/**
* Loads a neural net.
*/
BKPNeuralNet::BKPNeuralNet( char* filename )
{
int i, j; /* Temporary counters. */
int lixo;
int layers, *nLayers;
FILE *f;
char name[128];
char namebin[128];
/* Checking arguments: */
if ( filename == NULL )
return;
/* Inserting the correct extensions: */
sprintf( name, "%s.nnt", filename );
sprintf( namebin, "%s.bin", filename );
f = fopen(name, "r");
if ( f == NULL )
return;
/* Number of layers: */
fscanf( f, "Number of layers: %d\n", &layers);
/* Space to the number of neurons on each layer: */
nLayers = (int*)malloc( layers * sizeof(int) );
if( nLayers == NULL )
return;
/* Number of neurons on each layer: */
for( i = 0; i < layers; i++ )
{
fscanf( f, "Neurons on layer %d: %d\n", &lixo, &(nLayers[i]) );
}
//BKPNeuralNet( layers, nLayers );
CreateNet( layers, nLayers );
/* Closing the text file: */
fclose( f );
/* Opening the binary file: */
f = fopen( namebin, "rb" );
if( f == NULL )
return;
/* Values of the neurons: */
for( i = 0; i < _layers; i++ )
fread( _neurons[i] , sizeof(float), _nLayers[i], f );
/* Weight values */
for( i = _layers - 2; i >= 0; i-- )
for( j = 0; j <= _nLayers[i+1]; j++ )
fread( _weights[i][j], sizeof(float), _nLayers[i]+1, f );
/* Closing file: */
fclose( f );
}
//------------------------------default ctor ----------------------------
//
// creates a ANN based on the default values in params.ini
//-----------------------------------------------------------------------
CNeuralNet::CNeuralNet()
{
m_NumInputs = CParams::iNumInputs;
m_NumOutputs = CParams::iNumOutputs;
m_NumHiddenLayers = CParams::iNumHidden;
m_NeuronsPerHiddenLyr = CParams::iNeuronsPerHiddenLayer;
CreateNet();
}
//------------------------------default ctor ----------------------------
//
// creates a ANN based on the default values in params.ini
//-----------------------------------------------------------------------
CNeuralNet::CNeuralNet()
{
m_NumInputs = 4;
m_NumOutputs = 2;
m_NumHiddenLayers = 1;
m_NeuronsPerHiddenLyr = 6;
CreateNet();
}
//------------------------------- ctor -----------------------------------
//
//------------------------------------------------------------------------
CNeuralNet::CNeuralNet(int NumInputs,
int NumOutputs,
int HiddenNeurons,
double LearningRate):m_iNumInputs(NumInputs),
m_iNumOutputs(NumOutputs),
m_iNumHiddenLayers(1),
m_iNeuronsPerHiddenLyr(HiddenNeurons),
m_dLearningRate(LearningRate),
m_dErrorSum(9999),
m_bTrained(false),
m_iNumEpochs(0)
{
CreateNet();
}
void Scene2Controller::Setup() {
// Box2D
CreateBorder();
CreateCourt();
CreateNet();
scene_begin_time = ofGetElapsedTimef();
const ofPoint left(256-64, 200);
const ofPoint right(768-64, 200);
const float pause = 0.5;
model_.dialogue
.Speed(20.0)
.Foreground(ofColor::white)
.Background(ofColor(0, 0, 0, 0))
.Position("left", left)
.Position("right", right)
.Message("You know why you're here, right?", "right").Pause(pause)
.Message("No.", "left").Pause(pause)
.Message("Because you're bad at tennis.\nI'm gonna make you good.", "right").Pause(2.0)
.Clear();
}
void Scene3Controller::Setup() {
// Box2D
CreateBorder();
CreateCourt();
CreateEight();
CreateNet();
model_.world.SetContactListener(this);
const ofPoint right(768-256, 50);
const float pause = 2.0;
model_.dialogue
.Speed(25.0)
.PunctuationDelay(0.0)
.FontSize(16.0)
.Foreground(ofColor::black)
.Position("court", ofPoint(768, 600))
.Position("right", right)
.Position("below", right + ofVec2f(0, 100)).Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.1); //bass sound
volume_targets.push_back(0.5); //high arpeg v1
volume_targets.push_back(0.0); //high arpeg v2
volume_targets.push_back(0.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Message("I don't believe in gravity, \nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 0;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.2); //bass sound
volume_targets.push_back(0.5); //high arpeg v1
volume_targets.push_back(0.0); //high arpeg v2
volume_targets.push_back(1.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause)
.Message("I have time, \nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 1;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.5); //bass sound
volume_targets.push_back(0.3); //high arpeg v1
volume_targets.push_back(0.0); //high arpeg v2
volume_targets.push_back(1.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause)
