    // Copyright (C) 2015  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_DNn_LAYERS_H_
    #define DLIB_DNn_LAYERS_H_

    #include "layers_abstract.h"
    #include "../cuda/tensor.h"
    #include "core.h"
    #include <iostream>
    #include <string>
    #include "../rand.h"
    #include "../string.h"
    #include "../cuda/tensor_tools.h"
    #include "../vectorstream.h"
    #include "utilities.h"
    #include "../cuda/operation_mode.h"
    #include <sstream>

    namespace dlib
    {

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

        struct num_con_outputs
        {
            num_con_outputs(unsigned long n) : num_outputs(n) {}
            unsigned long num_outputs;
        };

        template <
            long _num_filters,
            long _nr,
            long _nc,
            int _stride_y,
            int _stride_x,
            int _padding_y = _stride_y!=1? 0 : _nr/2,
            int _padding_x = _stride_x!=1? 0 : _nc/2
            >
        class con_
        {
        public:

            static_assert(_num_filters > 0, "The number of filters must be > 0");
            static_assert(_nr >= 0, "The number of rows in a filter must be >= 0");
            static_assert(_nc >= 0, "The number of columns in a filter must be >= 0");
            static_assert(_stride_y > 0, "The filter stride must be > 0");
            static_assert(_stride_x > 0, "The filter stride must be > 0");
            static_assert(_nr==0 || (0 <= _padding_y && _padding_y < _nr), "The padding must be smaller than the filter size.");
            static_assert(_nc==0 || (0 <= _padding_x && _padding_x < _nc), "The padding must be smaller than the filter size.");
            static_assert(_nr!=0 || 0 == _padding_y, "If _nr==0 then the padding must be set to 0 as well.");
            static_assert(_nc!=0 || 0 == _padding_x, "If _nr==0 then the padding must be set to 0 as well.");

            con_(
                num_con_outputs o
            ) :
                learning_rate_multiplier(1),
                weight_decay_multiplier(1),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(0),
                num_filters_(o.num_outputs),
                padding_y_(_padding_y),
                padding_x_(_padding_x),
                use_bias(true),
                use_relu(false)
            {
                DLIB_CASSERT(num_filters_ > 0);
            }

            con_() : con_(num_con_outputs(_num_filters)) {}

            long num_filters() const { return num_filters_; }
            long nr() const
            {
                if (_nr==0)
                    return filters.nr();
                else
                    return _nr;
            }
            long nc() const
            {
                if (_nc==0)
                    return filters.nc();
                else
                    return _nc;
            }
            long stride_y() const { return _stride_y; }
            long stride_x() const { return _stride_x; }
            long padding_y() const { return padding_y_; }
            long padding_x() const { return padding_x_; }

            void set_num_filters(long num)
            {
                DLIB_CASSERT(num > 0);
                if (num != num_filters_)
                {
                    DLIB_CASSERT(get_layer_params().size() == 0,
                        "You can't change the number of filters in con_ if the parameter tensor has already been allocated.");
                    num_filters_ = num;
                }
            }

            double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
            double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val)  { weight_decay_multiplier  = val; }

            double get_bias_learning_rate_multiplier () const  { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier () const   { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }

            bool relu_is_disabled() const { return !use_relu; }

            void disable_relu()
            {
                use_relu = false;
            }

            void enable_relu()
            {
                use_relu = true;
            }

            bool bias_is_disabled() const { return !use_bias; }

            void disable_bias()
            {
                if (use_bias == false)
                    return;

                use_bias = false;
                if (params.size() == 0)
                    return;

                DLIB_CASSERT(params.size() == filters.size() + num_filters_);
                auto temp = params;
                params.set_size(params.size() - num_filters_);
                std::copy(temp.begin(), temp.end() - num_filters_, params.begin());
                biases = alias_tensor();
            }

            void enable_bias()
            {
                if (use_bias == true)
                    return;

                use_bias = true;
                if (params.size() == 0)
                    return;

                DLIB_CASSERT(params.size() == filters.size());
                auto temp = params;
                params.set_size(params.size() + num_filters_);
                std::copy(temp.begin(), temp.end(), params.begin());
                biases = alias_tensor(1, num_filters_);
                biases(params, filters.size()) = 0;
            }

            inline dpoint map_input_to_output (
                dpoint p
            ) const
            {
                p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
                p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
                return p;
            }

            inline dpoint map_output_to_input (
                dpoint p
            ) const
            {
                p.x() = p.x()*stride_x() - padding_x() + nc()/2;
                p.y() = p.y()*stride_y() - padding_y() + nr()/2;
                return p;
            }

            con_ (
                const con_& item
            ) :
                params(item.params),
                filters(item.filters),
                biases(item.biases),
                learning_rate_multiplier(item.learning_rate_multiplier),
                weight_decay_multiplier(item.weight_decay_multiplier),
                bias_learning_rate_multiplier(item.bias_learning_rate_multiplier),
                bias_weight_decay_multiplier(item.bias_weight_decay_multiplier),
                num_filters_(item.num_filters_),
                padding_y_(item.padding_y_),
                padding_x_(item.padding_x_),
                use_bias(item.use_bias),
                use_relu(item.use_relu)
            {
                // this->conv is non-copyable and basically stateless, so we have to write our
                // own copy to avoid trying to copy it and getting an error.
            }

            con_& operator= (
                const con_& item
            )
            {
                if (this == &item)
                    return *this;

