int main() {
printf("Neural Network training\n");
const unsigned int num_input = 3;
const unsigned int num_output = 4;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = 30;
const float desired_error = (const float) 0.02;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 10;
struct fann *ann = fann_create_standard(
num_layers,
num_input,
num_neurons_hidden,
num_neurons_hidden,
// num_neurons_hidden,
num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
// fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
struct fann_train_data *data = fann_read_train_from_file("../training.data");
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
fann_destroy_train(data);
fann_save(ann, "../neural.net");
fann_destroy(ann);
return 0;
}
void CNeuroNetwok::Init(const int nNeirons, const int nResults, const int nEpochs)
{
const unsigned int nLayersCount = 3;
const unsigned int nHiddenNeironsCount = 3;
m_nEpochsCount = nEpochs;
// Создаем нейросеть
// Количество входных нейронов столько же, сколько и входных параметров
// Выходных нейронов столько же, сколько и результатов.
m_pANN = fann_create_standard(nLayersCount, nNeirons, nHiddenNeironsCount, nResults);
if (!m_pANN)
throw std::runtime_error("Failed to init fann!");
// Выполняем очень важные инициализации :)
fann_set_activation_steepness_hidden(m_pANN, 1);
fann_set_activation_steepness_output(m_pANN, 1);
fann_set_activation_function_hidden(m_pANN, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(m_pANN, FANN_SIGMOID_SYMMETRIC);
fann_set_train_stop_function(m_pANN, FANN_STOPFUNC_BIT);
fann_set_bit_fail_limit(m_pANN, 0.01f);
m_bIsInited = true;
}
int
main(int argc, char **argv)
{
const unsigned int num_input = 360;
const unsigned int num_output = 1;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = num_input / 2;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 300;
const unsigned int epochs_between_reports = 10;
struct fann_train_data * data = NULL;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden / 2, num_output);
unsigned int i, j;
if (argc != 2)
{
fprintf(stderr, "Use: %s arquivoTreino\n", argv[0]);
exit(1);
}
fann_set_activation_function_hidden(ann, FANN_ELLIOT);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_learning_rate(ann, 0.1);
fann_set_learning_momentum(ann, 0.6);
data = fann_read_train_from_file(argv[1]);
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
free(data);
fann_save(ann, ARQ_RNA);
fann_destroy(ann);
return 0;
}
void MainWindow::onLearnMyocardial()
{
QString trainFileName = QFileDialog::getOpenFileName(this, tr("Open training file"),
"", tr("Training file (*.data)"));
if(trainFileName.isEmpty())
return;
if(mAnn != 0)
fann_destroy(mAnn);
const unsigned int num_input = 3;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 5;
const float desired_error = 0.001f;
const unsigned int max_epochs = 50000;
const unsigned int epochs_between_reports = 0;
mAnn= fann_create_standard(num_layers, num_input,
num_neurons_hidden, num_output);
fann_set_activation_function_hidden(mAnn, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(mAnn, FANN_SIGMOID_SYMMETRIC);
QByteArray ba = trainFileName.toLocal8Bit();
fann_train_on_file(mAnn, ba.data(), max_epochs,
epochs_between_reports, desired_error);
QMessageBox::information(this, "Training Complete", QString("Training error = ") + QString::number(fann_get_MSE(mAnn)));
ui->tabWidget->setTabEnabled(1, true);
}
int main() {
//unsigned int layers[] = {17, 500, 400, 300, 200, 1};
//unsigned int layerCount = sizeof(layers) / sizeof(unsigned int);
const char* netLoadFile =
"data/AAPL_ADBE_ATVI_COKE_EBAY_EXPE_NFLX_VA.300.250.200_100.net";
const char* netSaveFile =
"data/AAPL_ADBE_ATVI_COKE_EBAY_EXPE_NFLX_VA.300.250.200_200.net";
const float desired_error = (const float) 0.10;
const unsigned int max_epochs = 100;
const unsigned int epochs_between_reports = 1;
