void GazeTracker::trainNN() {
std::cout << "Getting data" << std::endl;
struct fann_train_data *data = fann_create_train_from_callback(_inputCount, _nnEyeWidth * _nnEyeHeight, 2, getTrainingData);
//fann_save_train(data, "data.txt");
std::cout << "Getting left data" << std::endl;
struct fann_train_data *dataLeft = fann_create_train_from_callback(_inputCount, _nnEyeWidth * _nnEyeHeight, 2, getTrainingDataLeft);
//fann_save_train(dataLeft, "dataLeft.txt");
fann_set_training_algorithm(_ANN, FANN_TRAIN_RPROP);
fann_set_learning_rate(_ANN, 0.75);
fann_set_training_algorithm(_ANNLeft, FANN_TRAIN_RPROP);
fann_set_learning_rate(_ANNLeft, 0.75);
std::cout << "Training" << std::endl;
fann_train_on_data(_ANN, data, 200, 20, 0.01);
std::cout << "Training left" << std::endl;
fann_train_on_data(_ANNLeft, dataLeft, 200, 20, 0.01);
double mse = fann_get_MSE(_ANN);
double mseLeft = fann_get_MSE(_ANNLeft);
std::cout << "MSE: " << mse << ", MSE left: " << mseLeft << std::endl;
}
void GazeTracker::trainNN()
{
cout << "Getting data" << endl;
struct fann_train_data* data = fann_create_train_from_callback(input_count, nn_eyewidth * nn_eyeheight, 2, getTrainingData);
//fann_save_train(data, "data.txt");
cout << "Getting left data" << endl;
struct fann_train_data* data_left = fann_create_train_from_callback(input_count, nn_eyewidth * nn_eyeheight, 2, getTrainingData_left);
//fann_save_train(data_left, "data_left.txt");
fann_set_training_algorithm(ANN, FANN_TRAIN_RPROP);
fann_set_learning_rate(ANN, 0.75);
fann_set_training_algorithm(ANN_left, FANN_TRAIN_RPROP);
fann_set_learning_rate(ANN_left, 0.75);
cout << "Training" << endl;
fann_train_on_data(ANN, data, 200, 20, 0.01);
cout << "Training left" << endl;
fann_train_on_data(ANN_left, data_left, 200, 20, 0.01);
double mse = fann_get_MSE(ANN);
double mse_left = fann_get_MSE(ANN_left);
cout << "MSE: " << mse << ", MSE left: " << mse_left << endl;
}
void ViFann::setTraining(const Algorithm &algorithm)
{
mAlgorithm = algorithm;
if(mAlgorithm == Incremental) fann_set_training_algorithm(mNetwork, FANN_TRAIN_INCREMENTAL);
else if(mAlgorithm == Batch) fann_set_training_algorithm(mNetwork, FANN_TRAIN_BATCH);
else if(mAlgorithm == RProp) fann_set_training_algorithm(mNetwork, FANN_TRAIN_RPROP);
else if(mAlgorithm == QuickProp) fann_set_training_algorithm(mNetwork, FANN_TRAIN_QUICKPROP);
}
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;
}
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(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 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);
}
/***
* @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_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;
}
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;
}
static struct sml_ann_bridge *
_sml_ann_bridge_new(struct fann *ann, bool trained)
{
struct sml_ann_bridge *iann = calloc(1, sizeof(struct sml_ann_bridge));
if (!iann) {
sml_critical("Could not create an struct sml_ann_bridge");
return NULL;
}
sol_vector_init(&iann->confidence_intervals, sizeof(Confidence_Interval));
iann->ann = ann;
iann->trained = trained;
iann->last_train_error = NAN;
if (iann->trained)
fann_set_training_algorithm(iann->ann, FANN_TRAIN_INCREMENTAL);
return iann;
}
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);
}
/*! ann:set_training_algorithm(function)
*# Sets the training function for the neural network.\n
*# Valid algorithms are {{fann.FANN_TRAIN_INCREMENTAL}},
*# {{fann.FANN_TRAIN_BATCH}}, {{fann.FANN_TRAIN_RPROP}} or
*# {{fann.FANN_TRAIN_QUICKPROP}}
*x ann:set_training_algorithm(fann.FANN_TRAIN_QUICKPROP)
*-
*/
static int ann_set_training_algorithm(lua_State *L)
{
struct fann **ann;
int alg;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
alg = luaL_checkinteger(L, 2);
if(alg < FANN_TRAIN_INCREMENTAL || alg > FANN_TRAIN_QUICKPROP)
luaL_error(L, "%d is not a valid algorithm", alg);
#ifdef FANN_VERBOSE
