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;
}
/*! train:scale(min, max)
*# Scales the inputs and outputs of training data to the new range [{{min}}-{{max}}]
*x
*-
*/
static int ann_train_scale(lua_State *L)
{
struct fann_train_data **train;
fann_type min, max;
train = luaL_checkudata(L, 1, FANN_TRAIN_METATABLE);
luaL_argcheck(L, train != NULL, 1, "'training data' expected");
min = luaL_checknumber(L, 2);
max = luaL_checknumber(L, 3);
#ifdef FANN_VERBOSE
printf("Scaling training data to %g - %g\n", (double)min, (double)max);
#endif
fann_scale_train_data(*train, min, max);
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[])
{
struct fann_train_data *train, *test, *all;
unsigned int i, j;
unsigned int length, half_length;
if(argc == 2)
{
train = fann_read_train_from_file(argv[1]);
fann_shuffle_train_data(train);
fann_scale_train_data(train, 0, 1);
fann_save_train(train, argv[1]);
return 0;
}
else if(argc == 3)
{
train = fann_read_train_from_file(argv[1]);
test = fann_read_train_from_file(argv[2]);
all = fann_merge_train_data(train, test);
fann_shuffle_train_data(all);
for(i = 0; i < train->num_data; i++)
{
for(j = 0; j < train->num_input; j++)
{
train->input[i][j] = all->input[i][j];
}
for(j = 0; j < train->num_output; j++)
{
train->output[i][j] = all->output[i][j];
}
}
for(i = 0; i < test->num_data; i++)
{
for(j = 0; j < test->num_input; j++)
{
test->input[i][j] = all->input[i + train->num_data][j];
}
for(j = 0; j < test->num_output; j++)
{
test->output[i][j] = all->output[i + train->num_data][j];
}
}
fann_save_train(train, argv[1]);
fann_save_train(test, argv[2]);
}
else if(argc == 4)
{
all = fann_read_train_from_file(argv[1]);
fann_shuffle_train_data(all);
fann_scale_train_data(all, 0, 1);
length = fann_length_train_data(all);
half_length = length/2;
train = fann_subset_train_data(all, 0, half_length);
test = fann_subset_train_data(all, half_length, length-half_length);
fann_save_train(train, argv[2]);
fann_save_train(test, argv[3]);
}
else
{
printf("usage: %s train_file\n", argv[0]);
printf("will shuffle the data in the file.\n");
printf("usage: %s train_file test_file\n", argv[0]);
printf("will shuffle the data in the two files and save the new data back to them.\n");
printf("usage: %s input_file train_file test_file\n", argv[0]);
printf("will shuffle the data in the input_file, and split it in two files and save the new data back to them.\n\n");
return -1;
}
return 0;
}
