output_file_ptr=fopen(OUTPUT_FILE,”w”))==NULL)                {                cout << “problem opening output file\n”;                exit(1);                } // enter the training mode : 1=training on     0=training off cout << “—————————————————————————-\n”; cout << “ C++ Neural Networks and Fuzzy Logic \n”; cout << “    Backpropagation simulator \n”; cout << “      version 2 \n”; cout << “—————————————————————————-\n”; cout << “Please enter 1 for TRAINING on, or 0 for off: \n\n”; cout << “Use training to change weights according to your\n”; cout << “expected outputs. Your training.dat file should contain\n”; cout << “a set of inputs and expected outputs. The number of\n”; cout << “inputs determines the size of the first (input) layer\n”; cout << “while the number of outputs determines the size of the\n”; cout << “last (output) layer :\n\n”; cin >> temp; backp.set_training(temp); if (backp.get_training_value() == 1)         {         cout << “—> Training mode is *ON*. weights will be saved\n”;         cout << “in the file weights.dat at the end of the\n”;         cout << “current set of input (training) data\n”;         } else         {         cout << “—> Training mode is *OFF*. weights will be loaded\n”;         cout << “from the file weights.dat and the current\n”;         cout << “(test) data set will be used. For the test\n”;         cout << “data set, the test.dat file should contain\n”;         cout << “only inputs, and no expected outputs.\n”;         } if (backp.get_training_value()==1)         {         // ————————————————————-         //    Read in values for the error_tolerance,         //    and the learning_parameter         // ————————————————————-         cout << “ Please enter in the error_tolerance\n”;         cout << “ —- between 0.001 to 100.0, try 0.1 to start - \n”;         cout << “\n”;         cout << “and the learning_parameter, beta\n”;         cout << “ —- between 0.01 to 1.0, try 0.5 to start - \n\n”;         cout << “ separate entries by a space\n”;         cout << “ example: 0.1 0.5 sets defaults mentioned :\n\n”;         cin >> error_tolerance >> learning_parameter;         // ————————————————————-         //    Read in values for the momentum         //    parameter, alpha (0-1.0)         //    and the noise factor, NF (0-1.0)         // ————————————————————-         cout << “Enter values now for the momentum \n”;         cout << “parameter, alpha(0-1.0)\n”;         cout << “ and the noise factor, NF (0-1.0)\n”;         cout << “You may enter zero for either of these\n”;         cout << “parameters, to turn off the momentum or\n”;         cout << “noise features.\n”;         cout << “If the noise feature is used, a random\n”;         cout << “component of noise is added to the inputs\n”;         cout << “This is decreased to 0 over the maximum\n”;         cout << “number of cycles specified.\n”;         cout << “enter alpha followed by NF, e.g., 0.3 0.5\n”;         cin >> alpha >> NF;         //—————————————————————-         // open training file for reading         //—————————————————————-         if ((training_file_ptr=fopen(TRAINING_FILE,”r”))==NULL)                {                cout << “problem opening training file\n”;                exit(1);                }         data_file_ptr=training_file_ptr; // training on         // Read in the maximum number of cycles         // each pass through the input data file is a cycle         cout << “Please enter the maximum cycles for the simulation\n”;         cout << “A cycle is one pass through the data set.\n”;         cout << “Try a value of 10 to start with\n”;         cin >> max_cycles;         cout << “Do you want to read weights from weights.dat to start?\n”;         cout << “Type 1 to read from file, 0 to randomize starting weights\n”;         cin >> start_weights;         } else         {         if ((test_file_ptr=fopen(TEST_FILE,”r”))==NULL)                {                cout << “problem opening test file\n”;                exit(1);                }         data_file_ptr=test_file_ptr; // training off         } // training: continue looping until the total error is less than //             the tolerance specified, or the maximum number of //             cycles is exceeded; use both the forward signal                propagation //             and the backward error propagation phases. If the error //             tolerance criteria is satisfied, save the weights in a                file. // no training: just proceed through the input data set once in the //             forward signal propagation phase only. Read the starting //             weights from a file. // in both cases report the outputs on the screen // initialize counters total_cycles=0; // a cycle is once through all the input data total_patterns=0; // a pattern is one entry in the input data new_NF=NF; // get layer information backp.get_layer_info(); // set up the network connections backp.set_up_network(); // initialize the weights if ((backp.get_training_value()==1)&&(start_weights!=1))         {         // randomize weights for all layers; there is no         // weight matrix associated with the input layer         // weight file will be written after processing         backp.randomize_weights();         // set up the noise factor value         backp.set_NF(new_NF);         } else         {         // read in the weight matrix defined by a         // prior run of the backpropagation simulator         // with training on         if ((weights_file_ptr=fopen(WEIGHTS_FILE,”r”))                        ==NULL)                {                cout << “problem opening weights file\n”;                exit(1);                }         backp.read_weights(weights_file_ptr);         fclose(weights_file_ptr);         } // main loop // if training is on, keep going through the input data //             until the error is acceptable or the maximum number of                cycles //             is exceeded. // if training is off, go through the input data once. report outputs // with inputs to file output.dat startup=1; vectors_in_buffer = MAX_VECTORS; // startup condition total_error = 0; while (   ((backp.get_training_value()==1)                          && (avgerr_per_pattern                                       > error_tolerance)                          && (total_cycles < max_cycles)                          && (vectors_in_buffer !