printf("Setting training algorithm to %d\n", alg);
#endif
fann_set_training_algorithm(*ann, alg);
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()
{
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 96;
const float desired_error = (const float) 0.001;
struct fann *ann;
struct fann_train_data *train_data, *test_data;
float momentum;
train_data = fann_read_train_from_file("../benchmarks/datasets/robot.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/robot.test");
for ( momentum = 0.0; momentum < 0.7; momentum += 0.1 )
{
printf("============= momentum = %f =============\n", momentum);
ann = fann_create_standard(num_layers,
train_data->num_input, num_neurons_hidden, train_data->num_output);
fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
fann_set_learning_momentum(ann, momentum);
fann_train_on_data(ann, train_data, 2000, 500, desired_error);
printf("MSE error on train data: %f\n", fann_test_data(ann, train_data));
printf("MSE error on test data : %f\n", fann_test_data(ann, test_data));
fann_destroy(ann);
}
fann_destroy_train(train_data);
fann_destroy_train(test_data);
return 0;
}
int main() {
printf("Reading XML.. .. ..\n");
ezxml_t f1 = ezxml_parse_file("test.xml"), classification, temp, algo, temp2;
classification = ezxml_child(f1, "classification");
temp = ezxml_child(classification, "algorithm");
algo = ezxml_child(temp, "MultiLayerPerceptron");
const unsigned int num_input = atoi(ezxml_child(classification, "input")->txt);
const unsigned int num_output = atoi(ezxml_child(classification, "output")->txt);
const unsigned int num_layers = atoi(ezxml_child(classification, "numberOfLayers")->txt);
const unsigned int num_neurons_hidden = atoi(ezxml_child(algo, "hiddenNeurons")->txt);
const float desired_error = (const float) (atof(ezxml_child(algo, "desiredError")->txt));
const unsigned int max_epochs = atoi(ezxml_child(algo, "maxEpochs")->txt);
const unsigned int epochs_between_reports = atoi(ezxml_child(algo, "epochsBetweenReports")->txt);
fann_type *calc_out;
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(ezxml_child(classification, "datafile")->txt);
fann_set_activation_steepness_hidden(ann, atoi(ezxml_child(algo, "hiddenActivationSteepness")->txt));
fann_set_activation_steepness_output(ann, atoi(ezxml_child(algo, "outputActivationSteepness")->txt));
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
temp2 = ezxml_child(algo, "trainStopFuction");
const char *stopFunc = temp2->txt;
if(stopFunc == "FANN_STOPFUNC_BIT"){
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
} else {
fann_set_train_stop_function(ann, FANN_STOPFUNC_MSE);
}
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("Test Results (%f,%f,%f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], data->input[i][2], calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Saving network.\n");
fann_save(ann, "xor_float.net");
decimal_point = fann_save_to_fixed(ann, "xor_fixed.net");
fann_save_train_to_fixed(data, "xor_fixed.data", decimal_point);
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
ezxml_free(f1);
return 0;
}
/*
arguments (all required):
- data filename
- topology, as number of neurons per layer separated by dashes
- epochs (integer)
- learning rate (0.0-1.0 float)
- output filename
*/
int main(int argc, char **argv)
{
// Argument 1: data filename.
const char *datafn = argv[1];
// Argument 2: topology.
unsigned int layer_sizes[MAX_LAYERS];
unsigned int num_layers = 0;
char *token = strtok(argv[2], "-");
while (token != NULL) {
layer_sizes[num_layers] = atoi(token);
++num_layers;
token = strtok(NULL, "-");
}
// Argument 3: epoch count.
unsigned int max_epochs = atoi(argv[3]);
// Argument 4: learning rate.
float learning_rate = atof(argv[4]);
// Argument 5: output filename.
const char *outfn = argv[5];
struct fann *ann;
ann = fann_create_standard_array(num_layers, layer_sizes);
// Misc parameters.