.Message("I have many racquets, \nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 2;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.5); //bass sound
volume_targets.push_back(0.2); //high arpeg v1
volume_targets.push_back(0.0); //high arpeg v2
volume_targets.push_back(1.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause)
.Message("We're playing a whole different game,\nwe will surely win this match!", "right").Then([this] () {
model_.served = true;
model_.ball_in_play = true;
model_.opponent_index = 3;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.5); //bass sound
volume_targets.push_back(0.2); //high arpeg v1
volume_targets.push_back(0.0); //high arpeg v2
volume_targets.push_back(0.9); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.3); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause)
.Message("I'm made of glass, \nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 4;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.5); //bass sound
volume_targets.push_back(0.0); //high arpeg v1
volume_targets.push_back(0.5); //high arpeg v2
volume_targets.push_back(0.0); //low arpeg v1
volume_targets.push_back(1.0); //low arpeg v2
volume_targets.push_back(0.0); //words 1
volume_targets.push_back(0.3); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause).Then([this] () {
model_.served = true;
model_.ball_in_play = true;
model_.opponent_index = 5;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.5); //bass sound
volume_targets.push_back(0.0); //high arpeg v1
volume_targets.push_back(0.5); //high arpeg v2
volume_targets.push_back(1.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.6); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.0); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
})
.Message("I'm late, I will...", "court").Pause(0.5).Clear().Pause(0.5)
.Message("I'm a blank slate,\nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 6;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.7); //bass sound
volume_targets.push_back(0.0); //high arpeg v1
volume_targets.push_back(0.5); //high arpeg v2
volume_targets.push_back(1.0); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.4); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.4); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause)
.Message("I've spent my life studying this game,\nI will surely win this match!", "right").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 7;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.7); //bass sound
volume_targets.push_back(0.0); //high arpeg v1
volume_targets.push_back(0.3); //high arpeg v2
volume_targets.push_back(0.5); //low arpeg v1
volume_targets.push_back(0.0); //low arpeg v2
volume_targets.push_back(0.4); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.4); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause).Foreground(ofColor::white)
.Message("I'm the score itself, \nI will surely win this match!", "right").Then([this] () {
model_.eight_body->SetActive(true);
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 8;
})
.Barrier("point").Then([this] () {
std::vector<float> volume_targets;
volume_targets.push_back(0.9); //bass sound
volume_targets.push_back(0.0); //high arpeg v1
volume_targets.push_back(0.2); //high arpeg v2
volume_targets.push_back(0.0); //low arpeg v1
volume_targets.push_back(1.0); //low arpeg v2
volume_targets.push_back(0.5); //words 1
volume_targets.push_back(0.0); //words 2
volume_targets.push_back(0.5); //words 3
scene_manager.GetMusic().GetSoundEffect<LoopSet>("opponents")->SetVolumeTargets(volume_targets);
}).Clear().Pause(pause).Foreground(ofColor::black)
.Message("I'm you, \nI will surely win this match!", "right").Then([this] () {
model_.eight_body->SetActive(false);
model_.served = false;
model_.ball_in_play = true;
model_.opponent_index = 9;
})
.Barrier("score").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
}).Barrier("score").Then([this] () {
model_.served = false;
model_.ball_in_play = true;
}).Barrier("point").Clear();
}
void CPnet::CPMenu(void) {
int selection;
while(selection != 8) {
cout << "=================================================================\n";
cout << "\tPerceptron Neural Net using Perceptron learning rule\n";
cout << "=================================================================\n\n";
cout << "[1]. Create a new Neural Net" << endl;
