// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This is an example illustrating the use of the deep learning tools from the
dlib C++ Library. I'm assuming you have already read the introductory
dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples. In this
example we are going to show how to create inception networks.
An inception network is composed of inception blocks of the form:
input from SUBNET
/ | \
/ | \
block1 block2 ... blockN
\ | /
\ | /
concatenate tensors from blocks
|
output
That is, an inception block runs a number of smaller networks (e.g. block1,
block2) and then concatenates their results. For further reading refer to:
Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of
the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
*/
#include <dlib/dnn.h>
#include <iostream>
#include <dlib/data_io.h>
using namespace std;
using namespace dlib;
// Inception layer has some different convolutions inside. Here we define
// blocks as convolutions with different kernel size that we will use in
// inception layer block.
template <typename SUBNET> using block_a1 = relu<con<10,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_a2 = relu<con<10,3,3,1,1,relu<con<16,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a3 = relu<con<10,5,5,1,1,relu<con<16,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a4 = relu<con<10,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
// Here is inception layer definition. It uses different blocks to process input
// and returns combined output. Dlib includes a number of these inceptionN
// layer types which are themselves created using concat layers.
template <typename SUBNET> using incept_a = inception4<block_a1,block_a2,block_a3,block_a4, SUBNET>;
// Network can have inception layers of different structure. It will work
// properly so long as all the sub-blocks inside a particular inception block
// output tensors with the same number of rows and columns.
template <typename SUBNET> using block_b1 = relu<con<4,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_b2 = relu<con<4,3,3,1,1,SUBNET>>;
template <typename SUBNET> using block_b3 = relu<con<4,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
template <typename SUBNET> using incept_b = inception3<block_b1,block_b2,block_b3,SUBNET>;
// Now we can define a simple network for classifying MNIST digits. We will
// train and test this network in the code below.
using net_type = loss_multiclass_log<
fc<10,
relu<fc<32,
max_pool<2,2,2,2,incept_b<
max_pool<2,2,2,2,incept_a<
input<matrix<unsigned char>>
>>>>>>>>;
int main(int argc, char** argv) try
{
// This example is going to run on the MNIST dataset.
if (argc != 2)
{
cout << "This example needs the MNIST dataset to run!" << endl;
cout << "You can get MNIST from http://yann.lecun.com/exdb/mnist/" << endl;
cout << "Download the 4 files that comprise the dataset, decompress them, and" << endl;
cout << "put them in a folder. Then give that folder as input to this program." << 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);
// Make an instance of our inception network.
net_type net;
cout << "The net has " << net.num_layers << " layers in it." << endl;
cout << net << endl;
cout << "Traning NN..." << endl;
dnn_trainer<net_type> trainer(net);
trainer.set_learning_rate(0.01);
trainer.set_min_learning_rate(0.00001);
trainer.set_mini_batch_size(128);
trainer.be_verbose();
trainer.set_synchronization_file("inception_sync", std::chrono::seconds(20));
// Train the network. This might take a few minutes...
trainer.train(training_images, training_labels);
// At this point our net object should have learned how to classify MNIST images. But
// before we try it out let's save it to disk. Note that, since the trainer has been
// running images through the network, net will have a bunch of state in it related to
// the last batch of images it processed (e.g. outputs from each layer). Since we
// don't care about saving that kind of stuff to disk we can tell the network to forget
// about that kind of transient data so that our file will be smaller. We do this by
// "cleaning" the network before saving it.
net.clean();
serialize("mnist_network_inception.dat") << net;
// Now if we later wanted to recall the network from disk we can simply say:
// deserialize("mnist_network_inception.dat") >> net;
// Now let's run the training images through the network. This statement runs all the
// images through it and asks the loss layer to convert the network's raw output into
// labels. In our case, these labels are the numbers between 0 and 9.
std::vector<unsigned long> predicted_labels = net(training_images);
int num_right = 0;
int num_wrong = 0;
// And then let's see if it classified them correctly.
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;
// Let's also see if the network can correctly classify the testing images.
// Since MNIST is an easy dataset, we should see 99% accuracy.
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;
}