                // this->conv is non-copyable and basically stateless, so we have to write our
                // own copy to avoid trying to copy it and getting an error.
                params = item.params;
                filters = item.filters;
                biases = item.biases;
                padding_y_ = item.padding_y_;
                padding_x_ = item.padding_x_;
                learning_rate_multiplier = item.learning_rate_multiplier;
                weight_decay_multiplier = item.weight_decay_multiplier;
                bias_learning_rate_multiplier = item.bias_learning_rate_multiplier;
                bias_weight_decay_multiplier = item.bias_weight_decay_multiplier;
                num_filters_ = item.num_filters_;
                use_bias = item.use_bias;
                use_relu = item.use_relu;
                return *this;
            }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                const long filt_nr = _nr!=0 ? _nr : sub.get_output().nr();
                const long filt_nc = _nc!=0 ? _nc : sub.get_output().nc();

                long num_inputs = filt_nr*filt_nc*sub.get_output().k();
                long num_outputs = num_filters_;
                // allocate params for the filters and also for the filter bias values.
                params.set_size(num_inputs*num_filters_ + static_cast<int>(use_bias) * num_filters_);

                dlib::rand rnd(std::rand());
                randomize_parameters(params, num_inputs+num_outputs, rnd);

                filters = alias_tensor(num_filters_, sub.get_output().k(), filt_nr, filt_nc);
                if (use_bias)
                {
                    biases = alias_tensor(1,num_filters_);
                    // set the initial bias values to zero
                    biases(params,filters.size()) = 0;
                }
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                conv.setup(sub.get_output(),
                           filters(params,0),
                           _stride_y,
                           _stride_x,
                           padding_y_,
                           padding_x_);

                if (use_bias)
                {
                    conv(false, output,
                         sub.get_output(),
                         filters(params,0),
                         biases(params, filters.size()),
                         use_relu);
                }
                else
                {
                    conv(false, output,
                         sub.get_output(),
                         filters(params,0));
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                conv.get_gradient_for_data (true, gradient_input, filters(params,0), sub.get_gradient_input());
                // no point computing the parameter gradients if they won't be used.
                if (learning_rate_multiplier != 0)
                {
                    auto filt = filters(params_grad,0);
                    conv.get_gradient_for_filters (false, gradient_input, sub.get_output(), filt);
                    if (use_bias)
                    {
                        auto b = biases(params_grad, filters.size());
                        tt::assign_conv_bias_gradient(b, gradient_input);
                    }
                }
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const con_& item, std::ostream& out)
            {
                serialize("con_6", out);
                serialize(item.params, out);
                serialize(item.num_filters_, out);
                serialize(_nr, out);
                serialize(_nc, out);
                serialize(_stride_y, out);
                serialize(_stride_x, out);
                serialize(item.padding_y_, out);
                serialize(item.padding_x_, out);
                serialize(item.filters, out);
                serialize(item.biases, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.use_bias, out);
                serialize(item.use_relu, out);
            }

            friend void deserialize(con_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                long nr;
                long nc;
                int stride_y;
                int stride_x;
                if (version == "con_4" || version == "con_5" || version == "con_6")
                {
                    deserialize(item.params, in);
                    deserialize(item.num_filters_, in);
                    deserialize(nr, in);
                    deserialize(nc, in);
                    deserialize(stride_y, in);
                    deserialize(stride_x, in);
                    deserialize(item.padding_y_, in);
                    deserialize(item.padding_x_, in);
                    deserialize(item.filters, in);
                    deserialize(item.biases, in);
                    deserialize(item.learning_rate_multiplier, in);
                    deserialize(item.weight_decay_multiplier, in);
                    deserialize(item.bias_learning_rate_multiplier, in);
                    deserialize(item.bias_weight_decay_multiplier, in);
                    if (item.padding_y_ != _padding_y) throw serialization_error("Wrong padding_y found while deserializing dlib::con_");
                    if (item.padding_x_ != _padding_x) throw serialization_error("Wrong padding_x found while deserializing dlib::con_");
                    if (nr != _nr) throw serialization_error("Wrong nr found while deserializing dlib::con_");
                    if (nc != _nc) throw serialization_error("Wrong nc found while deserializing dlib::con_");
                    if (stride_y != _stride_y) throw serialization_error("Wrong stride_y found while deserializing dlib::con_");
                    if (stride_x != _stride_x) throw serialization_error("Wrong stride_x found while deserializing dlib::con_");
                    if (version == "con_5" || version == "con_6")
                    {
                        deserialize(item.use_bias, in);
                    }
                    if (version == "con_6")
                    {
                        deserialize(item.use_relu, in);
                    }
                }
                else
                {
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::con_.");
                }
            }

            friend std::ostream& operator<<(std::ostream& out, const con_& item)
            {
                out << "con\t ("
                    << "num_filters="<<item.num_filters_
                    << ", nr="<<item.nr()
                    << ", nc="<<item.nc()
                    << ", stride_y="<<_stride_y
                    << ", stride_x="<<_stride_x
                    << ", padding_y="<<item.padding_y_
                    << ", padding_x="<<item.padding_x_
                    << ")";
                out << " learning_rate_mult="<<item.learning_rate_multiplier;
                out << " weight_decay_mult="<<item.weight_decay_multiplier;
                if (item.use_bias)
                {
                    out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                    out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                }
                else
                {
                    out << " use_bias=false";
                }
                if (item.use_relu)
                {
                    out << " use_relu="<< std::boolalpha << item.use_relu;
                }
                return out;
            }

            friend void to_xml(const con_& item, std::ostream& out)
            {
                out << "<con"
                    << " num_filters='"<<item.num_filters_<<"'"
                    << " nr='"<<item.nr()<<"'"
                    << " nc='"<<item.nc()<<"'"
                    << " stride_y='"<<_stride_y<<"'"
                    << " stride_x='"<<_stride_x<<"'"
                    << " padding_y='"<<item.padding_y_<<"'"
                    << " padding_x='"<<item.padding_x_<<"'"
                    << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'"
                    << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'"
                    << " bias_learning_rate_mult='"<<item.bias_learning_rate_multiplier<<"'"
                    << " bias_weight_decay_mult='"<<item.bias_weight_decay_multiplier<<"'"
                    << " use_bias='"<<(item.use_bias?"true":"false")<<"'"
                    << " use_relu='"<<(item.use_relu?"true":"false")<<"'"
                    << ">\n";
                out << mat(item.params);
                out << "</con>\n";
            }

        private:

            resizable_tensor params;
            alias_tensor filters, biases;

            tt::tensor_conv conv;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            long num_filters_;

            // These are here only because older versions of con (which you might encounter
            // serialized to disk) used different padding settings.
            int padding_y_;
            int padding_x_;
            bool use_bias;
            bool use_relu;
        };

        template <
            long num_filters,
            long nr,
            long nc,
            int stride_y,
            int stride_x,
            typename SUBNET
            >
        using con = add_layer<con_<num_filters,nr,nc,stride_y,stride_x>, SUBNET>;

