# dlib C++ Library - dnn_inception_ex.cpp

    // 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 << "Training 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;
    }