Category : C Source Code
Archive : NETCPP.ZIP
Filename : NETWORK.CPP
Output of file : NETWORK.CPP contained in archive : NETCPP.ZIP
#include
#include
#include
#include
void Network::adjustWeights(void)
{
for(int atCnxn = 0; atCnxn < nCnxns; atCnxn++)
cnxn[atCnxn].adjust();
}
void Network::allForward(void)
{
CLEAR_SCREEN();
CURSOR_GOTO(1,1);
for(atPattern = 0; atPattern < nPatterns; atPattern++)
{
forwardPass();
displayDiff();
}
}
void Network::backwardProp(void)
{
calcOutputError();
calcMiddleError();
adjustWeights();
}
void Network::calcMiddleError(void)
{
//This would need to be reworked for a multi-middle layer network
for(int atMidNeurode = 0; atMidNeurode < nInLayer[1]; atMidNeurode++)
{
float weightedSumOfErrors = 0;
int offset = nInLayer[0] * nInLayer[1] + nInLayer[2] * atMidNeurode;
for(int atHighNeurode = 0; atHighNeurode < nInLayer[OUTLAYER]; atHighNeurode++)
weightedSumOfErrors += cnxn[offset + atHighNeurode].n2->error * cnxn[offset + atHighNeurode].weight;
neuron[1][atMidNeurode]->derivTransfer(weightedSumOfErrors);
}
}
void Network::calcOutputError(void)
{
thisPatternError = 0;
for(int atNeurode = 0; atNeurode < nInLayer[OUTLAYER]; atNeurode++)
{
float diff = pat[atPattern].out[atNeurode] - neuron[OUTLAYER][atNeurode]->output;
//You may just pass diff to calcLocalError for "classic" backprop
//Or you can square it for "Quadratic" error func
//Or you can cube it for "Cubic" error func.
neuron[OUTLAYER][atNeurode]->derivTransfer(diff);
if(diff < 0)
{
if(diff < -acceptableError)
thisPatternError -= diff;
}else{
if (diff > acceptableError)
thisPatternError += diff;
}
}
if(thisPatternError > acceptableError)
totalError += thisPatternError;
}
bool Network::atEpochEnd(void){
if(atPattern == nPatterns - 1){
atPattern = 0;
++iteration;
return true;
}else{
atPattern++;
return false;
}
}
void Network::display(void)
{
cout<<"Network iteration "<
for(int atLayer = 0; atLayer < nLayers; atLayer++)
{
cout<<"Layer "<
{
cout<<"\tNeuron["<
if(atLayer != OUTLAYER)
{
for(int atHighNeuron = 0; atHighNeuron < nInLayer[atLayer+1];atHighNeuron++)
{
cout<<"\t\t cnxn["<
}
}
}
}
}
void Network::displayDiff(void)
{
#ifdef DISPLAY_YES
SET_TEXT_ATTR(WHITE);
DOS_PRINT("Pattern[%d] Error: %f\n\r", atPattern, thisPatternError);
DOS_PRINT("Desired: ");
for(int i = 0; i < outDepth * outWidth; i++)
DOS_PRINT("%6.3f ", (float) pat[atPattern].out[i]);
DOS_PRINT("\n\r");
DOS_PRINT("Actual: ");
for( i = 0; i < outDepth * outWidth; i++)
{
float err = pat[atPattern].out[i] - neuron[OUTLAYER][i]->output;
if(err < 0)
err *= -1;
if(err > acceptableError)
SET_TEXT_ATTR(RED);
else
SET_TEXT_ATTR(GREEN);
DOS_PRINT("%6.3f ",neuron[OUTLAYER][i]->output);
}
DOS_PRINT("\n\r");
SET_TEXT_ATTR(WHITE);
#endif
}
void Network::displayTotalError(void)
{
cout<<"Iteration: "<
void Network::displayPerformance(unsigned long elapsedTime)
{
cout<<"Iterations: "<
cout.precision(3);
cout<<" Connections per second: "<
void Network::forwardPass(void)
{
// Zero all inputs
for(int atLayer = 0; atLayer < nLayers; atLayer++)
{
for(int atNeuron = 0; atNeuron < nInLayer[atLayer]; atNeuron++)
neuron[atLayer][atNeuron]->input = 0.0;
}
//First, put the input pattern directly into the input neuron's output
//queues.
