int FANN_API cascade_callback
( struct fann *ann, struct fann_train_data *train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs )
{
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 );
if (mse_test<min_mse_test)
{
fann_save ( ann, "cascaded-test.net" );
min_mse_test=mse_test;
lowest_test_mse_epoch=epochs;
}
if (mse_train<min_mse_train)
{
fann_save ( ann, "cascaded.net" );
min_mse_train=mse_train;
}
plot((double)epochs,mse_train,mse_test,train_perc/100,test_perc/100);
// if ( prev_mse < mse_test && last_bads++>=3 )
// {
// do
// {
// func_num=func_num+rand() %6;
// activation[0] = ( enum fann_activationfunc_enum ) func_num;
// fann_set_cascade_activation_functions ( ann, activation, 1 );
// printf ( "\n Over-fitting. new func %s", FANN_ACTIVATIONFUNC_NAMES[func_num] );
// }
// while ( fann_get_errno ( ( struct fann_error* ) ann ) == 12 );
// last_bads=0;
// func_num=0;
// }
// else if ( last_bads>=1 && prev_mse > mse_test )
// last_bads--;
// prev_mse = mse_test;
ftest_data();
printf
( "\n %5d %4d %.08f %5.2f%% (%.08f) | %.08f %5.2f%% (%.08f e=%d) | %-4d %-4d %.2lf %s",
epochs, ann->total_neurons, mse_train,train_perc, min_mse_train, mse_test, test_perc,min_mse_test, lowest_test_mse_epoch, bit_fail_train,
bit_fail_test,
( ann->last_layer - 2 )->first_neuron->activation_steepness,
FANN_ACTIVATIONFUNC_NAMES[ ( ann->last_layer -
2 )->first_neuron->activation_function] );
// fann_save ( ann, "cascaded.net" );
jitter_train(train, cln_train_data);
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)
{
float mse=1000;
unsigned int num_train=R_NUM;
unsigned int num_test=T_NUM;
struct fann_train_data* data ;
unsigned int i;
const float desired_error = (const float) E_DES;
const unsigned int epochs_between_reports = N_EPR;
unsigned int bitf_limit=0;
unsigned int bitf=bitf_limit+1;
struct fann *ann;
#if MIMO_FANN
printf("MIMO fann\n");
#else
printf("Old fann\n");
#endif
#ifdef USE_XOR_DATA
if (argc<2)
{
printf("Error: please supply a data file\n");
return -1;
}
printf("Using %s\n", argv[1]);
data=fann_read_train_from_file(argv[1]);
#else
printf("Generating training data\n");
data = fann_create_train(S_DIM, I_DIM, O_DIM);
for ( i=0; i< S_DIM; i++)
{
f1(data, i);
}
#endif
ann=setup_net(data);
#if VERBOSE
fann_print_parameters(ann);
#endif
for (i=0; mse>desired_error && i!=num_train && bitf>bitf_limit; i++)
{
#if VERBOSE
mse=train_epoch_debug(ann, data, i);
#else
mse=fann_train_epoch(ann, data);
#endif
bitf=fann_get_bit_fail(ann);
if ( !((i) % epochs_between_reports))
printf("Epochs %8d. Current error: %.10f. Bit fail: %u\n", i+(!i), mse, bitf);
/*printf ("[ %7u ] MSE Error : %.10e ###################\n", i, mse);*/
}
printf("Epochs %8d. Current error: %.10f. Bit fail: %u\n", i+(!i), mse, bitf);
printf("Testing network. %f\n", fann_test_data(ann, data));
gettimeofday(&tv_start,NULL);
for (i=0; i!=num_test; i++)
fann_run_data(ann, data);
gettimeofday(&tv_now,NULL);
report("---",0);
#if 1
printf("Trying to save network\n");
#if MIMO_FANN
fann_save(ann, "saved_mimo.net");
fann_destroy(ann);
ann=fann_create_from_file("saved_mimo.net");
fann_save(ann, "saved_mimo2.net");
fann_destroy(ann);
#else
fann_save(ann, "saved_old.net");
#endif
#endif
return 0;
}
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;
}
