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()
{
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;
}
/*! ann:train_on_file(file, max_epochs, epochs_between_reports, desired_error)
*# Trains the neural network on the data in the file {{file}}, for up to
*# {{max_epochs}} epochs, reporting every {{epochs_between_reports}}.
*# Training stops when the error reaches {{desired_error}}
*x ann:train_on_file("xor.data", 500000, 1000, 0.001)
*-
*/
static int ann_train_on_file(lua_State *L)
{
struct fann **ann;
const char *fname;
int max_epochs, epochs_between_reports;
float desired_error;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
if(lua_gettop(L) < 5)
luaL_error(L, "insufficient parameters");
fname = lua_tostring(L, 2);
max_epochs = lua_tointeger(L, 3);
epochs_between_reports = lua_tointeger(L, 4);
desired_error = lua_tonumber(L, 5);
#ifdef FANN_VERBOSE
printf("Training on file \"%s\" for up to %d epochs...\n", fname, max_epochs);
#endif
fann_train_on_file(*ann, fname, max_epochs, epochs_between_reports, desired_error);
return 0;
}
int main()
{
struct fann *ann = fann_create(1,0.7,5,72,60,30,10,3);
fann_train_on_file(ann, "training.data",200,10,0.1);
fann_save(ann, "gesture_classify.net");
fann_destroy(ann);
return 0;
}
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;
}
int main()
{
struct fann *ann ;
ann = fann_create_from_file("gesture_classify.net");
if(ann==NULL){
ROS_INFO("Creating an ANN....");
ann = fann_create(1,0.7,3,16,18,20);
ROS_INFO("Done....");
}
fann_train_on_file(ann, "training.data",2000,100,0.1);
fann_save(ann, "gesture_classify.net");
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()
{
//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;
}
int main(int argc, char *argv[])
{
int i = 0;
const unsigned int width = 10;
const unsigned int height = 10;
const unsigned int num_dimensions = 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_som(width, height, num_dimensions);
ann->som_params->som_topology = FANN_SOM_TOPOLOGY_HEXAGONAL;
ann->som_params->som_neighborhood = FANN_SOM_NEIGHBORHOOD_DISTANCE;
ann->som_params->som_learning_decay = FANN_SOM_LEARNING_DECAY_LINEAR;
ann->som_params->som_learning_rate = 0.01;
ann->som_params->som_learning_rate_constant = 0.01;
/* Example using init_weights */
struct fann_train_data *data = fann_read_train_from_file("som_train.data");
for (i = 1; i < argc; i++) {
if (strcmp(argv[i], "-init") == 0)
fann_init_weights_som(ann, data);
else if (strcmp(argv[i], "-epoch") == 0) {
fann_train_epoch(ann, data);
return EXIT_SUCCESS;
}
}
/* Example without init_weights (random initialization)*/
fann_train_on_file(ann, "som_train.data", max_epochs, epochs_between_reports, desired_error);
/*fann_save(ann, "som_train.net"); */
fann_destroy(ann);
printf("\n");
return 0;
}
int main()
{
const float desired_error = (const float) 0.01;
const unsigned int max_epochs = 50000;
const unsigned int epochs_between_reports = 10;
unsigned int layers []= {6, 200, 200, 4};
struct fann * ann = fann_create_standard_array(4, layers);
fann_set_activation_function_layer(ann, FANN_SIGMOID_SYMMETRIC, 1);
fann_set_activation_function_layer(ann, FANN_SIGMOID_SYMMETRIC, 2);
fann_set_activation_function_layer(ann, FANN_SIGMOID_SYMMETRIC, 3);
fann_train_on_file(ann, "../training_file", max_epochs,
epochs_between_reports, desired_error);
fann_save(ann, "wii.net");
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;
}
void Classifier::trainFromFile(const std::string &filename) {
fann_train_on_file(neuralNetwork, filename.c_str(), maxEpochs,
printEpochs, reqError);
}
int main()
{
const unsigned int max_epochs = 1000;
const unsigned int epochs_between_reports = 10;
const unsigned int num_input = 48*48;
const unsigned int num_output = 30;
const unsigned int num_layers = 2;
const unsigned int num_neurons_hidden = 25;
const float desired_error = (const float) 0.0000;
fann_type *calc_out;
unsigned int i;
int incorrect,ret = 0;
int orig,pred; float max =0 ;
float learning_rate = 0.01;
struct fann *ann = fann_create_standard(num_layers, num_input, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_learning_rate(ann, learning_rate);
fann_train_on_file(ann, "facial-train.txt", max_epochs,
epochs_between_reports, desired_error);
fann_reset_MSE(ann);
struct fann_train_data *data = fann_read_train_from_file("facial-test.txt");
printf("Testing network..\n");
for(i = 0; i < fann_length_train_data(data); i++) {
calc_out = fann_test(ann, data->input[i], data->output[i] );
printf ("%i ", i );
max = calc_out[0];
int maxo = data->output[i][0];
for (int n=0; n<30; n++) {
printf (" %.2f/%.2f(%.2f) ",calc_out[n]*(2*96), data->output[i][n]*(2*96), data->output[i][n]*(2*96) - calc_out[n]*(2*96) );
}
printf ("\n");
}
printf("Mean Square Error: %f\n", fann_get_MSE(ann));
//printf ("Incorrect %i\n", incorrect);
fann_save(ann, "facial.net");
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