for(int atNeuron = 0; atNeuron < nInLayer[0]; atNeuron++)
neuron[0][atNeuron]->output = pat[atPattern].in[atNeuron];
//Then, for each layer, feedforward
int cnxnNumber = 0;
int offset = 0;
for(atLayer = 1; atLayer <= OUTLAYER; atLayer++)
{
int nCons = nInLayer[atLayer] * nInLayer[atLayer - 1];
for(cnxnNumber = offset; cnxnNumber < nCons + offset; cnxnNumber++)
cnxn[cnxnNumber].feedForward();
offset += nCons;
for(atNeuron = 0; atNeuron < nInLayer[atLayer]; atNeuron++)
neuron[atLayer][atNeuron]->transfer();
}
}
Network::Network(void)
{
nLayers = 0;
atPattern = 0;
iteration = 0;
totalError = 0;
}
Network::Network(int nL, int* layerSize)
{
nLayers = nL;
nInLayer = new int[nLayers];
if(!nInLayer)
{
cerr<<"Error during memory allocation of nInLayer array!\n";
return;
}
nNeurons = nCnxns = 0;
for(int i = 0; i < nLayers; i++)
{
nInLayer[i] = layerSize[i];
if(i > 0)
nCnxns += nInLayer[i] * nInLayer[i - 1];
nNeurons += nInLayer[i];
}
atPattern = 0;
cnxn = new Connection[nCnxns];
if(!cnxn)
{
cerr<<"Error during memory allocation of connections!\n";
return;
}
for(int atLayer = 0; atLayer < nLayers; atLayer++)
{
for(int atNeuron = 0; atNeuron < nInLayer[atLayer]; atNeuron++)
{
neuron[atLayer][atNeuron] = new Neuron;
if(!neuron[atLayer][atNeuron])
{
cerr<<"Error during memory allocation of neurons.\n"
<
}
}
}
}
bool Network::trained(void)
{
if(totalError < acceptableError)
return true;
else
return false;
}
bool Network::trainingFile(char* fileName)
{
ifstream dataFile(fileName);
if(!dataFile)
{
cerr<<"Error opening file "<
}
dataFile>>"nLayers: ";
dataFile>>nLayers;
nInLayer = new int[nLayers];
if(!nInLayer)
{
cerr<<"Error during memory allocation for nInLayer[]!\n";
return(false);
}
nNeurons = nCnxns = 0;
for(int atLayer = 0; atLayer < nLayers; atLayer++)
{
dataFile>>nInLayer[atLayer];
if(atLayer > 0)
nCnxns += nInLayer[atLayer] * nInLayer[atLayer - 1];
nNeurons += nInLayer[atLayer];
}
cnxn = new Connection[nCnxns];
if(!cnxn)
{
cerr<<"Error during allocation of "<
}
for(atLayer = 0; atLayer < nLayers; atLayer++)
{
for(int atNeuron = 0; atNeuron < nInLayer[atLayer]; atNeuron++)
{
neuron[atLayer][atNeuron] = new Neuron;
if(!neuron[atLayer][atNeuron])
{
cerr<<"Error during memory allocation of neurons.\n"
<
}
}
}
float learningConstant, momentum, samadCoefficient;
dataFile>>"acceptableError:">>acceptableError;
dataFile>>"learningConstant:">>learningConstant;
dataFile>>"Momentum:">>momentum;
dataFile>>"samadCoefficient:">>samadCoefficient;
int atCnxn = 0;
for( atLayer = 0; atLayer < nLayers - 1; atLayer++)
{
for(int atLowNeuron = 0; atLowNeuron < nInLayer[atLayer]; atLowNeuron++)
{
for(int atHighNeuron = 0; atHighNeuron < nInLayer[atLayer + 1]; atHighNeuron++)
{
cnxn[atCnxn].set(neuron[atLayer][atLowNeuron],neuron[atLayer + 1][atHighNeuron]);
cnxn[atCnxn].setRandom(learningConstant, momentum, samadCoefficient);
++atCnxn;
}
}
}
dataFile>>"nPatterns:">>nPatterns;
dataFile>>"width:">>inWidth;
dataFile>>"depth:">>inDepth;
dataFile>>"outputWidth:">>outWidth;
dataFile>>"outputDepth:">>outDepth;
pat = new Pattern[nPatterns];
if(!pat)
{
cerr<<"Error during memory allocation for patterns\n";
return(false);
}
for(int i = 0; i < nPatterns; i++)
if(!pat[i].getMem(inWidth * inDepth, outWidth * outDepth))
return(false);
if(!pat)
{
cerr<<"Error during allocation of memory for patterns!\n";
return(false);
}
int temp;
for(int atPattern = 0; atPattern < nPatterns; atPattern++)
{
for(int i = 0; i < inWidth * inDepth; i++)
{
dataFile>>temp;
if(temp == 1)
pat[atPattern].in[i] = 1 - acceptableError;
else
pat[atPattern].in[i] = acceptableError;
}
for( i = 0; i < outWidth * outDepth; i++)
{
dataFile>>temp;
if(temp == 1)
pat[atPattern].out[i] = 1.0;
else
pat[atPattern].out[i] = 0;
}
}
dataFile.close();
atPattern = 0;
totalError = 0;
return true;
}
void Network::zeroTotalError(void)
{
totalError = 0.0;
}
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