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_activation_steepness_hidden(ann, 0.5);
fann_set_activation_steepness_output(ann, 0.5);
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);
struct fann_train_data *data;
data = fann_read_train_from_file(datafn);
fann_init_weights(ann, data);
fann_set_learning_rate(ann, learning_rate);
fann_train_on_data(
ann,
data,
max_epochs,
10, // epochs between reports
DESIRED_ERROR
);
printf("Testing network. %f\n", fann_test_data(ann, data));
fann_type *calc_out;
for(unsigned int i = 0; i < fann_length_train_data(data); ++i)
{
calc_out = fann_run(ann, data->input[i]);
}
printf("RMSE = %f\n", sqrt(fann_get_MSE(ann)));
fann_save(ann, outfn);
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
void Trainer::set_training_algorithm(const TrainingAlgorithm& algorithm) {
std::cerr << "Set training algo to " << algorithm.name() << std::endl;
fann_set_training_algorithm(ann_, algorithm.train_enum());
}
struct fann * setup_net(struct fann_train_data * data)
{
struct fann *ann;
#if MIMO_FANN
#if OPTIMIZE == 0
ann = fann_create_standard( 3, data->num_input, H_DIM, data->num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
#endif
#if OPTIMIZE == 1
unsigned int i, j;
struct fann_descr *descr=(struct fann_descr*) calloc(1, sizeof(struct fann_descr));
fann_setup_descr(descr, 2, data->num_input);
i=0;
fann_setup_layer_descr(
&(descr->layers_descr[i]),
"connected_any_any",
1,
NULL
);
for (j=0; j< descr->layers_descr[i].num_neurons; j++)
{
fann_setup_neuron_descr(
descr->layers_descr[i].neurons_descr+j,
H_DIM,
"scalar_rprop_sigmoid_symmetric",
NULL
);
}
i=1;
fann_setup_layer_descr(
&(descr->layers_descr[i]),
"connected_any_any",
1,
NULL
);
for (j=0; j< descr->layers_descr[i].num_neurons; j++)
{
fann_setup_neuron_descr(
descr->layers_descr[i].neurons_descr+j,
data->num_output,
"scalar_rprop_sigmoid_symmetric",
NULL
);
}
ann = fann_create_from_descr( descr );
#endif
#if OPTIMIZE >= 2
{
unsigned int layers[] = { data->num_input, H_DIM, data->num_output };
/*char *type;
asprintf(&type, "%s_%s_%s", vals(implementation), vals(algorithm), vals(activation));*/
ann = fann_create_standard_array_typed(layer_type, neuron_type, 3, layers);
}
#endif
#else /*MIMO_FANN*/
#ifdef SPARSE
ann = fann_create_sparse( SPARSE, 3, data->num_input, H_DIM, data->num_output);
#else
ann = fann_create_standard( 3, data->num_input, H_DIM, data->num_output);
#endif
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
#endif /*MIMO_FANN*/
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
fann_set_bit_fail_limit(ann, 0.01f);
fann_set_activation_steepness_hidden(ann, 1);
fann_set_activation_steepness_output(ann, 1);
#if INIT_WEIGHTS == 1
fann_randomize_weights(ann,0,1);
#endif
#if INIT_WEIGHTS == 2
fann_init_weights(ann, data);
#endif
#ifdef USE_RPROP
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
#else
fann_set_training_algorithm(ann, FANN_TRAIN_BATCH);
#endif
return ann;
}
int main(int argc, const char* argv[])
{
if (argc < 2) {
printf("Usage: ./dinneuro filename\n");
return -1;
}
//подготавливаем выборки
if (csv2fann2(argv[1], 59, 50, 100, true)) { printf("Converted\n"); }
//получим данные о количестве входных и выходных параметров
int *params;
const char * filename;
const char * normfilename;
filename = "data.data";
//filename = "scaling.data";
normfilename = "normalized.train";
params = getparams(filename);
unsigned int num_threads = omp_get_thread_num();
float error;
const unsigned int num_input = params[1];
const unsigned int num_output = params[2];
//printf("num_input=%d num_output=%d\n", num_input, num_output);
const unsigned int num_layers = 4;
//const unsigned int num_neurons_hidden = num_output;
