// fann_print_parameters Display the ANN parameters
int sci_fann_print_parameters(char * fname)
{
int res;
struct fann * result_ann = NULL;
if (Rhs!=1)
{
Scierror(999,"%s usage: %s(ann_in)", fname, fname);
return 0;
}
// Get the ann
res = detect_fannlist(1);
if (res==-1) return 0;
result_ann = createCFannStructFromScilabFannStruct(1,&res);
if (result_ann==NULL)
{
Scierror(999,"%s: Problem while creating the fann scilab structure\n",fname);
return 0;
}
fann_print_parameters(result_ann);
LhsVar(1) = 0;
return 0;
}
int main( int argc, char** argv )
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
ann = fann_create_from_file("xor_float.net");
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return 0;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
data = fann_read_train_from_file("5K.txt");
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\n",
calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return ret;
}
/*! ann:print_parameters()
*# Prints the neural network's parameters
*x ann:print_parameters()
*-
*/
static int ann_print_parameters(lua_State *L)
{
struct fann **ann;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
fann_print_parameters(*ann);
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;
}
int main(void)
{
struct fann *ann = NULL;
unsigned int i = 0;
unsigned int num_neurons = 0;
unsigned int num_inputs = 0;
unsigned int num_outputs = 0;
fann_seed_rand();
/* GENERATE RANDOM RECURRENT NETWORKS */
for (i=0; i<NUM_TESTS; i++)
{
num_inputs = rand()%5;
num_neurons = rand()%10 + 1;
// Number of outputs must be <= number of neurons
do { num_outputs = rand()%10; } while (num_outputs > num_neurons);
ann = fann_create_fully_recurrent(num_neurons, num_inputs, num_outputs);
printf("\n\nCreated recurrent network (neurons=%d, inputs=%d, outputs=%d)\n",
num_neurons, num_inputs, num_outputs);
printf("\n-------------------------------------------\n");
printf("NET %d - CONNECTION MATRIX:\n", i);
printf(" (I=input, b=bias, O=output, #=internal)\n");
printf("-------------------------------------------\n");
fann_print_connections(ann);
printf("\n\n-------------------------------------------\n");
printf("NET %d - ANN PARAMETERS:\n", i);
printf("-------------------------------------------\n");
fann_print_parameters(ann);
/* CLEAN UP */
fann_destroy(ann);
}
return 0;
}
void ann_list(ann_t *net)
{
fann_print_parameters((struct fann *) net);
}
int main()
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
#ifdef FIXEDFANN
ann = fann_create_from_file("digitde_validation_fixed.net");
#else
ann = fann_create_from_file("digitde_validation_float.net");
#endif
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return -1;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
#ifdef FIXEDFANN
data = fann_read_train_from_file("digitde_validation_fixed.data");
#else
data = fann_read_train_from_file("digitde_validation.data");
#endif
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_test(ann, data->input[i], data->output[i]);
#ifdef FIXEDFANN
printf("GG test (%d, %d) -> %d, should be %d, 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_get_multiplier(ann));
if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2)
{
printf("Test failed\n");
ret = -1;
}
#else
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],
(float) fann_abs(calc_out[0] - data->output[i][0]));
#endif
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return ret;
}
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()
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
#ifdef FIXEDFANN
ann = fann_create_from_file("./lib/fann/wc2fann/web_comp_fixed.net");
#else
ann = fann_create_from_file("./lib/fann/wc2fann/web_comp_config.net");
#endif
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return -1;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
#ifdef FIXEDFANN
data = fann_read_train_from_file("./lib/fann/wc2fann/web_comp_fixed.data");
#else
data = fann_read_train_from_file("./lib/fann/wc2fann/data/selection.test");
#endif
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_test(ann, data->input[i], data->output[i]);
#ifdef FIXEDFANN
printf("Web Comp test (%d, %d) -> %d, should be %d, 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_get_multiplier(ann));
if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2)
{
printf("Test failed\n");
ret = -1;
}
#else
printf("Web Comp 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]));
//Web_Comp
double answer = fann_abs(calc_out[0] - data->output[0][0]);
FILE *output;
output = fopen("./lib/fann/wc2fann/data/Web_Comp_Answer.txt","w");
fprintf(output, "%f", answer);
fclose(output);
#endif
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return ret;
}
int main()
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
int max_expected_idx=0,max_predicted_idx=0,count=0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
#ifdef FIXEDFANN
ann = fann_create_from_file("mnist_fixed1.net");
#else
ann = fann_create_from_file("mnist_float.net");
#endif
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return -1;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
#ifdef FIXEDFANN
data = fann_read_train_from_file("mnist.data");
#else
data = fann_read_train_from_file("mnist.data");
#endif
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_test(ann, data->input[i], data->output[i]);
#ifdef FIXEDFANN
printf("XOR test (%d, %d) -> %d, should be %d, 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_get_multiplier(ann));
if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2)
{
printf("Test failed\n");
ret = -1;
}
#else
max_expected_idx = 0;
max_predicted_idx = 0;
for(int k=1;k<10;k++)
{
if(data->output[i][max_expected_idx] < data->output[i][k])
{
max_expected_idx = k;
}
if(calc_out[max_predicted_idx] < calc_out[k])
{
max_predicted_idx = k;
}
}
printf("MNIST test %d Expected %d , returned=%d\n",
i,max_expected_idx, max_predicted_idx);
if(max_expected_idx == max_predicted_idx)
count++;
#endif
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
printf("Number correct=%d\n",count);
return ret;
}