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

        template <
            long _num_filters,
            long _nr,
            long _nc,
            int _stride_y,
            int _stride_x,
            int _padding_y = _stride_y!=1? 0 : _nr/2,
            int _padding_x = _stride_x!=1? 0 : _nc/2
            >
        class cont_
        {
        public:

            static_assert(_num_filters > 0, "The number of filters must be > 0");
            static_assert(_nr > 0, "The number of rows in a filter must be > 0");
            static_assert(_nc > 0, "The number of columns in a filter must be > 0");
            static_assert(_stride_y > 0, "The filter stride must be > 0");
            static_assert(_stride_x > 0, "The filter stride must be > 0");
            static_assert(0 <= _padding_y && _padding_y < _nr, "The padding must be smaller than the filter size.");
            static_assert(0 <= _padding_x && _padding_x < _nc, "The padding must be smaller than the filter size.");

            cont_(
                num_con_outputs o
            ) :
                learning_rate_multiplier(1),
                weight_decay_multiplier(1),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(0),
                num_filters_(o.num_outputs),
                padding_y_(_padding_y),
                padding_x_(_padding_x),
                use_bias(true)
            {
                DLIB_CASSERT(num_filters_ > 0);
            }

            cont_() : cont_(num_con_outputs(_num_filters)) {}

            long num_filters() const { return num_filters_; }
            long nr() const { return _nr; }
            long nc() const { return _nc; }
            long stride_y() const { return _stride_y; }
            long stride_x() const { return _stride_x; }
            long padding_y() const { return padding_y_; }
            long padding_x() const { return padding_x_; }

            void set_num_filters(long num)
            {
                DLIB_CASSERT(num > 0);
                if (num != num_filters_)
                {
                    DLIB_CASSERT(get_layer_params().size() == 0,
                        "You can't change the number of filters in cont_ if the parameter tensor has already been allocated.");
                    num_filters_ = num;
                }
            }

            double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
            double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val)  { weight_decay_multiplier  = val; }

            double get_bias_learning_rate_multiplier () const  { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier () const   { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }
            void disable_bias() { use_bias = false; }
            bool bias_is_disabled() const { return !use_bias; }

            inline dpoint map_output_to_input (
                dpoint p
            ) const
            {
                p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
                p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
                return p;
            }

            inline dpoint map_input_to_output (
                dpoint p
            ) const
            {
                p.x() = p.x()*stride_x() - padding_x() + nc()/2;
                p.y() = p.y()*stride_y() - padding_y() + nr()/2;
                return p;
            }

            cont_ (
                const cont_& item
            ) :
                params(item.params),
                filters(item.filters),
                biases(item.biases),
                learning_rate_multiplier(item.learning_rate_multiplier),
                weight_decay_multiplier(item.weight_decay_multiplier),
                bias_learning_rate_multiplier(item.bias_learning_rate_multiplier),
                bias_weight_decay_multiplier(item.bias_weight_decay_multiplier),
                num_filters_(item.num_filters_),
                padding_y_(item.padding_y_),
                padding_x_(item.padding_x_),
                use_bias(item.use_bias)
            {
                // this->conv is non-copyable and basically stateless, so we have to write our
                // own copy to avoid trying to copy it and getting an error.
            }

            cont_& operator= (
                const cont_& item
            )
            {
                if (this == &item)
                    return *this;

                // this->conv is non-copyable and basically stateless, so we have to write our
                // own copy to avoid trying to copy it and getting an error.
                params = item.params;
                filters = item.filters;
                biases = item.biases;
                padding_y_ = item.padding_y_;
                padding_x_ = item.padding_x_;
                learning_rate_multiplier = item.learning_rate_multiplier;
                weight_decay_multiplier = item.weight_decay_multiplier;
                bias_learning_rate_multiplier = item.bias_learning_rate_multiplier;
                bias_weight_decay_multiplier = item.bias_weight_decay_multiplier;
                num_filters_ = item.num_filters_;
                use_bias = item.use_bias;
                return *this;
            }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                long num_inputs = _nr*_nc*sub.get_output().k();
                long num_outputs = num_filters_;
                // allocate params for the filters and also for the filter bias values.
                params.set_size(num_inputs*num_filters_ + num_filters_ * static_cast<int>(use_bias));

                dlib::rand rnd(std::rand());
                randomize_parameters(params, num_inputs+num_outputs, rnd);

                filters = alias_tensor(sub.get_output().k(), num_filters_, _nr, _nc);
                if (use_bias)
                {
                    biases = alias_tensor(1,num_filters_);
                    // set the initial bias values to zero
                    biases(params,filters.size()) = 0;
                }
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto filt = filters(params,0);
                unsigned int gnr = _stride_y * (sub.get_output().nr() - 1) + filt.nr() - 2 * padding_y_;
                unsigned int gnc = _stride_x * (sub.get_output().nc() - 1) + filt.nc() - 2 * padding_x_;
                unsigned int gnsamps = sub.get_output().num_samples();
                unsigned int gk = filt.k();
                output.set_size(gnsamps,gk,gnr,gnc);
                conv.setup(output,filt,_stride_y,_stride_x,padding_y_,padding_x_);
                conv.get_gradient_for_data(false, sub.get_output(),filt,output);
                if (use_bias)
                {
                    tt::add(1,output,1,biases(params,filters.size()));
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                auto filt = filters(params,0);
                conv(true, sub.get_gradient_input(),gradient_input, filt);
                // no point computing the parameter gradients if they won't be used.
                if (learning_rate_multiplier != 0)
                {
                    auto filt = filters(params_grad,0);
                    conv.get_gradient_for_filters (false, sub.get_output(),gradient_input, filt);
                    if (use_bias)
                    {
                        auto b = biases(params_grad, filters.size());
                        tt::assign_conv_bias_gradient(b, gradient_input);
                    }
                }
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const cont_& item, std::ostream& out)
            {
                serialize("cont_2", out);
                serialize(item.params, out);
                serialize(item.num_filters_, out);
                serialize(_nr, out);
                serialize(_nc, out);
                serialize(_stride_y, out);
                serialize(_stride_x, out);
                serialize(item.padding_y_, out);
                serialize(item.padding_x_, out);
                serialize(item.filters, out);
                serialize(item.biases, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.use_bias, out);
            }