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_LINEAR);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
//printf("test\n");
data = fann_read_train_from_file(filename);
printf("Readed train from %s\n", filename);
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 );
printf("Scaled\n");
//сохраним нормализованную обучающу выборку в файл
fann_save_train(data, normfilename);
printf("Saved scaled file %s\n", normfilename);
unsigned int i;
printf("Start learning...\n");
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);
//если ошибка обучения меньше или равно заданной - выходим из цикла обучения
//if (error <= desired_error) { printf ("Desired error detected. Finishing teaching.\n"); break; }
//если текущий счетчик делится без остатка на epochs_between_reports - пишем лог
//if (i % epochs_between_reports == 0) { printf("Epochs %8d. Current error: %.10f\n", i, error); }
}
printf("End learning.\n");
printf("MSE = %f\n", fann_get_MSE(ann));
//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);
//проверка
printf("Testing...\n");
fann_type *calc_out;
//printf("fann_length_train_data=%d\n",fann_length_train_data(data));
printf("Creating network.\n");
ann = fann_create_from_file("scaling.net");
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return 0;
}
//печатаем параметры сети
//fann_print_connections(ann);
//fann_print_parameters(ann);
printf("Testing network.\n");
data = fann_read_train_from_file(filename);
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
fann_scale_input( ann, data->input[i] );
calc_out = fann_run( ann, data->input[i] );
fann_descale_output( ann, calc_out );
printf("Result %f original %f error %f or %.2f%%\n",
calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]), (100*(float) fann_abs(calc_out[0] - data->output[i][0]))/(float)calc_out[0]);
}
fann_destroy_train( data );
fann_destroy(ann);
return 0;
}
int main(int argc, char **argv)
{
if(argc < 3)
{
printf("Usage: train_net <input.train> <output.net>\n");
exit(-1);
}
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 8;
const float desired_error = (const float) 0.000042;
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_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_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_train_on_file(ann, argv[1], max_epochs, epochs_between_reports, desired_error);
struct fann_train_data *data;
data = fann_read_train_from_file(argv[1]);
fann_init_weights(ann, data);
printf("Training network on data from %s.\n", argv[1]);
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network. %f\n", fann_test_data(ann, data));
double error, errorSum = 0;
unsigned int i = 0, size = fann_length_train_data(data);
fann_type *calc_out;
for(i = 0; i < size; i++)
{
calc_out = fann_run(ann, data->input[i]);
error = fann_abs(calc_out[0] - data->output[i][0]) * 1000;
printf("Distance test (%d dBm,%f%%) -> %f meters, should be %f meters, difference=%f meters\n",
(int)(data->input[i][0] * 150 - 150), data->input[i][1], calc_out[0] * 1000, data->output[i][0] * 1000,
error);
errorSum += error;
}
printf("Average Error: %f\n", errorSum / size);
fann_save(ann, argv[2]);
fann_destroy(ann);
return 0;
}
/***
* @function rspamd_fann.create_full(params)
* Creates new neural network with parameters:
* - `layers` {table/numbers}: table of layers in form: {N1, N2, N3 ... Nn} where N is number of neurons in a layer
* - `activation_hidden` {string}: activation function type for hidden layers (`tanh` by default)
* - `activation_output` {string}: activation function type for output layer (`tanh` by default)
* - `sparsed` {float}: create sparsed ANN, where number is a coefficient for sparsing
* - `learn` {string}: learning algorithm (quickprop, rprop or incremental)
