Function main

examples/dnn_inception_ex.cpp:64–153

        >>>>>>>>;

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)
    {