How to train gender model?

I am trying to train the gender model (apart from the pretrained model) myself. According to the network definitions on the gender example, I have prepared a training program like this which is a combination of the network definitions of gender network and imagenet training.

```
#include
#include
#include
#include
#include
#include
#include

using namespace std;
using namespace dlib;

template class BN, int stride, typename SUBNET>
using block = BN>>>>;

template using res_ = relu>;
template using ares_ = relu>;

template using alevel1 = avg_pool<2, 2, 2, 2, ares_<64, SUBNET>>;
template using alevel2 = avg_pool<2, 2, 2, 2, ares_<32, SUBNET>>;

using net_type = loss_multiclass_log>>>>>>>>;

#define PBSTR "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||"
#define PBWIDTH 40

rectangle make_random_cropping_rect_resnet(
const matrix& img,
dlib::rand& rnd
)
{
// figure out what rectangle we want to crop from the image
double mins = 0.466666666, maxs = 0.875;
auto scale = mins + rnd.get_random_double()*(maxs-mins);
auto size = scale*std::min(img.nr(), img.nc());
rectangle rect(size, size);
// randomly shift the box around
point offset(rnd.get_random_32bit_number()%(img.nc()-rect.width()),
rnd.get_random_32bit_number()%(img.nr()-rect.height()));
return move_rect(rect, offset);
}

// ----------------------------------------------------------------------------------------

void randomly_crop_image (
const matrix& img,
matrix& crop,
dlib::rand& rnd
)
{
auto rect = make_random_cropping_rect_resnet(img, rnd);

// now crop it out as a 227x227 image.
extract_image_chip(img, chip_details(rect, chip_dims(32,32)), crop);

// Also randomly flip the image
if (rnd.get_random_double() > 0.5)
crop = fliplr(crop);

// And then randomly adjust the colors.
apply_random_color_offset(crop, rnd);
}

void randomly_crop_images (
const matrix& img,
dlib::array>& crops,
dlib::rand& rnd,
long num_crops
)
{
std::vector dets;
for (long i = 0; i < num_crops; ++i)
{
auto rect = make_random_cropping_rect_resnet(img, rnd);
dets.push_back(chip_details(rect, chip_dims(32,32)));
}

extract_image_chips(img, dets, crops);

for (auto&& img : crops)
{
// Also randomly flip the image
if (rnd.get_random_double() > 0.5)
img = fliplr(img);

// And then randomly adjust the colors.
apply_random_color_offset(img, rnd);
}
}

// ----------------------------------------------------------------------------------------

struct image_info
{
string filename;
string label;
long numeric_label;
};

std::vector get_imagenet_train_listing(
const std::string& images_folder
)
{
std::vector results;
image_info temp;
temp.numeric_label = 0;
// We will loop over all the label types in the dataset, each is contained in a subfolder.
auto subdirs = directory(images_folder).get_dirs();
// But first, sort the sub directories so the numeric labels will be assigned in sorted order.