            friend void deserialize(cont_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                long nr;
                long nc;
                int stride_y;
                int stride_x;
                if (version == "cont_1" || version == "cont_2")
                {
                    deserialize(item.params, in);
                    deserialize(item.num_filters_, in);
                    deserialize(nr, in);
                    deserialize(nc, in);
                    deserialize(stride_y, in);
                    deserialize(stride_x, in);
                    deserialize(item.padding_y_, in);
                    deserialize(item.padding_x_, in);
                    deserialize(item.filters, in);
                    deserialize(item.biases, in);
                    deserialize(item.learning_rate_multiplier, in);
                    deserialize(item.weight_decay_multiplier, in);
                    deserialize(item.bias_learning_rate_multiplier, in);
                    deserialize(item.bias_weight_decay_multiplier, in);
                    if (item.padding_y_ != _padding_y) throw serialization_error("Wrong padding_y found while deserializing dlib::con_");
                    if (item.padding_x_ != _padding_x) throw serialization_error("Wrong padding_x found while deserializing dlib::con_");
                    if (nr != _nr) throw serialization_error("Wrong nr found while deserializing dlib::con_");
                    if (nc != _nc) throw serialization_error("Wrong nc found while deserializing dlib::con_");
                    if (stride_y != _stride_y) throw serialization_error("Wrong stride_y found while deserializing dlib::con_");
                    if (stride_x != _stride_x) throw serialization_error("Wrong stride_x found while deserializing dlib::con_");
                    if (version == "cont_2")
                    {
                        deserialize(item.use_bias, in);
                    }
                }
                else
                {
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::con_.");
                }
            }

            friend std::ostream& operator<<(std::ostream& out, const cont_& item)
            {
                out << "cont\t ("
                    << "num_filters="<<item.num_filters_
                    << ", nr="<<_nr
                    << ", nc="<<_nc
                    << ", stride_y="<<_stride_y
                    << ", stride_x="<<_stride_x
                    << ", padding_y="<<item.padding_y_
                    << ", padding_x="<<item.padding_x_
                    << ")";
                out << " learning_rate_mult="<<item.learning_rate_multiplier;
                out << " weight_decay_mult="<<item.weight_decay_multiplier;
                if (item.use_bias)
                {
                    out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                    out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                }
                else
                {
                    out << " use_bias=false";
                }
                return out;
            }

            friend void to_xml(const cont_& item, std::ostream& out)
            {
                out << "<cont"
                    << " num_filters='"<<item.num_filters_<<"'"
                    << " nr='"<<_nr<<"'"
                    << " nc='"<<_nc<<"'"
                    << " stride_y='"<<_stride_y<<"'"
                    << " stride_x='"<<_stride_x<<"'"
                    << " padding_y='"<<item.padding_y_<<"'"
                    << " padding_x='"<<item.padding_x_<<"'"
                    << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'"
                    << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'"
                    << " bias_learning_rate_mult='"<<item.bias_learning_rate_multiplier<<"'"
                    << " bias_weight_decay_mult='"<<item.bias_weight_decay_multiplier<<"'"
                    << " use_bias='"<<(item.use_bias?"true":"false")<<"'>\n";
                out << mat(item.params);
                out << "</cont>\n";
            }

        private:

            resizable_tensor params;
            alias_tensor filters, biases;

            tt::tensor_conv conv;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            long num_filters_;

            int padding_y_;
            int padding_x_;

            bool use_bias;

        };

        template <
            long num_filters,
            long nr,
            long nc,
            int stride_y,
            int stride_x,
            typename SUBNET
            >
        using cont = add_layer<cont_<num_filters,nr,nc,stride_y,stride_x>, SUBNET>;

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

        template <
            int scale_y,
            int scale_x
            >
        class upsample_
        {
        public:
            static_assert(scale_y >= 1, "upsampling scale factor can't be less than 1.");
            static_assert(scale_x >= 1, "upsampling scale factor can't be less than 1.");

            upsample_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                output.set_size(
                    sub.get_output().num_samples(),
                    sub.get_output().k(),
                    scale_y*sub.get_output().nr(),
                    scale_x*sub.get_output().nc());
                tt::resize_bilinear(output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                tt::resize_bilinear_gradient(sub.get_gradient_input(), gradient_input);
            }

            inline dpoint map_input_to_output (dpoint p) const
            {
                p.x() = p.x()*scale_x;
                p.y() = p.y()*scale_y;
                return p;
            }
            inline dpoint map_output_to_input (dpoint p) const
            {
                p.x() = p.x()/scale_x;
                p.y() = p.y()/scale_y;
                return p;
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const upsample_& /*item*/, std::ostream& out)
            {
                serialize("upsample_", out);
                serialize(scale_y, out);
                serialize(scale_x, out);
            }

            friend void deserialize(upsample_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "upsample_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::upsample_.");

                int _scale_y;
                int _scale_x;
                deserialize(_scale_y, in);
                deserialize(_scale_x, in);
                if (_scale_y != scale_y || _scale_x != scale_x)
                    throw serialization_error("Wrong scale found while deserializing dlib::upsample_");
            }

            friend std::ostream& operator<<(std::ostream& out, const upsample_& /*item*/)
            {
                out << "upsample\t ("
                    << "scale_y="<<scale_y
                    << ", scale_x="<<scale_x
                    << ")";
                return out;
            }

            friend void to_xml(const upsample_& /*item*/, std::ostream& out)
            {
                out << "<upsample"
                    << " scale_y='"<<scale_y<<"'"
                    << " scale_x='"<<scale_x<<"'/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <
            int scale,
            typename SUBNET
            >
        using upsample = add_layer<upsample_<scale,scale>, SUBNET>;