//struct fann *ann = fann_create_standard_array(layerCount, layers);
struct fann *ann = fann_create_from_file(netLoadFile);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
printf("%s\n", netSaveFile);
fann_train_on_file(ann,
"data/AAPL_ADBE_ATVI_COKE_EBAY_EXPE_NFLX_VA.train.fann", max_epochs,
epochs_between_reports, desired_error);
fann_save(ann, netSaveFile);
fann_destroy(ann);
printf("%s\n", netSaveFile);
return 0;
}
int main(int argc , char **argv) {
unsigned int num_data;
unsigned int num_input;
unsigned int num_output;
const unsigned int num_layers=3 ; //input, hidden and output
unsigned int num_neurons_hidden;
const float desired_error = 0.0001;
const unsigned int max_iterations = 1000;
const unsigned int iterations_between_reports = 5;
if (argc != 5) {
printf("usage : train trainFile.txt testFile.txt ouputNetBasename nbrHiddenNeurons\n");
return 0;
}
FILE *in= fopen(argv[1],"r");
fscanf(in,"%d %d %d\n",&num_data,&num_input,&num_output);
fclose(in);
num_neurons_hidden = (unsigned int) atoi (argv[4]);
struct fann *ann = fann_create_standard( num_layers, num_input, num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
train(ann, argv[1],argv[2],argv[3], max_iterations,iterations_between_reports, desired_error,num_neurons_hidden);
fann_destroy(ann);
return 0;
}
int main(int argc, const char* argv[])
{
const unsigned int max_epochs = 1000;
unsigned int num_threads = 1;
struct fann_train_data *data;
struct fann *ann;
long before;
float error;
unsigned int i;
if(argc == 2)
num_threads = atoi(argv[1]);
data = fann_read_train_from_file("../../datasets/mushroom.train");
ann = fann_create_standard(3, fann_num_input_train_data(data), 32, fann_num_output_train_data(data));
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID);
before = GetTickCount();
for(i = 1; i <= max_epochs; i++)
{
error = num_threads > 1 ? fann_train_epoch_irpropm_parallel(ann, data, num_threads) : fann_train_epoch(ann, data);
printf("Epochs %8d. Current error: %.10f\n", i, error);
}
printf("ticks %d", GetTickCount()-before);
fann_destroy(ann);
fann_destroy_train(data);
return 0;
}
int main( int argc, char** argv )
{
const unsigned int num_input = 3;
const unsigned int num_output = 1;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = 5;
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 5000;
const unsigned int epochs_between_reports = 1000;
struct fann_train_data * data = NULL;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
data = fann_read_train_from_file("../../datasets/scaling.data");
fann_set_scaling_params(
ann,
data,
-1, /* New input minimum */
1, /* New input maximum */
-1, /* New output minimum */
1); /* New output maximum */
fann_scale_train( ann, data );
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
fann_destroy_train( data );
fann_save(ann, "scaling.net");
fann_destroy(ann);
return 0;
}
OcrNet::OcrNet(int data_set_size, int num_rows, int num_columns, int num_hidden, int num_output, int max_epochs, float error)
{
const int num_layers = 3;
DataSetSize = data_set_size;
Rows = num_rows;
Cols = num_columns;
MaxEpochs = max_epochs;
Error = error;
Ann = new struct fann* [DataSetSize];
for(int i=0; i < DataSetSize; i++)
{
Ann[i] = fann_create_standard(num_layers, Rows * Cols, num_hidden, num_output);
fann_set_activation_function_hidden(Ann[i], FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(Ann[i], FANN_SIGMOID_SYMMETRIC);
}
// fann_set_activation_steepness_hidden(Ann, 1);
// fann_set_activation_steepness_output(Ann, 1);
// fann_set_learning_rate(Ann, 0.01);
// fann_set_learning_momentum(Ann, 0.7);
// fann_set_rprop_increase_factor(Ann, 5);
// fann_set_rprop_decrease_factor(Ann, 0.007);
// fann_set_rprop_delta_max(Ann, 17);
// fann_set_rprop_delta_zero(Ann, 0.01);
}
void BasicBrain::makeNN() {
assert(layout_.numLayers_ >= 3);
assert(layout_.numLayers_ < 20);