=0))                          || ((backp.get_training_value()==0)                          && (total_cycles < 1))                          || ((backp.get_training_value()==1)                          && (startup==1))                          ) { startup=0; error_last_cycle=0; // reset for each cycle patterns_per_cycle=0; backp.update_momentum(); // added to reset                        // momentum matrices                        // each cycle // process all the vectors in the datafile // going through one buffer at a time // pattern by pattern while ((vectors_in_buffer==MAX_VECTORS))         {         vectors_in_buffer=                backp.fill_IObuffer(data_file_ptr); // fill buffer                if (vectors_in_buffer < 0)                       {                       cout << “error in reading in vectors, aborting\n”;                       cout << “check that there are no extra linefeeds\n”;                       cout << “in your data file, and that the number\n”;                       cout << “of layers and size of layers match the\n”;                       cout << “the parameters provided.\n”;                       exit(1);                       }                // process vectors                for (i=0; i<vectors_in_buffer; i++)                       {                       // get next pattern                       backp.set_up_pattern(i);                       total_patterns++;                       patterns_per_cycle++;                       // forward propagate                       backp.forward_prop();                       if (backp.get_training_value()==0)                              backp.write_outputs(output_file_ptr);                       // back_propagate, if appropriate                       if (backp.get_training_value()==1)                              {                              backp.backward_prop(error_last_pattern);                              error_last_cycle +=                                     error_last_pattern*error_last_pattern;                              avgerr_per_pattern=                ((float)sqrt((double)error_last_cycle/patterns_per_cycle));                              // if it’s not the last cycle, update weights                            if ((avgerr_per_pattern                                     > error_tolerance)                                     && (total_cycles+1 < max_cycles))                                     backp.update_weights(learning_                                            parameter, alpha);                              // backp.list_weights(); // can                              // see change in weights by                              // using list_weights before and                              // after back_propagation                              }                       }        error_last_pattern = 0;        } total_error += error_last_cycle; total_cycles++; // update NF // gradually reduce noise to zero if (total_cycles>0.7*max_cycles)                new_NF = 0; else   if (total_cycles>0.5*max_cycles)                       new_NF = 0.25*NF;                else   if (total_cycles>0.3*max_cycles)                                     new_NF = 0.50*NF;                              else   if (total_cycles>0.1*max_cycles)                                            new_NF = 0.75*NF; backp.set_NF(new_NF); // most character displays are 25 lines // user will see a corner display of the cycle count // as it changes cout << “\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n”; cout << total_cycles << “\t” << avgerr_per_pattern << “\n”; fseek(data_file_ptr, 0L, SEEK_SET); // reset the file pointer                              // to the beginning of                              // the file vectors_in_buffer = MAX_VECTORS; // reset } // end main loop if (backp.get_training_value()==1)         {         if ((weights_file_ptr=fopen(WEIGHTS_FILE,”w”))                       ==NULL)                {                cout << “problem opening weights file\n”;                exit(1);                }         } cout << “\n\n\n\n\n\n\n\n\n\n\n”; cout << “————————————————————————\n”; cout << “    done:   results in file output.dat\n”; cout << “            training: last vector only\n”; cout << “            not training: full cycle\n\n”; if (backp.get_training_value()==1)         {         backp.write_weights(weights_file_ptr);         backp.write_outputs(output_file_ptr);         avg_error_per_cycle=(float)sqrt((double)total_error/         total_cycles);         error_last_cycle=(float)sqrt((double)error_last_cycle);         fclose(weights_file_ptr); cout << “              weights saved in file weights.dat\n”; cout << “\n”; cout << “——>average error per cycle = “ << avg_error_per_cycle << “ <—-\n”; cout << “——>error last cycle = “ << error_last_cycle << “ <—-\n”; ???cout << “->error last cycle per pattern=“<<avgerr_per_pattern <<“<—-\n”;         } cout << “——————>total cycles = “ << total_cycles << “ <—-\n”; cout << “——————>total patterns = “ << total_patterns << “ <—-\n”; cout << “—————————— ;——————————————\n”; // close all files fclose(data_file_ptr); fclose(output_file_ptr); } 


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Copyright © IDG Books Worldwide, Inc.



C++ Neural Networks and Fuzzy Logic
C++ Neural Networks and Fuzzy Logic
ISBN: 1558515526
EAN: 2147483647
Year: 1995
Pages: 139

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