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

        template <
            long NR_,
            long NC_
            >
        class resize_to_
        {
        public:
            static_assert(NR_ >= 1, "NR resize parameter can't be less than 1.");
            static_assert(NC_ >= 1, "NC resize parameter can't be less than 1.");

            resize_to_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                scale_y = (double)NR_/(double)sub.get_output().nr();
                scale_x = (double)NC_/(double)sub.get_output().nc();

                output.set_size(
                    sub.get_output().num_samples(),
                    sub.get_output().k(),
                    NR_,
                    NC_);
                tt::resize_bilinear(output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                tt::resize_bilinear_gradient(sub.get_gradient_input(), gradient_input);
            }

            inline dpoint map_input_to_output (dpoint p) const
            {
                p.x() = p.x()*scale_x;
                p.y() = p.y()*scale_y;
                return p;
            }

            inline dpoint map_output_to_input (dpoint p) const
            {
                p.x() = p.x()/scale_x;
                p.y() = p.y()/scale_y;
                return p;
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const resize_to_& item, std::ostream& out)
            {
                serialize("resize_to_", out);
                serialize(NR_, out);
                serialize(NC_, out);
                serialize(item.scale_y, out);
                serialize(item.scale_x, out);
            }

            friend void deserialize(resize_to_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "resize_to_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::resize_to_.");

                long _nr;
                long _nc;
                deserialize(_nr, in);
                deserialize(_nc, in);
                deserialize(item.scale_y, in);
                deserialize(item.scale_x, in);
                if (_nr != NR_ || _nc != NC_)
                    throw serialization_error("Wrong size found while deserializing dlib::resize_to_");
            }

            friend std::ostream& operator<<(std::ostream& out, const resize_to_& /*item*/)
            {
                out << "resize_to ("
                    << "nr=" << NR_
                    << ", nc=" << NC_
                    << ")";
                return out;
            }

            friend void to_xml(const resize_to_& /*item*/, std::ostream& out)
            {
                out << "<resize_to";
                out << " nr='" << NR_ << "'" ;
                out << " nc='" << NC_ << "'/>\n";
            }
        private:
            resizable_tensor params;
            double scale_y;
            double scale_x;

        };  // end of class resize_to_

        template <
            long NR,
            long NC,
            typename SUBNET
            >
        using resize_to = add_layer<resize_to_<NR,NC>, SUBNET>;

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

        template <long k_ = -1, long nr_ = -1, long nc_ = -1>
        class reshape_to_
        {
        public:
            explicit reshape_to_() :
                output_k(k_),
                output_nr(nr_),
                output_nc(nc_)
            {
                static_assert(k_ == -1 || k_ > 0, "Output k must be positive or -1");
                static_assert(nr_ == -1 || nr_ > 0, "Output nr must be positive or -1");
                static_assert(nc_ == -1 || nc_ > 0, "Output nc must be positive or -1");

                input_k = input_nr = input_nc = 0;
                needs_rescale = false;
            }

            // Getters for dimensions
            long get_output_k() const { return output_k; }
            long get_output_nr() const { return output_nr; }
            long get_output_nc() const { return output_nc; }

            // Setters for dimensions
            void set_output_k(long k) {
                DLIB_CASSERT(k == -1 || k > 0, "Output k must be positive or -1 to keep original dimension");
                output_k = k;
            }
            void set_output_nr(long nr) {
                DLIB_CASSERT(nr == -1 || nr > 0, "output nr must be positive or -1 to keep original dimension");
                output_nr = nr;
            }
            void set_output_nc(long nc) {
                DLIB_CASSERT(nc == -1 || nc > 0, "output nc must be positive or -1 to keep original dimension");
                output_nc = nc;
            }

            template <typename SUBNET>
            void setup(const SUBNET& sub)
            {
                const auto& input = sub.get_output();
                input_k = input.k();
                input_nr = input.nr();
                input_nc = input.nc();

                // Calculate output dimensions using input dims where target is -1
                if (k_ == -1) output_k = input_k;
                if (nr_ == -1) output_nr = input_nr;
                if (nc_ == -1) output_nc = input_nc;

                // Check if this is well a pure reshape
                long input_elements = input_k * input_nr * input_nc;
                long output_elements = output_k * output_nr * output_nc;
                if (input_elements != output_elements && input_k == output_k) needs_rescale = true;
                DLIB_CASSERT(input_elements == output_elements || needs_rescale,
                    "Cannot reshape tensor of " << input_elements <<
                    " elements into shape with " << output_elements << " elements. " <<
                    "For spatial rescaling, the channel dimension (k) must remain constant.");
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                // Set the output size (always preserving batch dimension)
                const tensor& input = sub.get_output();
                output.set_size(input.num_samples(), output_k, output_nr, output_nc);

                if (!needs_rescale)
                {
                    // Create an alias of the input tensor with the output shape
                    alias_tensor input_alias(output.num_samples(), output_k, output_nr, output_nc);
                    // Get a view of the input tensor with the new shape
                    auto input_reshaped = input_alias(const_cast<tensor&>(input), 0);
                    // Copy the view to the output tensor
                    tt::copy_tensor(false, output, 0, input_reshaped, 0, input_reshaped.k());
                }
                else
                {
                    // Only spatial dimensions need to be resized
                    tt::resize_bilinear(output, input);
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto& grad = sub.get_gradient_input();

                if (!needs_rescale) {
                    // Create an alias of the gradient tensor with the original input shape
                    alias_tensor grad_alias(grad.num_samples(), grad.k(), grad.nr(), grad.nc());
                    // Get a view of the input gradient with the required shape
                    auto grad_reshaped = grad_alias(const_cast<tensor&>(gradient_input), 0);
                    // Copy the view to the output gradient
                    tt::copy_tensor(true, grad, 0, grad_reshaped, 0, grad_reshaped.k());
                }
                else
                {
                    // Only spatial dimensions were resized
                    tt::resize_bilinear_gradient(grad, gradient_input);
                }
            }

