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;
}
bool ViFann::setActivation(const Activation &activation, const int &layer)
{
if(mNetwork == NULL) return false;
fann_activationfunc_enum function;
if(activation == Sigmoid) function = FANN_SIGMOID_SYMMETRIC;
else if(activation == Elliot) function = FANN_ELLIOT_SYMMETRIC;
else if(activation == Gaussian) function = FANN_GAUSSIAN_SYMMETRIC;
else if(activation == Linear) function = FANN_LINEAR_PIECE_SYMMETRIC;
else if(activation == Cosine) function = FANN_COS_SYMMETRIC;
else if(activation == Sine) function = FANN_SIN_SYMMETRIC;
else return false;
fann_set_activation_function_layer(mNetwork, function, layer);
return true;
}
int ftest_data(void)
{
// sar_start_epoch=0;
// printf("\r\n\r\n--------------------------------------------------------------------------------");
double val_2[10];
fann_type *calc_out2;
unsigned calc2;
int curi=0;
unsigned fails=0,success=0;
double perc=0;
double minv=9,maxv=-1;
int i;
int minat=0,maxat=0;
test_mse=fann_test_data(ann,test_data);
for (curi=0;curi<fann_length_train_data(train_data);curi++)
{
calc2=curi;//rand()%(fann_length_train_data(train_data)-1);
//printf("\r\ntesting %u %u ",calc1,calc2);
//fann_scale_input(ann, test_data->input[calc1]);
//fann_scale_input(ann, train_data->input[calc2]);
// fann_scale_output(ann, test_data->input[calc1]);
//fann_scale_input(ann, train_data->input[calc2]);
calc_out2 = fann_run(ann, train_data->input[calc2]);
// fann_descale_output(ann,calc_out2);
memcpy(&val_2, calc_out2, sizeof(double)*3);
minv=9;
maxv=-1;
for (i=0;i<train_data->num_output;i++)
{
if ((double)calc_out2[i]<minv)
{
minv=val_2[i];
minat=i;
}
if ((double)calc_out2[i]>maxv)
{
maxv=val_2[i];
maxat=i;
}
}
int ok=0;
ok=0;
for (i=0;i<train_data->num_output;i++)
if (train_data->output[calc2][i]==1&&maxat==i)
ok=1;
if (ok)success++;
else
fails++;
}
train_perc=((double)success/(double)fann_length_train_data(train_data))*100.0f;
/* printf(" fails %5u success %5u (%5.2f%%) ",
fails,success,train_perc
); */
fails=0;
success=0;
unsigned failed_classes[10];
for (curi=0;curi<test_data->num_output;curi++)
failed_classes[curi]=0;
int nfunc=0;
double train_thr_mse=0;
nfunc=fann_get_activation_function(ann, 3, 0);
int stpns;
stpns=fann_get_activation_steepness(ann,1,0);
// printf("\r\n%f",diff_mse*0.1f);
//fann_set_activation_steepness_layer(ann, 0.3f, 1);
//fann_set_activation_function_layer(ann,FANN_THRESHOLD_SYMMETRIC,3);
for (curi=0;curi<fann_length_train_data(test_data);curi++)
{
calc2=curi;//rand()%(fann_length_train_data(train_data)-1);
//printf("\r\ntesting %u %u ",calc1,calc2);
//fann_scale_input(ann, test_data->input[calc1]);
//fann_scale_input(ann, train_data->input[calc2]);
// fann_scale_output(ann, test_data->input[calc1]);
//fann_scale_input(ann, train_data->input[calc2]);
calc_out2 = fann_run(ann, test_data->input[calc2]);
// fann_descale_output(ann,calc_out2);
memcpy(&val_2, calc_out2, sizeof(double)*3);
minv=9;
maxv=-1;
for (i=0;i<test_data->num_output;i++)
{
if (val_2[i]<minv)
{
minv=val_2[i];
minat=i;
}
if (val_2[i]>maxv)
{
maxv=val_2[i];
maxat=i;
}
}
int ok=0;
ok=0;
for (i=0;i<test_data->num_output;i++)
{
if (test_data->output[calc2][i]==1&&maxat==i)
ok=1;
else if (test_data->output[calc2][i]==1&&maxat!=i)
failed_classes[i]++;
}
if (ok)success++;
else
fails++;
}
test_perc=((double)success/(double)fann_length_train_data(test_data))*100.0f;
/* printf(" fails %5u success %5u (%5.2f%%) [fails: ",
fails,success,test_perc
);
for (curi=0;curi<test_data->num_output;curi++)
printf("%4u ",failed_classes[curi]);
printf("] "); */
// fann_set_activation_function_hidden ( ann, rand()*0.81);
// printf("\r\n rpropfact dec/inc r %.5f %.5f lr %.5f mom %.5f",fann_get_rprop_decrease_factor(ann),fann_get_rprop_increase_factor(ann), fann_get_learning_rate ( ann),
// fann_get_learning_momentum(ann));
// rebuild_functions();
fann_set_activation_function_layer(ann,nfunc,3);
fann_set_activation_steepness_layer(ann,stpns, 1);
}
