Function dbn_demo

examples/machine_learning/deep_belief_net.cpp:238–309

};

int dbn_demo(bool console, int perc) {
    printf("** ArrayFire DBN Demo **\n\n");

    array train_images, test_images;
    array train_target, test_target;
    int num_classes, num_train, num_test;

    // Load mnist data
    float frac = (float)(perc) / 100.0;
    setup_mnist<true>(&num_classes, &num_train, &num_test, train_images,
                      test_images, train_target, test_target, frac);

    int feature_size = train_images.elements() / num_train;

    // Reshape images into feature vectors
    array train_feats = moddims(train_images, feature_size, num_train).T();
    array test_feats  = moddims(test_images, feature_size, num_test).T();

    train_target = train_target.T();
    test_target  = test_target.T();

    // Network parameters
    vector<int> layers;
    layers.push_back(100);
    layers.push_back(50);

    // Create network
    dbn network(train_feats.dims(1), num_classes, layers);

    // Train network
    timer::start();
    network.train(train_feats, train_target,
                  0.2,    // rbm learning rate
                  4.0,    // nn learning rate
                  15,     // rbm epochs
                  250,    // nn epochs
                  100,    // batch_size
                  0.5,    // max error
                  true);  // verbose
    af::sync();
    double train_time = timer::stop();

    // Run the trained network and test accuracy.
    array train_output = network.predict(train_feats);
    array test_output  = network.predict(test_feats);

    // Benchmark prediction
    af::sync();
    timer::start();
    for (int i = 0; i < 100; i++) { network.predict(test_feats); }
    af::sync();
    double test_time = timer::stop() / 100;

    printf("\nTraining set:\n");
    printf("Accuracy on training data: %2.2f\n",
           accuracy(train_output, train_target));

    printf("\nTest set:\n");