            // Mapping functions for coordinate transformations
            inline dpoint map_input_to_output(const dpoint& p) const {
                double scale_x = output_nc / static_cast<double>(input_nc);
                double scale_y = output_nr / static_cast<double>(input_nr);
                return dpoint(p.x() * scale_x, p.y() * scale_y);
            }
            inline dpoint map_output_to_input(const dpoint& p) const {
                double scale_x = input_nc / static_cast<double>(output_nc);
                double scale_y = input_nr / static_cast<double>(output_nr);
                return dpoint(p.x() * scale_x, p.y() * scale_y);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const reshape_to_& item, std::ostream& out)
            {
                serialize("reshape_to_", out);
                serialize(item.input_k, out);
                serialize(item.input_nr, out);
                serialize(item.input_nc, out);
                serialize(item.output_k, out);
                serialize(item.output_nr, out);
                serialize(item.output_nc, out);
                serialize(item.needs_rescale, out);
            }

            friend void deserialize(reshape_to_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "reshape_to_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::reshape_to_.");
                deserialize(item.input_k, in);
                deserialize(item.input_nr, in);
                deserialize(item.input_nc, in);
                deserialize(item.output_k, in);
                deserialize(item.output_nr, in);
                deserialize(item.output_nc, in);
                deserialize(item.needs_rescale, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const reshape_to_& item)
            {
                out << "reshape_to (";
                out << "k=" << std::to_string(item.output_k);
                out << ", nr=" << std::to_string(item.output_nr);
                out << ", nc=" << std::to_string(item.output_nc);
                out << ", mode=" << (item.needs_rescale ? "spatial_rescale" : "pure_reshape");
                out << ")";
                return out;
            }

            friend void to_xml(const reshape_to_& item, std::ostream& out)
            {
                out << "<reshape_to"
                    << " k='" << item.output_k << "'"
                    << " nr='" << item.output_nr << "'"
                    << " nc='" << item.output_nc << "'"
                    << " mode='" << (item.needs_rescale ? "spatial_rescale" : "pure_reshape") << "'"
                    << "/>\n";
            }

        private:
            long input_k, input_nr, input_nc;       // Input dimensions
    		long output_k, output_nr, output_nc;    // Output dimensions
            bool needs_rescale;
            resizable_tensor params;                // No trainable parameters
        };

        template <long k, long nr, long nc, typename SUBNET>
        using reshape_to = add_layer<reshape_to_<k, nr, nc>, SUBNET>;

        template <long k, long nr, long nc, typename SUBNET>
        using flatten = add_layer<reshape_to_<k * nr * nc, 1, 1>, SUBNET>;

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

        template <
            long _nr,
            long _nc,
            int _stride_y,
            int _stride_x,
            int _padding_y = _stride_y!=1? 0 : _nr/2,
            int _padding_x = _stride_x!=1? 0 : _nc/2
            >
        class max_pool_
        {
            static_assert(_nr >= 0, "The number of rows in a filter must be >= 0");
            static_assert(_nc >= 0, "The number of columns in a filter must be >= 0");
            static_assert(_stride_y > 0, "The filter stride must be > 0");
            static_assert(_stride_x > 0, "The filter stride must be > 0");
            static_assert(0 <= _padding_y && ((_nr==0 && _padding_y == 0) || (_nr!=0 && _padding_y < _nr)),
                "The padding must be smaller than the filter size, unless the filters size is 0.");
            static_assert(0 <= _padding_x && ((_nc==0 && _padding_x == 0) || (_nc!=0 && _padding_x < _nc)),
                "The padding must be smaller than the filter size, unless the filters size is 0.");
        public:

            max_pool_(
            ) :
                padding_y_(_padding_y),
                padding_x_(_padding_x)
            {}

            long nr() const { return _nr; }
            long nc() const { return _nc; }
            long stride_y() const { return _stride_y; }
            long stride_x() const { return _stride_x; }
            long padding_y() const { return padding_y_; }
            long padding_x() const { return padding_x_; }

            inline dpoint map_input_to_output (
                dpoint p
            ) const
            {
                p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
                p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
                return p;
            }

            inline dpoint map_output_to_input (
                dpoint p
            ) const
            {
                p.x() = p.x()*stride_x() - padding_x() + nc()/2;
                p.y() = p.y()*stride_y() - padding_y() + nr()/2;
                return p;
            }

            max_pool_ (
                const max_pool_& item
            )  :
                padding_y_(item.padding_y_),
                padding_x_(item.padding_x_)
            {
                // this->mp is non-copyable so we have to write our own copy to avoid trying to
                // copy it and getting an error.
            }

            max_pool_& operator= (
                const max_pool_& item
            )
            {
                if (this == &item)
                    return *this;

                padding_y_ = item.padding_y_;
                padding_x_ = item.padding_x_;

                // this->mp is non-copyable so we have to write our own copy to avoid trying to
                // copy it and getting an error.
                return *this;
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                mp.setup_max_pooling(_nr!=0?_nr:sub.get_output().nr(),
                                     _nc!=0?_nc:sub.get_output().nc(),
                                     _stride_y, _stride_x, padding_y_, padding_x_);

                mp(output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(const tensor& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                mp.setup_max_pooling(_nr!=0?_nr:sub.get_output().nr(),
                                     _nc!=0?_nc:sub.get_output().nc(),
                                     _stride_y, _stride_x, padding_y_, padding_x_);

                mp.get_gradient(gradient_input, computed_output, sub.get_output(), sub.get_gradient_input());
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const max_pool_& item, std::ostream& out)
            {
                serialize("max_pool_2", out);
                serialize(_nr, out);
                serialize(_nc, out);
                serialize(_stride_y, out);
                serialize(_stride_x, out);
                serialize(item.padding_y_, out);
                serialize(item.padding_x_, out);
            }

            friend void deserialize(max_pool_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                long nr;
                long nc;
                int stride_y;
                int stride_x;
                if (version == "max_pool_2")
                {
                    deserialize(nr, in);
                    deserialize(nc, in);
                    deserialize(stride_y, in);
                    deserialize(stride_x, in);
                    deserialize(item.padding_y_, in);
                    deserialize(item.padding_x_, in);
                }
                else
                {
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::max_pool_.");
                }