unsigned int* layerArray = new unsigned int[layout_.numLayers_];
layerArray[0] = layout_.numInputs_;
for(size_t i = 1; i < (layout_.numLayers_ - 1); i++) {
layerArray[i] = layout_.neuronsPerHidden;
}
layerArray[layout_.numLayers_ - 1] = layout_.numOutputs;
nn_ = fann_create_standard_array(layout_.numLayers_, layerArray);
fann_set_activation_function_hidden(nn_, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(nn_, FANN_SIGMOID_SYMMETRIC);
inputs_ = new fann_type[layout_.numInputs_];
reset();
delete[] layerArray;
#ifdef _CHECK_BRAIN_ALLOC
size_t id = ++nnAllocCnt_;
nnAllocs_[nn_] = id;
std::cerr << "alloc: " << id << std::endl;
#endif
}
int main()
{
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 3;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann = fann_create_standard(num_layers, num_input,
num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_train_on_file(ann, "xor.data", max_epochs,
epochs_between_reports, desired_error);
fann_save(ann, "xor_float.net");
fann_destroy(ann);
return 0;
}
int main()
{
const unsigned int num_input = 1;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 10;
const float desired_error = (const float) 0.0003;
const unsigned int max_epochs = 50000;
const unsigned int epochs_between_reports = 1000;
struct fann_train_data *train_data;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
train_data=fann_read_train_from_file("seno.data");
//AQUI
//fann_set_scaling_params(ann,train_data,-1.0,1.0,-1.0,1.0);
//fann_scale_train(ann,train_data);
//fann_scale_train_data(train_data,-1,1);
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
fann_save(ann, "mlp2.net");
fann_destroy(ann);
return 0;
}
int main()
{
fann_type *calc_out;
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 9;
const float desired_error = (const float) 0;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann;
struct fann_train_data *data;
unsigned int i = 0;
unsigned int decimal_point;
printf("Creating network.\n");
ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
data = fann_read_train_from_file("osyslec_train.data");
fann_set_activation_steepness_hidden(ann, 1);
fann_set_activation_steepness_output(ann, 1);
fann_set_activation_function_hidden(ann, FANN_SIGMOID);
fann_set_activation_function_output(ann, FANN_SIGMOID);
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
fann_set_bit_fail_limit(ann, 0.01f);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_init_weights(ann, data);
printf("Training network.\n");
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network. %f\n", fann_test_data(ann, data));
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("GG test (%f,%f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Saving network.\n");
fann_save(ann, "osyslec_train_float.net");
decimal_point = fann_save_to_fixed(ann, "osyslec_train_fixed.net");
fann_save_train_to_fixed(data, "osyslec_train_fixed.data", decimal_point);
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
int main()
{
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 3;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
unsigned int i;
fann_type *calc_out;
struct fann_train_data *data;
struct fann *ann = fann_create_standard(num_layers,
num_input, num_neurons_hidden, num_output);
data = fann_read_train_from_file("xor.data");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);
train_on_steepness_file(ann, "xor.data", max_epochs,
epochs_between_reports, desired_error, (float) 1.0, (float) 0.1,
(float) 20.0);
fann_set_activation_function_hidden(ann, FANN_THRESHOLD_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_THRESHOLD_SYMMETRIC);
for(i = 0; i != fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));
}
fann_save(ann, "xor_float.net");
fann_destroy(ann);
fann_destroy_train(data);
return 0;
}
void Classifier::createNew()
{
fann_destroy(neuralNetwork);