cout << "[2]. Train the current Neural Net" << endl;
cout << "[3]. Load a Neural Net from file" << endl;
cout << "[4]. Test Neural Net" << endl;
cout << "[5]. Save the current Net" << endl;
cout << "[6]. Set training display" << endl;
cout << "[7]. Display Neural Net Info" << endl;
cout << "[8]. Quit the program" << endl << endl;
cout << "selection: ";
selection = getche() - '0';
WAIT(0.5*SECOND);
CLEAR_SCREEN();
switch(selection) {
case 1:
SaveCurrentData();
cout << "\nCreating Neural Net...";
WAIT(1*SECOND);
CreateNet();
break;
case 2:
if (bNeuralNetCreated) {
cout << "\nTraining \"" << szNeuralNetName << "\" function..." << endl;
WAIT(0.7*SECOND);
cout << "Enter Learning rate: ";
cin >> LEARNING_RATE;
WAIT(0.7*SECOND);
cout << "Enter number of epochs: ";
cin >> CPN_ITER;
NormalizeInput();
NormalizeTarget();
CLEAR_SCREEN();
WAIT(0.7*SECOND);
cout << "\nTraining \"" << szNeuralNetName << "\" function..." << endl;
WAIT(2*SECOND);
CLEAR_SCREEN();
TrainNetwork();
} else {
UpdateScreen();
}
break;
case 3:
SaveCurrentData();
cout << "\n\nLoading Neural Net..." << endl;
WAIT(0.85*SECOND);
cout << "\nEnter the name of the Neural Net or the complete path where "
<< "the Net data can be found\n: ";
cin.sync();
cin.getline(szNeuralNetName, MAX_PATH);
LoadNet();
WAIT(1.5*SECOND);
CLEAR_SCREEN();
cout << "\n\n\n\n\n\n\n\n\n\t\t\tNeural Net loaded successfuly!" << endl;
UpdateScreen();
break;
case 4:
if (bNeuralNetTrained) {
cout << "\nTesting \"" << szNeuralNetName << "\" function..." << endl;
cout << "1. default test" << endl;
cout << "2. selective test" << endl;
cout << "\nselection: ";
int nNumOfPattern;
int sel = getche();
CLEAR_SCREEN();
WAIT(0.7*SECOND);
DeNormalizeInput();
DeNormalizeTarget();
switch(sel) {
case '1':
TestNetwork();
break;
case '2':
cout << "\nEnter the number of patterns to be tested: ";
cin >> nNumOfPattern;
SelectiveTest(nNumOfPattern);
UpdateScreen();
break;
default:
cout << "\nunknown selection." << endl;
UpdateScreen();
}
} else {
UpdateScreen();
}
break;
case 5:
if (bNeuralNetCreated) {
if (fExist(szNeuralNetName)) {
CLEAR_SCREEN();
WAIT(0.5*SECOND);
cout << "\n\"" << szNeuralNetName << "\"";
cout << ": this file already exist,do you want to overwrite it?" << endl;
cout << "Yes(y) No(n): ";
char response;
cin >> response;
response = tolower(response);
if(response == 'y') {
SaveNet();
} else {
WAIT(0.5*SECOND);
cout << "\nPlease enter a new name or a complete file path where the "
<< "Neural Net will be saved\n: ";
cin.sync();
cin.getline(szNeuralNetName, MAX_PATH);
SaveNet();
}
} else {
SaveNet();
}
CLEAR_SCREEN();
WAIT(1.5*SECOND);
cout << "\n\n\n\n\n\n\n\n\n\t\t\tThe Neural Net was saved successfuly!" << endl;
}
/**
* Loads a neural net.
*/
int LoadNet( char* filename, PtNet *myNet )
{
int i, j; /* Temporary counters. */
int lixo;
int layers, *nLayers;
FILE *f;
char name[128];
char namebin[128];
PtNet Net;
/* Checking arguments: */
if ( filename == NULL )
return -1;
/* Inserting the correct extensions: */
sprintf( name, "%s.nnt", filename );
sprintf( namebin, "%s.bin", filename );
f = fopen(name, "r");
if ( f == NULL )
return -3;
/* Number of layers: */
fscanf(f, "Number of layers: %d\n", &layers);
/* Space to the number of neurons on each layer: */
nLayers = (int*)malloc(layers*sizeof(int));
if (nLayers == NULL)
return -1;
/* Number of neurons on each layer: */
for ( i=0 ; i<layers ; i++ )
{
fscanf(f, "Neurons on layer %d: %d\n", &lixo, &(nLayers[i]));
printf( "Neurons on layer %d: %d\n", lixo, (nLayers[i]));
}
CreateNet( &Net, layers, nLayers);
/* Closing the text file: */
fclose(f);
/* Opening the binary file: */
f = fopen(namebin, "rb");
if ( f == NULL )
return -3;
/* Values of the neurons: */
for ( i=0 ; i<Net->Layers ; i++ )
{
fread(Net->Neurons[i] , sizeof(float), Net->NLayers[i], f);
/*for ( j=0 ; j<Net->NLayers[i] ; j++ )
fscanf(f, "%f", &(Net->Neurons[i][j]));*/
}
/* Weight values */
for( i=Net->Layers-2 ; i>=0 ; i-- )
for( j=0 ; j<=Net->NLayers[i+1] ; j++ )
{
fread(Net->Weights[i][j], sizeof(float), Net->NLayers[i]+1, f);
/*for( k=0 ; k<=Net->NLayers[i] ; k++ )
{
fscanf(f, "%f", &(Net->Weights[i][j][k]));
} */
}
/* Closing file: */
fclose(f);
/* Updating pointers: */
*myNet = Net;
return 0;
}
NeuronalNet::NeuronalNet(){
CreateNet();
}
BKPNeuralNet::BKPNeuralNet( int Layers, int *nLayers)
{
CreateNet( Layers, nLayers);
}