                if (item.padding_y_ != _padding_y) throw serialization_error("Wrong padding_y found while deserializing dlib::max_pool_");
                if (item.padding_x_ != _padding_x) throw serialization_error("Wrong padding_x found while deserializing dlib::max_pool_");
                if (_nr != nr) throw serialization_error("Wrong nr found while deserializing dlib::max_pool_");
                if (_nc != nc) throw serialization_error("Wrong nc found while deserializing dlib::max_pool_");
                if (_stride_y != stride_y) throw serialization_error("Wrong stride_y found while deserializing dlib::max_pool_");
                if (_stride_x != stride_x) throw serialization_error("Wrong stride_x found while deserializing dlib::max_pool_");
            }

            friend std::ostream& operator<<(std::ostream& out, const max_pool_& item)
            {
                out << "max_pool ("
                    << "nr="<<_nr
                    << ", nc="<<_nc
                    << ", stride_y="<<_stride_y
                    << ", stride_x="<<_stride_x
                    << ", padding_y="<<item.padding_y_
                    << ", padding_x="<<item.padding_x_
                    << ")";
                return out;
            }

            friend void to_xml(const max_pool_& item, std::ostream& out)
            {
                out << "<max_pool"
                    << " nr='"<<_nr<<"'"
                    << " nc='"<<_nc<<"'"
                    << " stride_y='"<<_stride_y<<"'"
                    << " stride_x='"<<_stride_x<<"'"
                    << " padding_y='"<<item.padding_y_<<"'"
                    << " padding_x='"<<item.padding_x_<<"'"
                    << "/>\n";
            }

        private:

            tt::pooling mp;
            resizable_tensor params;

            int padding_y_;
            int padding_x_;
        };

        template <
            long nr,
            long nc,
            int stride_y,
            int stride_x,
            typename SUBNET
            >
        using max_pool = add_layer<max_pool_<nr,nc,stride_y,stride_x>, SUBNET>;

        template <
            typename SUBNET
            >
        using max_pool_everything = add_layer<max_pool_<0,0,1,1>, SUBNET>;

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

        template <
            long _nr,
            long _nc,
            int _stride_y,
            int _stride_x,
            int _padding_y = _stride_y!=1? 0 : _nr/2,
            int _padding_x = _stride_x!=1? 0 : _nc/2
            >
        class avg_pool_
        {
        public:
            static_assert(_nr >= 0, "The number of rows in a filter must be >= 0");
            static_assert(_nc >= 0, "The number of columns in a filter must be >= 0");
            static_assert(_stride_y > 0, "The filter stride must be > 0");
            static_assert(_stride_x > 0, "The filter stride must be > 0");
            static_assert(0 <= _padding_y && ((_nr==0 && _padding_y == 0) || (_nr!=0 && _padding_y < _nr)),
                "The padding must be smaller than the filter size, unless the filters size is 0.");
            static_assert(0 <= _padding_x && ((_nc==0 && _padding_x == 0) || (_nc!=0 && _padding_x < _nc)),
                "The padding must be smaller than the filter size, unless the filters size is 0.");

            avg_pool_(
            ) :
                padding_y_(_padding_y),
                padding_x_(_padding_x)
            {}

            long nr() const { return _nr; }
            long nc() const { return _nc; }
            long stride_y() const { return _stride_y; }
            long stride_x() const { return _stride_x; }
            long padding_y() const { return padding_y_; }
            long padding_x() const { return padding_x_; }

            inline dpoint map_input_to_output (
                dpoint p
            ) const
            {
                p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
                p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
                return p;
            }

            inline dpoint map_output_to_input (
                dpoint p
            ) const
            {
                p.x() = p.x()*stride_x() - padding_x() + nc()/2;
                p.y() = p.y()*stride_y() - padding_y() + nr()/2;
                return p;
            }

            avg_pool_ (
                const avg_pool_& item
            )  :
                padding_y_(item.padding_y_),
                padding_x_(item.padding_x_)
            {
                // this->ap is non-copyable so we have to write our own copy to avoid trying to
                // copy it and getting an error.
            }

            avg_pool_& operator= (
                const avg_pool_& item
            )
            {
                if (this == &item)
                    return *this;

                padding_y_ = item.padding_y_;
                padding_x_ = item.padding_x_;

                // this->ap is non-copyable so we have to write our own copy to avoid trying to
                // copy it and getting an error.
                return *this;
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                ap.setup_avg_pooling(_nr!=0?_nr:sub.get_output().nr(),
                                     _nc!=0?_nc:sub.get_output().nc(),
                                     _stride_y, _stride_x, padding_y_, padding_x_);

                ap(output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(const tensor& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                ap.setup_avg_pooling(_nr!=0?_nr:sub.get_output().nr(),
                                     _nc!=0?_nc:sub.get_output().nc(),
                                     _stride_y, _stride_x, padding_y_, padding_x_);

                ap.get_gradient(gradient_input, computed_output, sub.get_output(), sub.get_gradient_input());
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const avg_pool_& item, std::ostream& out)
            {
                serialize("avg_pool_2", out);
                serialize(_nr, out);
                serialize(_nc, out);
                serialize(_stride_y, out);
                serialize(_stride_x, out);
                serialize(item.padding_y_, out);
                serialize(item.padding_x_, out);
            }

            friend void deserialize(avg_pool_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);

                long nr;
                long nc;
                int stride_y;
                int stride_x;
                if (version == "avg_pool_2")
                {
                    deserialize(nr, in);
                    deserialize(nc, in);
                    deserialize(stride_y, in);
                    deserialize(stride_x, in);
                    deserialize(item.padding_y_, in);
                    deserialize(item.padding_x_, in);
                }
                else
                {
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::avg_pool_.");
                }

                if (item.padding_y_ != _padding_y) throw serialization_error("Wrong padding_y found while deserializing dlib::avg_pool_");
                if (item.padding_x_ != _padding_x) throw serialization_error("Wrong padding_x found while deserializing dlib::avg_pool_");
                if (_nr != nr) throw serialization_error("Wrong nr found while deserializing dlib::avg_pool_");
                if (_nc != nc) throw serialization_error("Wrong nc found while deserializing dlib::avg_pool_");
                if (_stride_y != stride_y) throw serialization_error("Wrong stride_y found while deserializing dlib::avg_pool_");
                if (_stride_x != stride_x) throw serialization_error("Wrong stride_x found while deserializing dlib::avg_pool_");
            }

