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/*
Train the venerable LeNet from
LeCun, Yann, et al. "Gradient-based learning applied to document recognition."
Proceedings of the IEEE 86.11 (1998): 2278-2324.
on MNIST
*/
#include <dlib/dnn.h>
#include <iostream>
#include <dlib/data_io.h>
using namespace std;
using namespace dlib;
int main(int argc, char** argv) try
{
if (argc != 2)
{
cout << "give MNIST data folder!" << endl;
return 1;
}
std::vector<matrix<unsigned char>> training_images;
std::vector<unsigned long> training_labels;
std::vector<matrix<unsigned char>> testing_images;
std::vector<unsigned long> testing_labels;
load_mnist_dataset(argv[1], training_images, training_labels, testing_images, testing_labels);
typedef loss_multiclass_log<fc<relu<fc<relu<fc<max_pool<relu<con<max_pool<relu<con<
input<matrix<unsigned char>>>>>>>>>>>>>> net_type;
net_type net(fc_(10),
relu_(),
fc_(84),
relu_(),
fc_(120),
max_pool_(2,2,2,2),
relu_(),
con_(16,5,5),
max_pool_(2,2,2,2),
relu_(),
con_(6,5,5));
dnn_trainer<net_type> trainer(net,sgd(0.1));
trainer.set_mini_batch_size(128);
trainer.be_verbose();
trainer.set_synchronization_file("mnist_sync", std::chrono::seconds(20));
trainer.train(training_images, training_labels);
net.clean();
serialize("mnist_network.dat") << net;
// Run the net on all the data to get predictions
std::vector<unsigned long> predicted_labels = net(training_images);
int num_right = 0;
int num_wrong = 0;
for (size_t i = 0; i < training_images.size(); ++i)
{
if (predicted_labels[i] == training_labels[i])
++num_right;
else
++num_wrong;
}
cout << "training num_right: " << num_right << endl;
cout << "training num_wrong: " << num_wrong << endl;
cout << "training accuracy: " << num_right/(double)(num_right+num_wrong) << endl;
predicted_labels = net(testing_images);
num_right = 0;
num_wrong = 0;
for (size_t i = 0; i < testing_images.size(); ++i)
{
if (predicted_labels[i] == testing_labels[i])
++num_right;
else
++num_wrong;
}
cout << "testing num_right: " << num_right << endl;
cout << "testing num_wrong: " << num_wrong << endl;
cout << "testing accuracy: " << num_right/(double)(num_right+num_wrong) << endl;
}
catch(std::exception& e)
{
cout << e.what() << endl;
}