std::sort(subdirs.begin(), subdirs.end());
for (auto subdir : subdirs)
{
// Now get all the images in this label type
temp.label = subdir.name();
for (auto image_file : subdir.get_files())
{
temp.filename = image_file;
results.push_back(temp);
}
++temp.numeric_label;
}
return results;
}

std::vector get_imagenet_val_listing(
const std::string& imagenet_root_dir,
const std::string& validation_images_file
)
{
ifstream fin(validation_images_file);
string label, filename;
std::vector results;
image_info temp;
temp.numeric_label = -1;
while(fin >> label >> filename)
{
temp.filename = imagenet_root_dir+"/"+filename;
if (!file_exists(temp.filename))
{
cerr << "file doesn't exist! " << temp.filename << endl;
exit(1);
}
if (label != temp.label)
++temp.numeric_label;

temp.label = label;
results.push_back(temp);
}

return results;
}

void display_progressbar(float percentage)
{
uint32_t val = (int)(percentage * 100);
uint32_t lpad = (int)(percentage * PBWIDTH);
uint32_t rpad = PBWIDTH - lpad;
printf("\r%3d%% [%.*s%*s]", val, lpad, PBSTR, rpad, "");
fflush(stdout);
}

// ----------------------------------------------------------------------------------------

int main(int argc, char** argv) try
{
if (argc != 3)
{
cout << "To run this program you need a copy of the imagenet ILSVRC2015 dataset and" << endl;
cout << "also the file http://dlib.net/files/imagenet2015_validation_images.txt.bz2" << endl;
cout << endl;
cout << "With those things, you call this program like this: " << endl;
cout << "./dnn_imagenet_train_ex /path/to/ILSVRC2015 imagenet2015_validation_images.txt" << endl;
return 1;
}

cout << "\nSCANNING IMAGENET DATASET\n" << endl;

auto listing = get_imagenet_train_listing(string("./men-women-classification/data"));
cout << "images in dataset: " << listing.size() << endl;
const auto number_of_classes = listing.back().numeric_label+1;
if (listing.size() == 0 || number_of_classes != 2)
{
cout << "Didn't find the imagenet dataset. " << endl;
return 1;
}

set_dnn_prefer_smallest_algorithms();

const double initial_learning_rate = 0.1;
const double weight_decay = 0.1;
const double momentum = 0.9;

net_type net;
dnn_trainer trainer(net,sgd(weight_decay, momentum));
trainer.be_verbose();
trainer.set_learning_rate(initial_learning_rate);
trainer.set_synchronization_file("genderNET_trainer_state_file.dat", std::chrono::minutes(5));

// This threshold is probably excessively large. You could likely get good results
// with a smaller value but if you aren't in a hurry this value will surely work well.
trainer.set_iterations_without_progress_threshold(20);
// Since the progress threshold is so large might as well set the batch normalization
// stats window to something big too.
set_all_bn_running_stats_window_sizes(net, 2);

std::vector> samples;
std::vector labels;

// Start a bunch of threads that read images from disk and pull out random crops. It's
// important to be sure to feed the GPU fast enough to keep it busy. Using multiple
// thread for this kind of data preparation helps us do that. Each thread puts the
// crops into the data queue.
dlib::pipe>> data(200);
auto f = [&data, &listing](time_t seed)
{
dlib::rand rnd(time(0)+seed);
matrix img;
std::pair> temp;
while(data.is_enabled())
{
temp.first = listing[rnd.get_random_32bit_number()%listing.size()];
load_image(img, temp.first.filename);
randomly_crop_image(img, temp.second, rnd);
data.enqueue(temp);
}
};

std::thread data_loader1([f](){ f(1); });
std::thread data_loader2([f](){ f(2); });
std::thread data_loader3([f](){ f(3); });
std::thread data_loader4([f](){ f(4); });

// The main training loop. Keep making mini-batches and giving them to the trainer.
// We will run until the learning rate has dropped by a factor of 1e-3.
int j=0;
while(trainer.get_learning_rate() >= initial_learning_rate*1e-03)
{
samples.clear();
labels.clear();
int i=0;
// make a 160 image mini-batch
std::pair> img;
while(samples.size() < 160)
{
data.dequeue(img);

samples.push_back(std::move(img.second));
labels.push_back(img.first.numeric_label);

i++;

}
trainer.train_one_step(samples, labels);
j++;

}

cout << " dnn_prefer_smallest_algorithms: EXECUTED 03 Mini Batch" << endl;
// Training done, tell threads to stop and make sure to wait for them to finish before
// moving on.
data.disable();
data_loader1.join();
data_loader2.join();
data_loader3.join();
data_loader4.join();

// also wait for threaded processing to stop in the trainer.
trainer.get_net();

net.clean();
cout << "saving network" << endl;
serialize("resnet_genderNET32.dat") << net;
<< endl;
}
catch(std::exception& e)
{
cout << e.what() << endl;
}
```
The training process runs fine, but average loss seems consistent.
`step#: 113 learning rate: 0.0001 average loss: 0.672652 steps without apparent progress: 12`

I am not sure if I'm doing anything wrong. Please help.