neuralNetwork = fann_create_standard(NUM_LAYERS, NUM_INP, numHidden, NUM_OUTP);
//fann_set_activation_function_hidden(neuralNetwork, FANN_SIGMOID_SYMMETRIC);
//fann_set_activation_function_output(neuralNetwork, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_hidden(neuralNetwork, FANN_LINEAR_PIECE_SYMMETRIC);
fann_set_activation_function_output(neuralNetwork, FANN_LINEAR_PIECE_SYMMETRIC);
fann_set_training_algorithm(neuralNetwork,FANN_TRAIN_INCREMENTAL);
}
int main(int argc, char *argv[])
{
unsigned int layers[3] = {38, 17, 9};
struct fann *ann = fann_create_standard_array(3, layers);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_train_on_file(ann, "scotland.data", 100000, 100, 0.00001);
fann_save(ann, "scotland_test.net");
fann_destroy(ann);
return 0;
}
struct fann *random_network(){
// create and randomize activation function
struct fann *ann = fann_create_standard(num_layers,num_input,num_hidden_nodes,num_output);
fann_set_activation_function_hidden(ann,FANN_SIGMOID);
fann_set_activation_function_output(ann,FANN_SIGMOID);
// randomize weights
fann_randomize_weights(ann,-1,1);
// return
return ann;
}
/***
* @function rspamd_fann.create(nlayers, [layer1, ... layern])
* Creates new neural network with `nlayers` that contains `layer1`...`layern`
* neurons in each layer
* @param {number} nlayers number of layers
* @param {number} layerI number of neurons in each layer
* @return {fann} fann object
*/
static gint
lua_fann_create (lua_State *L)
{
#ifndef WITH_FANN
return 0;
#else
struct fann *f, **pfann;
guint nlayers, *layers, i;
nlayers = luaL_checknumber (L, 1);
if (nlayers > 0) {
layers = g_malloc (nlayers * sizeof (layers[0]));
if (lua_type (L, 2) == LUA_TNUMBER) {
for (i = 0; i < nlayers; i ++) {
layers[i] = luaL_checknumber (L, i + 2);
}
}
else if (lua_type (L, 2) == LUA_TTABLE) {
for (i = 0; i < nlayers; i ++) {
lua_rawgeti (L, 2, i + 1);
layers[i] = luaL_checknumber (L, -1);
lua_pop (L, 1);
}
}
f = fann_create_standard_array (nlayers, layers);
fann_set_activation_function_hidden (f, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output (f, FANN_SIGMOID_SYMMETRIC);
fann_set_training_algorithm (f, FANN_TRAIN_INCREMENTAL);
fann_randomize_weights (f, 0, 1);
if (f != NULL) {
pfann = lua_newuserdata (L, sizeof (gpointer));
*pfann = f;
rspamd_lua_setclass (L, "rspamd{fann}", -1);
}
else {
lua_pushnil (L);
}
g_free (layers);
}
else {
lua_pushnil (L);
}
return 1;
#endif
}
int main()
{
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 32;
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 300;
const unsigned int epochs_between_reports = 10;
struct fann *ann;
struct fann_train_data *train_data, *test_data;
unsigned int i = 0;
printf("Creating network.\n");
train_data = fann_read_train_from_file("../datasets/mushroom.train");
ann = fann_create_standard(num_layers,
train_data->num_input, num_neurons_hidden, train_data->num_output);
printf("Training network.\n");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
fann_set_activation_function_output(ann, FANN_SIGMOID_STEPWISE);
/*fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL); */
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network.\n");
test_data = fann_read_train_from_file("../datasets/mushroom.test");
fann_reset_MSE(ann);
for(i = 0; i < fann_length_train_data(test_data); i++)
{
fann_test(ann, test_data->input[i], test_data->output[i]);
}
printf("MSE error on test data: %f\n", fann_get_MSE(ann));
printf("Saving network.\n");
fann_save(ann, "mushroom_float.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
int main( void ) {
fann_type *calc_out;
fann_type input[ N ];
int i;
struct fann *ann = fann_create_standard( 3, N, N, 1 );
fann_set_activation_function_hidden( ann, ACT );
fann_set_activation_function_output( ann, FANN_LINEAR );
for( i=0; i<1000; i++ )
calc_out = fann_run(ann, input);
fann_destroy(ann);
return 0;
