Function main

examples/dnn_introduction_ex.cpp:28–169

using namespace dlib;
 
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;
    }


    // MNIST is broken into two parts, a training set of 60000 images and a test set of
    // 10000 images.  Each image is labeled so that we know what hand written digit is
    // depicted.  These next statements load the dataset into memory.
    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);


    // Now let's define the LeNet.  Broadly speaking, there are 3 parts to a network
    // definition.  The loss layer, a bunch of computational layers, and then an input
    // layer.  You can see these components in the network definition below.  
    // 
    // The input layer here says the network expects to be given matrix<unsigned char>
    // objects as input.  In general, you can use any dlib image or matrix type here, or
    // even define your own types by creating custom input layers.
    //
    // Then the middle layers define the computation the network will do to transform the
    // input into whatever we want.  Here we run the image through multiple convolutions,
    // ReLU units, max pooling operations, and then finally a fully connected layer that
    // converts the whole thing into just 10 numbers.  
    // 
    // Finally, the loss layer defines the relationship between the network outputs, our 10
    // numbers, and the labels in our dataset.  Since we selected loss_multiclass_log it
    // means we want to do multiclass classification with our network.   Moreover, the
    // number of network outputs (i.e. 10) is the number of possible labels.  Whichever
    // network output is largest is the predicted label.  So for example, if the first
    // network output is largest then the predicted digit is 0, if the last network output
    // is largest then the predicted digit is 9.  
    using net_type = loss_multiclass_log<
                                fc<10,        
                                relu<fc<84,   
                                relu<fc<120,  
                                max_pool<2,2,2,2,relu<con<16,5,5,1,1,
                                max_pool<2,2,2,2,relu<con<6,5,5,1,1,
                                input<matrix<unsigned char>> 
                                >>>>>>>>>>>>;
    // This net_type defines the entire network architecture.  For example, the block
    // relu<fc<84,SUBNET>> means we take the output from the subnetwork, pass it through a
    // fully connected layer with 84 outputs, then apply ReLU.  Similarly, a block of
    // max_pool<2,2,2,2,relu<con<16,5,5,1,1,SUBNET>>> means we apply 16 convolutions with a
    // 5x5 filter size and 1x1 stride to the output of a subnetwork, then apply ReLU, then
    // perform max pooling with a 2x2 window and 2x2 stride.