* - `randomize` {boolean}: randomize weights (true by default)
* @return {fann} fann object
*/
static gint
lua_fann_create_full (lua_State *L)
{
#ifndef WITH_FANN
return 0;
#else
struct fann *f, **pfann;
guint nlayers, *layers, i;
const gchar *activation_hidden = NULL, *activation_output, *learn_alg = NULL;
gdouble sparsed = 0.0;
gboolean randomize_ann = TRUE;
GError *err = NULL;
if (lua_type (L, 1) == LUA_TTABLE) {
lua_pushstring (L, "layers");
lua_gettable (L, 1);
if (lua_type (L, -1) != LUA_TTABLE) {
return luaL_error (L, "bad layers attribute");
}
nlayers = rspamd_lua_table_size (L, -1);
if (nlayers < 2) {
return luaL_error (L, "bad layers attribute");
}
layers = g_new0 (guint, nlayers);
for (i = 0; i < nlayers; i ++) {
lua_rawgeti (L, -1, i + 1);
layers[i] = luaL_checknumber (L, -1);
lua_pop (L, 1);
}
lua_pop (L, 1); /* Table */
if (!rspamd_lua_parse_table_arguments (L, 1, &err,
"sparsed=N;randomize=B;learn=S;activation_hidden=S;activation_output=S",
&sparsed, &randomize_ann, &learn_alg, &activation_hidden, &activation_output)) {
g_free (layers);
if (err) {
gint r;
r = luaL_error (L, "invalid arguments: %s", err->message);
g_error_free (err);
return r;
}
else {
return luaL_error (L, "invalid arguments");
}
}
if (sparsed != 0.0) {
f = fann_create_standard_array (nlayers, layers);
}
else {
f = fann_create_sparse_array (sparsed, nlayers, layers);
}
if (f != NULL) {
pfann = lua_newuserdata (L, sizeof (gpointer));
*pfann = f;
rspamd_lua_setclass (L, "rspamd{fann}", -1);
}
else {
g_free (layers);
return luaL_error (L, "cannot create fann");
}
fann_set_activation_function_hidden (f,
string_to_activation_func (activation_hidden));
fann_set_activation_function_output (f,
string_to_activation_func (activation_output));
fann_set_training_algorithm (f, string_to_learn_alg (learn_alg));
if (randomize_ann) {
fann_randomize_weights (f, 0, 1);
}
g_free (layers);
}
else {
return luaL_error (L, "bad arguments");
}
return 1;
#endif
}
int main(int argc,char **argv)
{
unlink(histfile);
srand ( time ( NULL ) );
// printf ( "Reading data.\n" );
train_data = fann_read_train_from_file ( "train.dat" );
test_data = fann_read_train_from_file ( "test.dat" );
// signal ( 2, sig_term );
// fann_scale_train_data ( train_data, 0, 1.54 );
// fann_scale_train_data ( test_data, 0, 1.54 );
//cln_test_data=fann_duplicate_train_data(test_data);
cln_train_data=fann_duplicate_train_data(train_data);
printf ( "Creating cascaded 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 );
fann_set_activation_function_output ( ann, FANN_SIGMOID);
fann_set_train_error_function ( ann, FANN_ERRORFUNC_LINEAR );
// if (fann_set_scaling_params(ann, train_data,-1.0f,1.0f,0.0f, 1.0f)==-1)
// printf("set scaling error: %s\n",fann_get_errno((struct fann_error*)ann));
// fann_scale_train_input(ann,train_data);
// fann_scale_output_train_data(train_data,0.0f,1.0f);
// fann_scale_input_train_data(train_data, -1.0,1.0f);
// fann_scale_output_train_data(test_data,-1.0f,1.0f);
// fann_scale_input_train_data(test_data, -1.0,1.0f);
//fann_scale_train(ann,train_data);
// fann_scale_train(ann,weight_data);
// fann_scale_train(ann,test_data);
/*
* fann_set_cascade_output_change_fraction(ann, 0.1f);
* ;
* fann_set_cascade_candidate_change_fraction(ann, 0.1f);
*
*/
// fann_set_cascade_output_stagnation_epochs ( ann, 180 );
//fann_set_cascade_weight_multiplier ( ann, ( fann_type ) 0.1f );
fann_set_callback ( ann, cascade_callback );
if ( !multi )
{
/* */
// steepness[0] = 0.22;
steepness[0] = 0.9;
steepness[1] = 1.0;
/*
* steepness[1] = 0.55;
* ;
* steepness[1] = 0.33;
* ;
* steepness[3] = 0.11;
* ;
* steepness[1] = 0.01;
*
*/
/*
* steepness = 0.5;
*
*/