}
int main(int argc, char* argv[]) {
bool iflag, oflag, tflag, nflag, gflag;
int flag;
struct fann *nNetwork;
char* inputFileName;
char* dataFileName;
char* outputFileName;
srand(time(NULL));
while((flag = getopt(argc,argv,"i:o:t:")) != -1) {
switch(flag) {
case 'i':
inputFileName = optarg;
iflag = true;
break;
case 'o':
outputFileName = optarg;
oflag = true;
break;
case 't':
dataFileName = optarg;
tflag = true;
break;
}
}
if(iflag) {
nNetwork = fann_create_from_file(inputFileName);
}
else {
nNetwork = fann_create_standard(3,NUM_IN_OUT,NUM_IN_OUT,NUM_IN_OUT);
}
fann_set_activation_function_hidden(nNetwork, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(nNetwork, FANN_SIGMOID_SYMMETRIC);
if(tflag) {
fann_train_on_file(nNetwork,dataFileName,MAX_ITERATIONS,50,ERROR);
}
if(oflag) {
fann_save(nNetwork,outputFileName);
}
fann_destroy(nNetwork);
return 0;
}
int main()
{
struct fann *ann;
struct fann_train_data *train_data, *test_data;
const float desired_error = (const float) 0.001;
unsigned int max_neurons = 40;
unsigned int neurons_between_reports = 1;
printf("Reading data.\n");
train_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.test");
fann_scale_train_data(train_data, 0, 1);
fann_scale_train_data(test_data, 0, 1);
printf("Creating network.\n");
ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR_PIECE);
fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
fann_print_parameters(ann);
printf("Training network.\n");
fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
fann_print_connections(ann);
printf("\nTrain error: %f, Test error: %f\n\n", fann_test_data(ann, train_data),
fann_test_data(ann, test_data));
printf("Saving network.\n");
fann_save(ann, "two_spirali.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
NeuralNet::NeuralNet(){
cout << "Initializing neural network" << endl;
this->numInputNeurons = 144;
this->numOutputNeurons = 1;
this->numLayers = 3;
this->numHiddenNeurons = 140;
this->ptrNeuralNet = fann_create_standard(numLayers, numInputNeurons, numHiddenNeurons, numOutputNeurons);
fann_set_activation_steepness_hidden(this->ptrNeuralNet, 1);
fann_set_activation_steepness_output(this->ptrNeuralNet, 1);
//sigmoidal function
fann_set_activation_function_hidden(this->ptrNeuralNet, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(this->ptrNeuralNet, FANN_SIGMOID_SYMMETRIC);
fann_set_train_stop_function(this->ptrNeuralNet, FANN_STOPFUNC_BIT);
fann_set_bit_fail_limit(this->ptrNeuralNet, 0.01f);
fann_set_training_algorithm(this->ptrNeuralNet, FANN_TRAIN_RPROP);
}
int main()
{
//FANN TRAINING:
const unsigned int num_input = 4;
const unsigned int num_output = 3;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 4;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 200000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann = fann_create_standard(num_layers, num_input,
num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_train_on_file(ann, "irisDataSet.data", max_epochs,
epochs_between_reports, desired_error);
fann_save(ann, "irisFANN_float.net");
fann_destroy(ann);
//FANN TESTING
fann_type *calc_out;
fann_type input[4];
struct fann *annR = fann_create_from_file("irisFANN_float.net");
input[0] = 5.1;
input[1] = 3.5;
input[2] = 1.4;
input[3] = 0.2;
calc_out = fann_run(annR, input);
std::cout << "Testing irisFANN, with data: " << input[0] << ", " << input[1] << ", " << input[2] << ", " << input[3] << ", "
<< ", output: " << calc_out[0] << ", " << calc_out[1] << ", " << calc_out[2] << "\n";
fann_destroy(annR);
return 0;
}
/*! ann:set_activation_function_hidden(function)
*# Sets the activation function for the hidden layer neurons.
*x ann:set_activation_function_hidden(fann.FANN_SIGMOID_SYMMETRIC)
*-
*/
static int ann_set_activation_function_hidden(lua_State *L)
{
struct fann **ann;
int fun;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
