    // 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 dnn_introduction_ex.cpp
        example.  So in this example program I'm going to go over a number of more
        advanced parts of the API, including:
            - Using multiple GPUs
            - Training on large datasets that don't fit in memory
            - Defining large networks
            - Accessing and configuring layers in a network
    */

    #include <dlib/dnn.h>
    #include <iostream>
    #include <dlib/data_io.h>

    using namespace std;
    using namespace dlib;

    // ----------------------------------------------------------------------------------------

    // Let's start by showing how you can conveniently define large and complex
    // networks.  The most important tool for doing this are C++'s alias templates.
    // These let us define new layer types that are combinations of a bunch of other
    // layers.  These will form the building blocks for more complex networks.

    // So let's begin by defining the building block of a residual network (see
    // Figure 2 in Deep Residual Learning for Image Recognition by He, Zhang, Ren,
    // and Sun).  We are going to decompose the residual block into a few alias
    // statements.  First, we define the core block.

    // Here we have parameterized the "block" layer on a BN layer (nominally some
    // kind of batch normalization), the number of filter outputs N, and the stride
    // the block operates at.
    template <
        int N,
        template <typename> class BN,
        int stride,
        typename SUBNET
        >
    using block  = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;

    // Next, we need to define the skip layer mechanism used in the residual network
    // paper.  They create their blocks by adding the input tensor to the output of
    // each block.  So we define an alias statement that takes a block and wraps it
    // with this skip/add structure.

    // Note the tag layer.  This layer doesn't do any computation.  It exists solely
    // so other layers can refer to it.  In this case, the add_prev1 layer looks for
    // the tag1 layer and will take the tag1 output and add it to the input of the
    // add_prev1 layer.  This combination allows us to implement skip and residual
    // style networks.  We have also set the block stride to 1 in this statement.
    // The significance of that is explained next.
    template <
        template <int,template<typename>class,int,typename> class block,
        int N,
        template<typename>class BN,
        typename SUBNET
        >
    using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;

    // Some residual blocks do downsampling.  They do this by using a stride of 2
    // instead of 1.  However, when downsampling we need to also take care to
    // downsample the part of the network that adds the original input to the output
    // or the sizes won't make sense (the network will still run, but the results
    // aren't as good).  So here we define a downsampling version of residual.  In
    // it, we make use of the skip1 layer.  This layer simply outputs whatever is
    // output by the tag1 layer.  Therefore, the skip1 layer (there are also skip2,
    // skip3, etc. in dlib) allows you to create branching network structures.

    // residual_down creates a network structure like this:
    /*
             input from SUBNET
                 /     \
                /       \
             block     downsample(using avg_pool)
                \       /
                 \     /
               add tensors (using add_prev2 which adds the output of tag2 with avg_pool's output)
                    |
                  output
    */
    template <
        template <int,template<typename>class,int,typename> class block,
        int N,
        template<typename>class BN,
        typename SUBNET
        >
    using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;

    // Now we can define 4 different residual blocks we will use in this example.
    // The first two are non-downsampling residual blocks while the last two
    // downsample.  Also, res and res_down use batch normalization while ares and
    // ares_down have had the batch normalization replaced with simple affine
    // layers.  We will use the affine version of the layers when testing our
    // networks.
    template <typename SUBNET> using res       = relu<residual<block,8,bn_con,SUBNET>>;
    template <typename SUBNET> using ares      = relu<residual<block,8,affine,SUBNET>>;
    template <typename SUBNET> using res_down  = relu<residual_down<block,8,bn_con,SUBNET>>;
    template <typename SUBNET> using ares_down = relu<residual_down<block,8,affine,SUBNET>>;

    // Now that we have these convenient aliases, we can define a residual network
    // without a lot of typing.  Note the use of a repeat layer.  This special layer
    // type allows us to type repeat<9,res,SUBNET> instead of
    // res<res<res<res<res<res<res<res<res<SUBNET>>>>>>>>>.  It will also prevent
    // the compiler from complaining about super deep template nesting when creating
    // large networks.
    const unsigned long number_of_classes = 10;
    using net_type = loss_multiclass_log<fc<number_of_classes,
                                avg_pool_everything<
                                res<res<res<res_down<
                                repeat<9,res, // repeat this layer 9 times
                                res_down<
                                res<
                                input<matrix<unsigned char>>
                                >>>>>>>>>>;

    // And finally, let's define a residual network building block that uses
    // parametric ReLU units instead of regular ReLU.
    template <typename SUBNET>
    using pres  = prelu<add_prev1<bn_con<con<8,3,3,1,1,prelu<bn_con<con<8,3,3,1,1,tag1<SUBNET>>>>>>>>;

