by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95
|
|
C++ Neural Networks and Fuzzy Logic
by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526 Pub Date: 06/01/95 |
| Previous | Table of Contents | Next |
A Look at the Functions in the layer.cpp File
The following is a listing of the functions in the layer.cpp file along with a brief statement of each one's purpose.
Note that the functions squash(float) and randomweight(unsigned) are declared inline. This means that the function's source code is inserted wherever it appears. This increases code size, but also increases speed because a function call, which is expensive, is avoided.
The final file to look at is the backprop.cpp file presented in Listing 7.3.
Listing 7.3 The backprop.cpp file for the backpropagation simulator
// 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;
unsigned temp, startup;
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 1 \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;
//---------------------
// 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 simula-\
tion\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;
}
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 propaga
tion
// 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
// 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)
{
// randomize weights for all layers; there is no
// weight matrix associated with the input layer
// weight file will be written after processing
// so open for writing
if ((weights_file_ptr=fopen(WEIGHTS_FILE,"w"))
==NULL)
{
cout << "problem opening weights file\n";
exit(1);
}
backp.randomize_weights();
}
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);
}
// 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;
// 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
z *error_last_pattern;
backp.update_weights(learning_parameter);
// backp.list_weights();
// can
// see change in weights by
// using list_weights before and
// after back_propagation
}
}
error_last_pattern = 0;
}
avgerr_per_pattern=((float)sqrt((double)error_last_cycle
/patterns_per_cycle));
total_error += error_last_cycle;
total_cycles++;
// most character displays are 26 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\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
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);
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(weights_file_ptr);
fclose(output_file_ptr);
}
| Previous | Table of Contents | Next |