if(lua_gettop(L) < 2)
luaL_error(L, "insufficient parameters");
fun = lua_tointeger(L, 2);
#ifdef FANN_VERBOSE
printf("Setting hidden activation function to %d\n", fun);
#endif
fann_set_activation_function_hidden(*ann, fun);
return 0;
}
int main()
{
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 350;
const unsigned int epochs_between_reports = 25;
struct fann *ann;
struct fann_train_data *train_data;
unsigned int i = 0;
printf("Creating network.\n");
train_data = fann_read_train_from_file("ann_training_data");
// Using incremental training -> shuffle training data
fann_shuffle_train_data(train_data);
// ann = fann_create_standard(num_layers,
// train_data->num_input, num_neurons_hidden, train_data->num_output);
//ann = fann_create_standard(500, 2, 50, 50, 21);
unsigned int layers[4] = {150, 70, 30, 22};
ann = fann_create_standard_array(4, layers);
printf("Training network.\n");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
fann_set_activation_function_output(ann, FANN_LINEAR_PIECE); //FANN_SIGMOID_STEPWISE);
fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
printf("Saving network.\n");
fann_save(ann, "mymoves_gestures.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy(ann);
return 0;
}
int main_sample()
{
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 3;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann = fann_create_standard(num_layers, num_input,
num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_train_on_file(ann, "xor.data", max_epochs,
epochs_between_reports, desired_error);
fann_save(ann, "xor_float.net");
fann_type *calc_out;
fann_type input[2];
//ann = fann_create_from_file("xor_float.net");
input[0] = -1;
input[1] = 1;
calc_out = fann_run(ann, input);
printf("xor test (%f,%f) -> %f\n", input[0], input[1], calc_out[0]);
fann_destroy(ann);
getchar();
return 0;
}
int main ( int argc, char **argv )
{
srand(time(NULL));
if ( argc<=1 )
{
// printf ( "neuro num\r\n" );
// exit ( 0 );
}
if (argc>2)
{
//desired_error=atof(argv[2]);
numn=atoi(argv[1]);
l1n=atoi(argv[2]);
if (argc>3)
l2n=atoi(argv[3]);
if (argc>4)
l3n=atoi(argv[4]);
if (argc>5)
l4n=atoi(argv[5]);
if (argc>6)
l5n=atoi(argv[6]);
if (argc>7)
l6n=atoi(argv[7]);
}
signal ( 2, sig_term );
srand ( time ( NULL ) );
printf("loading training data...");
train_data = fann_read_train_from_file ( "train.dat" );
test_data = fann_read_train_from_file ( "test.dat" );
weight_data=fann_merge_train_data(train_data,test_data);
cln_weight_data=fann_duplicate_train_data(weight_data);
cln_test_data=fann_duplicate_train_data(test_data);
cln_train_data=fann_duplicate_train_data(train_data);
//num_neurons_hidden = atoi ( argv[1] );
srand(time(NULL));
y=atoi(argv[2]);
lay=atoi(argv[1]);
ln=lay+2;
if (lay==1)
y2=train_data->num_output;
best_perc=1;
printf("\r\ndoing %ux%u [layers=%u,out=%u]",lay,y,ln, train_data->num_output);
while (true)
{
neur1=1+(rand()%y);
neur2=1+(rand()%y);
conn_rate=0.5f+((rand()%50)*0.01f);
printf("\r\n%2dx%-4d: ",neur1,neur2);
// printf("create network: layers=%d l1n=%d l2n=%d l3n=%d l4n=%d\ l5n=%d l6n=%dr\n",numn,l1n,l2n,l3n,l4n,l5n,l6n);
ann = fann_create_standard (//conn_rate,
ln,
train_data->num_input,
neur1,
neur2,
train_data->num_output );
//fann_init_weights ( ann, train_data );
printf(" [%p] ",ann);
if ( ( int ) ann==NULL )
{
printf ( "error" );
exit ( 0 );
}
fann_set_activation_function_hidden(ann,FANN_SIGMOID);
fann_set_activation_function_output(ann,FANN_SIGMOID);
rebuild_functions(neur1);
fann_set_training_algorithm ( ann, FANN_TRAIN_RPROP );
fann_set_sarprop_temperature(ann,15000.0f);
//fann_randomize_weights ( ann, -((rand()%10)*0.1f), ((rand()%10)*0.1f) );
fann_init_weights(ann,train_data);
got_inc=0;
prev_epoch_mse=1;
//
epochs=0;
unsigned last_best_perc_epoch=0;
unsigned last_sync_epoch=0;
unsigned last_ftest_secs=0;
last_sync_epoch=0;
last_best_perc_epoch=0;