    // ----------------------------------------------------------------------------------------

    int main(int argc, char** argv) try
    {
        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);

        // dlib uses cuDNN under the covers.  One of the features of cuDNN is the
        // option to use slower methods that use less RAM or faster methods that use
        // a lot of RAM.  If you find that you run out of RAM on your graphics card
        // then you can call this function and we will request the slower but more
        // RAM frugal cuDNN algorithms.
        set_dnn_prefer_smallest_algorithms();

        // Create a network as defined above.  This network will produce 10 outputs
        // because that's how we defined net_type.  However, fc layers can have the
        // number of outputs they produce changed at runtime.
        net_type net;
        // So if you wanted to use the same network but override the number of
        // outputs at runtime you can do so like this:
        net_type net2(num_fc_outputs(15));

        // Now, let's imagine we wanted to replace some of the relu layers with
        // prelu layers.  We might do it like this:
        using net_type2 = loss_multiclass_log<fc<number_of_classes,
                                    avg_pool_everything<
                                    pres<res<res<res_down< // 2 prelu layers here
                                    tag4<repeat<9,pres,    // 9 groups, each containing 2 prelu layers
                                    res_down<
                                    res<
                                    input<matrix<unsigned char>>
                                    >>>>>>>>>>>;

        // prelu layers have a floating point parameter.  If you want to set it to
        // something other than its default value you can do so like this:
        net_type2 pnet(prelu_(0.2),
                       prelu_(0.25),
                       repeat_group(prelu_(0.3),prelu_(0.4)) // Initialize all the prelu instances in the repeat
                                                             // layer.  repeat_group() is needed to group the
                                                             // things that are part of repeat's block.
                       );
        // As you can see, a network will greedily assign things given to its
        // constructor to the layers inside itself.  The assignment is done in the
        // order the layers are defined, but it will skip layers where the
        // assignment doesn't make sense.

        // Now let's print the details of the pnet to the screen and inspect it.
        cout << "The pnet has " << pnet.num_layers << " layers in it." << endl;
        cout << pnet << endl;
        // These print statements will output this (I've truncated it since it's
        // long, but you get the idea):
        /*
            The pnet has 131 layers in it.
            layer<0>    loss_multiclass_log
            layer<1>    fc       (num_outputs=10) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<2>    avg_pool (nr=0, nc=0, stride_y=1, stride_x=1, padding_y=0, padding_x=0)
            layer<3>    prelu    (initial_param_value=0.2)
            layer<4>    add_prev1
            layer<5>    bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<6>    con      (num_filters=8, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<7>    prelu    (initial_param_value=0.25)
            layer<8>    bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<9>    con      (num_filters=8, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<10>   tag1
            ...
            layer<34>   relu
            layer<35>   bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<36>   con      (num_filters=8, nr=3, nc=3, stride_y=2, stride_x=2, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<37>   tag1
            layer<38>   tag4
            layer<39>   prelu    (initial_param_value=0.3)
            layer<40>   add_prev1
            layer<41>   bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            ...
            layer<118>  relu
            layer<119>  bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<120>  con      (num_filters=8, nr=3, nc=3, stride_y=2, stride_x=2, padding_y=0, padding_x=0) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<121>  tag1
            layer<122>  relu
            layer<123>  add_prev1
            layer<124>  bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<125>  con      (num_filters=8, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<126>  relu
            layer<127>  bn_con   eps=1e-05 learning_rate_mult=1 weight_decay_mult=0 bias_learning_rate_mult=1 bias_weight_decay_mult=1
            layer<128>  con      (num_filters=8, nr=3, nc=3, stride_y=1, stride_x=1, padding_y=1, padding_x=1) learning_rate_mult=1 weight_decay_mult=1 bias_learning_rate_mult=1 bias_weight_decay_mult=0
            layer<129>  tag1
            layer<130>  input<matrix>
        */

        // Now that we know the index numbers for each layer, we can access them
        // individually using layer<index>(pnet).  For example, to access the output
        // tensor for the first prelu layer we can say:
        layer<3>(pnet).get_output();
        // Or to print the prelu parameter for layer 7 we can say:
        cout << "prelu param: "<< layer<7>(pnet).layer_details().get_initial_param_value() << endl;

        // We can also access layers by their type.  This next statement finds the
        // first tag1 layer in pnet, and is therefore equivalent to calling
        // layer<10>(pnet):
        layer<tag1>(pnet);
        // The tag layers don't do anything at all and exist simply so you can tag
        // parts of your network and access them by layer<tag>().  You can also
        // index relative to a tag.  So for example, to access the layer immediately
        // after tag4 you can say:
        layer<tag4,1>(pnet); // Equivalent to layer<38+1>(pnet).