// fann_set_cascade_activation_steepnesses ( ann, steepness, 2);
/*
* activation = FANN_SIN_SYMMETRIC;
*/
/*
* activation[0] = FANN_SIGMOID;
*
*/
activation[0] = FANN_SIGMOID;
/*
* activation[2] = FANN_ELLIOT_SYMMETRIC;
*
*/
activation[1] = FANN_LINEAR_PIECE;
/*
* activation[4] = FANN_GAUSSIAN_SYMMETRIC;
* ;
* activation[5] = FANN_SIGMOID;
*
*/
activation[2] = FANN_ELLIOT;
activation[3] = FANN_COS;
/*
*
*
*/
activation[4] = FANN_SIN;
fann_set_cascade_activation_functions ( ann, activation, 5);
/* fann_set_cascade_num_candidate_groups ( ann,
fann_num_input_train_data
( train_data ) ); */
}
else
{
/*
* fann_set_cascade_activation_steepnesses(ann, &steepness, 0.75);
*
*/
// fann_set_cascade_num_candidate_groups ( ann, 1 );
}
/* TODO: weight mult > 0.01 */
/* if ( training_algorithm == FANN_TRAIN_QUICKPROP )
{
fann_set_learning_rate ( ann, 0.35f );
}
else
{
fann_set_learning_rate ( ann, 0.7f );
}
fann_set_bit_fail_limit ( ann, ( fann_type ) 0.9f );*/
/*
* fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
*
*/
//fann_scale_output_train_data(train_data,0.0f,1.0f);
//fann_scale_input_train_data(train_data, -1.0f,1.0f);
// fann_scale_output_train_data(test_data, 0.0f,1.0f);
//fann_scale_input_train_data(test_data, -1.0f,1.0f);
// fann_randomize_weights ( ann, -0.2f, 0.2f );
fann_init_weights ( ann, train_data );
printf ( "Training network.\n" );
fann_cascadetrain_on_data ( ann, train_data, max_neurons,
1, 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: %.08f, Train bit-fail: %d, Test error: %.08f, Test bit-fail: %d\n\n",
mse_train, bit_fail_train, mse_test, bit_fail_test );
printf ( "Saving cascaded network.\n" );
fann_save ( ann, "cascaded.net" );
// printf ( "Cleaning up.\n" );
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 ( 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(int argc, char *argv[])
{
struct fann_train_data *dadosTreino, *dadosTeste;
struct fann *ANN;
fann_type *ANN_Answers;
int *layers, i, j, aux;
chromosome chromo;
float erro = 0.0;
checkArgs(argc, argv);
buildChromosome(argv, &chromo);
checkDatasetFiles();
dadosTreino = fann_read_train_from_file(nomeArqTreino);
layers = (int *) calloc(2+chromo.qntCamadasOcultas, sizeof(int));
layers[0] = qntNeuroniosEntrada;
layers[2+chromo.qntCamadasOcultas-1] = qntNeuroniosSaida;
aux = chromo.neurOcultos;
for (i=1; i < 2+chromo.qntCamadasOcultas-1 ; i++)
{
layers[i] = aux;
aux = aux/2;
}
// CRIANDO A RNA:
ANN = fann_create_standard_array(2+chromo.qntCamadasOcultas, layers);
// TREINO
fann_set_learning_rate(ANN, chromo.learnRate);
fann_set_learning_momentum(ANN, chromo.moment);
fann_set_activation_function_hidden( ANN, chromo.fcOculta );
fann_set_activation_function_output( ANN, chromo.fcSaida );
fann_set_training_algorithm(ANN, chromo.algAprend );
if (fann_get_training_algorithm(ANN) == FANN_TRAIN_QUICKPROP)
fann_set_quickprop_decay(ANN, chromo.decay);
// Em python, o treino ficava entre um try.
// Se desse erro, escrevia "Resultado: 999.0" e exit
fann_train_on_data(ANN, dadosTreino, chromo.epocasTreino, 50, desiredError);
fann_destroy_train(dadosTreino);
// TESTES:
dadosTeste = fann_read_train_from_file( nomeArqValidacao);
// Em python, o teste também ficava entre um try.
// Se desse erro, escrevia "Resultado: 999.0" e exit
for(i = 0; i < fann_length_train_data(dadosTeste); i++)
{
ANN_Answers = fann_run(ANN, dadosTeste->input[i]);
if (ANN_Answers == NULL)
{
printf("Resultado: 999.0\n");
exit(2);
}
for (j=0; j < qntNeuroniosSaida; j++)
erro += (float) powf(fann_abs(ANN_Answers[j] - dadosTeste->output[i][j]), 2);
}
printf("Resultado: %f\n", erro/(fann_length_train_data(dadosTeste)-1));
fann_destroy_train(dadosTeste);
saveANN(argc, argv, ANN);
fann_destroy(ANN);
}