if (good_ann)
fann_destroy(good_ann);
good_ann=fann_copy(ann);
unlink(histfile);
for (u=0;u<1000;u++)
{
fflush(NULL);
train_mse=fann_train_epoch(ann, train_data);
if (jitter_train)
apply_jjit(train_data,cln_train_data);
if (time(NULL)-last_ftest_secs>=1)
{
//printf("\r\n%5u %9.6f %5.2f ",epochs,train_mse,test_perc);
//printf(" %4.2f",test_perc);
printf(".");
last_ftest_secs=time(NULL);
}
ftest_data();
plot(epochs,train_mse,test_mse);
/* if (epochs>10&&((int)test_perc==43||(int)test_perc==57))
{
printf(" [excluded %.2f] ",test_perc);
break;
} else {
} */
//printf("excluded %f ",test_perc);
double prev_test_perc;
// if (prev_epoch_mse==best_perc)
// printf("o");
if ((int)test_perc>(int)train_perc&&epochs-last_stat_epoch>10)
{
fann_destroy(good_ann);
good_ann=fann_copy(ann);
if (test_perc!=prev_test_perc)
printf("%.2f [%f]",test_perc,train_mse);
//printf(" sync[%4.2f]",test_perc);
last_stat_epoch=epochs;
}
else if (epochs-last_sync_epoch>111500)
{
last_sync_epoch=epochs;
}
if (epochs>210&&test_perc>best_perc)
{
// u--;
// fann_destroy(good_ann);
// good_ann=fann_copy(ann);
printf(" [saved best %.0f] ",test_perc);
last_stat_epoch=epochs;
// printf("%f",test_perc);
// fann_destroy(ann);
// ann=fann_copy(good_ann);
fann_save(ann,"mutate-best.net");
best_perc=test_perc;
printf(" %6.2f [%f]",test_perc,train_mse);
last_best_perc_epoch=epochs;
}
else if (epochs>11100&&((int)test_perc<=63||(int)test_perc==(int)prev_test_perc))
{
//best_perc=test_perc;
// printf("x");
// printf(".");
//printf("\r%6.8f",train_mse);
// printf("done\r\n");
break;
}
static unsigned last_restore_epoch=0;
if (epochs>100&&test_mse-train_mse>=0.25f&&epochs-last_restore_epoch>=120)
{
/* fann_set_learning_rate ( ann,0.31f+(rand()%90)*0.01f);
fann_set_learning_momentum(ann,(rand()%90)*0.01f);
printf(" [restored @ %u lr %.2f mm %.2f]",epochs,fann_get_learning_rate(ann),
fann_get_learning_momentum(ann));
fann_destroy(ann);
ann=fann_copy(good_ann);
last_stat_epoch=epochs;
last_restore_epoch=epochs; */
double rdec,rinc;
rdec=0.0101f+((rand()%100)*0.00001f);
if (!rdec)
rdec=0.01f;
rinc=1.0001f+((rand()%90)*0.00001f);
if (!rinc)
rinc=1.1f;
static double prev_test_epoch_mse;
// rinc+=diff_mse*0.000001f;
// fann_set_rprop_increase_factor(ann,rinc );
// fann_set_rprop_decrease_factor(ann, rdec);
}
else if (test_mse-train_mse<=0.1f)
{
fann_destroy(good_ann);
good_ann=fann_copy(ann);
// printf("s");
}
else
{
fann_set_sarprop_temperature(ann,fann_get_sarprop_temperature(ann)-0.0001f);
}
static unsigned last_train_change_epoch=0;
if (test_mse>=train_mse&&epochs-last_train_change_epoch>=100)
{
last_train_change_epoch=epochs;
//fann_set_training_algorithm(ann,FANN_TRAIN_SARPROP);
jitter_train=0;
}
else
{
//fann_set_training_algorithm(ann,FANN_TRAIN_RPROP);
jitter_train=0;
}
got_inc=test_perc-prev_epoch_mse;
prev_epoch_mse=test_perc;
prev_test_perc=test_perc;
epochs++;
if (epochs-last_best_perc_epoch>511500)
{
printf(" failed");
break;
}
if (epochs>2200&&(int)train_perc<40)
{
printf("skip 1\r\n");
break;
}
if ((int)test_perc>=80)
{
printf("\r\ngot it %f\r\n",test_perc);
fann_save(ann,"good.net");
exit(0);
}
// printf("\n%6u ",epochs);
}
printf(" %6.2f inc: %.2f",test_perc,got_inc);
// printf("%6.2f %6.2f",train_perc,test_perc);
fann_destroy ( ann );
}
fann_destroy_train ( train_data );
fann_destroy_train ( test_data );
fann_destroy ( ann );
return 0;
}
int main()
{
struct fann *ann;
struct fann_train_data *train_data, *test_data;
const float desired_error = (const float)0.0;
unsigned int max_neurons = 30;
unsigned int neurons_between_reports = 1;
unsigned int bit_fail_train, bit_fail_test;
float mse_train, mse_test;
unsigned int i = 0;
fann_type *output;
fann_type steepness;
int multi = 0;
enum fann_activationfunc_enum activation;
enum fann_train_enum training_algorithm = FANN_TRAIN_RPROP;
printf("Reading data.\n");