        // Or to access the layer 2 layers after tag4:
        layer<tag4,2>(pnet);
        // Tagging is a very useful tool for making complex network structures.  For
        // example, the add_prev1 layer is implemented internally by using a call to
        // layer<tag1>().

        // Ok, that's enough talk about defining and inspecting networks.  Let's
        // talk about training networks!

        // The dnn_trainer will use SGD by default, but you can tell it to use
        // different solvers like adam with a weight decay of 0.0005 and the given
        // momentum parameters.
        dnn_trainer<net_type,adam> trainer(net,adam(0.0005, 0.9, 0.999));
        // Also, if you have multiple graphics cards you can tell the trainer to use
        // them together to make the training faster.  For example, replacing the
        // above constructor call with this one would cause it to use GPU cards 0
        // and 1.
        //dnn_trainer<net_type,adam> trainer(net,adam(0.0005, 0.9, 0.999), {0,1});

        trainer.be_verbose();
        // While the trainer is running it keeps an eye on the training error.  If
        // it looks like the error hasn't decreased for the last 2000 iterations it
        // will automatically reduce the learning rate by 0.1.  You can change these
        // default parameters to some other values by calling these functions.  Or
        // disable the automatic shrinking entirely by setting the shrink factor to 1.
        trainer.set_iterations_without_progress_threshold(2000);
        trainer.set_learning_rate_shrink_factor(0.1);
        // The learning rate will start at 1e-3.
        trainer.set_learning_rate(1e-3);
        trainer.set_synchronization_file("mnist_resnet_sync", std::chrono::seconds(100));

        // Now, what if your training dataset is so big it doesn't fit in RAM?  You
        // make mini-batches yourself, any way you like, and you send them to the
        // trainer by repeatedly calling trainer.train_one_step().
        //
        // For example, the loop below stream MNIST data to out trainer.
        std::vector<matrix<unsigned char>> mini_batch_samples;
        std::vector<unsigned long> mini_batch_labels;
        dlib::rand rnd(time(0));
        // Loop until the trainer's automatic shrinking has shrunk the learning rate to 1e-6.
        // Given our settings, this means it will stop training after it has shrunk the
        // learning rate 3 times.
        while(trainer.get_learning_rate() >= 1e-6)
        {
            mini_batch_samples.clear();
            mini_batch_labels.clear();

            // make a 128 image mini-batch
            while(mini_batch_samples.size() < 128)
            {
                auto idx = rnd.get_random_32bit_number()%training_images.size();
                mini_batch_samples.push_back(training_images[idx]);
                mini_batch_labels.push_back(training_labels[idx]);
            }

            // Tell the trainer to update the network given this mini-batch
            trainer.train_one_step(mini_batch_samples, mini_batch_labels);

            // You can also feed validation data into the trainer by periodically
            // calling trainer.test_one_step(samples,labels).  Unlike train_one_step(),
            // test_one_step() doesn't modify the network, it only computes the testing
            // error which it records internally.  This testing error will then be print
            // in the verbose logging and will also determine when the trainer's
            // automatic learning rate shrinking happens.  Therefore, test_one_step()
            // can be used to perform automatic early stopping based on held out data.
        }

        // When you call train_one_step(), the trainer will do its processing in a
        // separate thread.  This allows the main thread to work on loading data
        // while the trainer is busy executing the mini-batches in parallel.
        // However, this also means we need to wait for any mini-batches that are
        // still executing to stop before we mess with the net object.  Calling
        // get_net() performs the necessary synchronization.
        trainer.get_net();

        net.clean();
        serialize("mnist_res_network.dat") << net;

        // Now we have a trained network.  However, it has batch normalization
        // layers in it.  As is customary, we should replace these with simple
        // affine layers before we use the network.  This can be accomplished by
        // making a network type which is identical to net_type but with the batch
        // normalization layers replaced with affine.  For example:
        using test_net_type = loss_multiclass_log<fc<number_of_classes,
                                    avg_pool_everything<
                                    ares<ares<ares<ares_down<
                                    repeat<9,ares,
                                    ares_down<
                                    ares<
                                    input<matrix<unsigned char>>
                                    >>>>>>>>>>;
        // Then we can simply assign our trained net to our testing net.
        test_net_type tnet = net;
        // Or if you only had a file with your trained network you could deserialize
        // it directly into your testing network.
        deserialize("mnist_res_network.dat") >> tnet;

        // And finally, we can run the testing network over our data.

        std::vector<unsigned long> predicted_labels = tnet(training_images);
        int num_right = 0;
        int num_wrong = 0;
        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;

        predicted_labels = tnet(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;
    }