train_data = fann_read_train_from_file("../benchmarks/datasets/parity8.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/parity8.test");
fann_scale_train_data(train_data, -1, 1);
fann_scale_train_data(test_data, -1, 1);
printf("Creating network.\n");
ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
fann_set_training_algorithm(ann, training_algorithm);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
if(!multi)
{
/*steepness = 0.5;*/
steepness = 1;
fann_set_cascade_activation_steepnesses(ann, &steepness, 1);
/*activation = FANN_SIN_SYMMETRIC;*/
activation = FANN_SIGMOID_SYMMETRIC;
fann_set_cascade_activation_functions(ann, &activation, 1);
fann_set_cascade_num_candidate_groups(ann, 8);
}
if(training_algorithm == FANN_TRAIN_QUICKPROP)
{
fann_set_learning_rate(ann, 0.35);
fann_randomize_weights(ann, -2.0,2.0);
}
fann_set_bit_fail_limit(ann, 0.9);
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
fann_print_parameters(ann);
fann_save(ann, "cascade_train2.net");
printf("Training network.\n");
fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
fann_print_connections(ann);
mse_train = fann_test_data(ann, train_data);
bit_fail_train = fann_get_bit_fail(ann);
mse_test = fann_test_data(ann, test_data);
bit_fail_test = fann_get_bit_fail(ann);
printf("\nTrain error: %f, Train bit-fail: %d, Test error: %f, Test bit-fail: %d\n\n",
mse_train, bit_fail_train, mse_test, bit_fail_test);
for(i = 0; i < train_data->num_data; i++)
{
output = fann_run(ann, train_data->input[i]);
if((train_data->output[i][0] >= 0 && output[0] <= 0) ||
(train_data->output[i][0] <= 0 && output[0] >= 0))
{
printf("ERROR: %f does not match %f\n", train_data->output[i][0], output[0]);
}
}
printf("Saving network.\n");
fann_save(ann, "cascade_train.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
int main()
{
fann_type *calc_out;
const unsigned int num_input = 22500;
const unsigned int num_output = 1;
//const unsigned int num_layers = 4;
const unsigned int num_layers = 4;
/* this value can be changed to tweak the network */
const unsigned int num_neurons_hidden = 50;
//const unsigned int num_neurons_hidden = 150;
const float desired_error = (const float) 0.02;
const unsigned int max_epochs = 15000;
const unsigned int epochs_between_reports = 20;
float learning_rate = .5;
struct fann *ann;
struct fann_train_data *data;
int num_neurons = 0;
unsigned int i = 0;
unsigned int decimal_point;
/* CREATING NETWORK */
ann = fann_create_standard(num_layers, num_input,
num_neurons_hidden,
num_neurons_hidden, num_output);
/* reading training data */
data = fann_read_train_from_file("training.data");
fann_set_activation_steepness_hidden(ann, 1);
fann_set_activation_steepness_output(ann, 1);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_init_weights(ann, data);
/*
TRAINING NETWORK
run x epochs at learn rate .y
*/
//fann_set_learning_rate(ann, .7);
//fann_train_on_data(ann, data, 200, epochs_between_reports, .4);
//fann_train_on_data(ann, data, 500, epochs_between_reports, .002);
fann_set_learning_rate(ann, .5);
fann_train_on_data(ann, data,5000, epochs_between_reports, .2);
//fann_train_on_data(ann, data,50, epochs_between_reports, .2);
fann_set_learning_rate(ann, .2);
fann_train_on_data(ann, data,1000, epochs_between_reports, .15);
//fann_train_on_data(ann, data,100, epochs_between_reports, .15);
fann_set_learning_rate(ann, .1);
fann_train_on_data(ann, data,5000, epochs_between_reports, .002);
//fann_train_on_data(ann, data,200, epochs_between_reports, .00002);
/* TESTING NETWORK */
printf("Testing network. %f\n", fann_test_data(ann, data));
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
/*printf("%f, should be %f, difference=%f\n",
calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0])); */
}
/* SAVING NETWORK */
fann_save(ann, "image_spam.net");
/* CLEANING UP */
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