            friend std::ostream& operator<<(std::ostream& out, const avg_pool_& item)
            {
                out << "avg_pool ("
                    << "nr="<<_nr
                    << ", nc="<<_nc
                    << ", stride_y="<<_stride_y
                    << ", stride_x="<<_stride_x
                    << ", padding_y="<<item.padding_y_
                    << ", padding_x="<<item.padding_x_
                    << ")";
                return out;
            }

            friend void to_xml(const avg_pool_& item, std::ostream& out)
            {
                out << "<avg_pool"
                    << " nr='"<<_nr<<"'"
                    << " nc='"<<_nc<<"'"
                    << " stride_y='"<<_stride_y<<"'"
                    << " stride_x='"<<_stride_x<<"'"
                    << " padding_y='"<<item.padding_y_<<"'"
                    << " padding_x='"<<item.padding_x_<<"'"
                    << "/>\n";
            }
        private:

            tt::pooling ap;
            resizable_tensor params;

            int padding_y_;
            int padding_x_;
        };

        template <
            long nr,
            long nc,
            int stride_y,
            int stride_x,
            typename SUBNET
            >
        using avg_pool = add_layer<avg_pool_<nr,nc,stride_y,stride_x>, SUBNET>;

        template <
            typename SUBNET
            >
        using avg_pool_everything = add_layer<avg_pool_<0,0,1,1>, SUBNET>;

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

        const double DEFAULT_LAYER_NORM_EPS = 1e-5;

        class layer_norm_
        {
        public:
            explicit layer_norm_(
                double eps_ = DEFAULT_LAYER_NORM_EPS
            ) :
                learning_rate_multiplier(1),
                weight_decay_multiplier(0),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(1),
                eps(eps_)
            {
            }

            double get_eps() const { return eps; }

            double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
            double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val)  { weight_decay_multiplier  = val; }

            double get_bias_learning_rate_multiplier () const  { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier () const   { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                gamma = alias_tensor(1, sub.get_output().k());
                beta = gamma;

                params.set_size(gamma.size()+beta.size());

                gamma(params,0) = 1;
                beta(params,gamma.size()) = 0;
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto g = gamma(params,0);
                auto b = beta(params,gamma.size());
                tt::layer_normalize(eps, output, means, invstds, sub.get_output(), g, b);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                auto g = gamma(params, 0);
                auto g_grad = gamma(params_grad, 0);
                auto b_grad = beta(params_grad, gamma.size());
                tt::layer_normalize_gradient(eps, gradient_input, means, invstds, sub.get_output(), g, sub.get_gradient_input(), g_grad, b_grad, dmeans, dvars);
            }

            const tensor& get_layer_params() const { return params; };
            tensor& get_layer_params() { return params; };

            friend void serialize(const layer_norm_& item, std::ostream& out)
            {
                serialize("layer_norm_", out);
                serialize(item.params, out);
                serialize(item.gamma, out);
                serialize(item.beta, out);
                serialize(item.means, out);
                serialize(item.invstds, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.eps, out);
            }

            friend void deserialize(layer_norm_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "layer_norm_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::layer_norm_.");
                deserialize(item.params, in);
                deserialize(item.gamma, in);
                deserialize(item.beta, in);
                deserialize(item.means, in);
                deserialize(item.invstds, in);
                deserialize(item.learning_rate_multiplier, in);
                deserialize(item.weight_decay_multiplier, in);
                deserialize(item.bias_learning_rate_multiplier, in);
                deserialize(item.bias_weight_decay_multiplier, in);
                deserialize(item.eps, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const layer_norm_& item)
            {
                out << "layer_norm";
                out << " eps="<<item.eps;
                out << " learning_rate_mult="<<item.learning_rate_multiplier;
                out << " weight_decay_mult="<<item.weight_decay_multiplier;
                out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                return out;
            }

            friend void to_xml(const layer_norm_& item, std::ostream& out)
            {
                out << "<layer_norm";
                out << " eps='"<<item.eps<<"'";
                out << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'";
                out << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'";
                out << " bias_learning_rate_mult='"<<item.bias_learning_rate_multiplier<<"'";
                out << " bias_weight_decay_mult='"<<item.bias_weight_decay_multiplier<<"'";
                out << ">\n";
                out << mat(item.params);
                out << "</layer_norm>\n";
            }

        private:
            resizable_tensor params;
            alias_tensor gamma, beta;
            resizable_tensor means, invstds;
            resizable_tensor dmeans, dvars;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            double eps;
        };

        template <typename SUBNET>
        using layer_norm = add_layer<layer_norm_, SUBNET>;

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

        const double DEFAULT_RMS_NORM_EPS = 1e-5;

        class rms_norm_
        {
        public:
            explicit rms_norm_(
                double eps_ = DEFAULT_RMS_NORM_EPS
            ) :
                learning_rate_multiplier(1),
                weight_decay_multiplier(0),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(1),
                eps(eps_)
            {
            }

            double get_eps() const { return eps; }

            double get_learning_rate_multiplier() const { return learning_rate_multiplier; }
            double get_weight_decay_multiplier() const { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val) { weight_decay_multiplier = val; }

            double get_bias_learning_rate_multiplier() const { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier() const { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val) { bias_weight_decay_multiplier = val; }

            inline dpoint map_input_to_output(const dpoint& p) const { return p; }
            inline dpoint map_output_to_input(const dpoint& p) const { return p; }

            template <typename SUBNET>
            void setup(const SUBNET& sub)
            {
                gamma = alias_tensor(1, sub.get_output().k());
                params.set_size(gamma.size());
                gamma(params, 0) = 1;
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto g = gamma(params, 0);
                tt::rms_normalize(eps, output, scale, sub.get_output(), g);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                auto g = gamma(params, 0);
                auto g_grad = gamma(params_grad, 0);
                tt::rms_normalize_gradient(gradient_input, scale, sub.get_output(), g, sub.get_gradient_input(), g_grad, dscale);
            }

            const tensor& get_layer_params() const { return params; };
            tensor& get_layer_params() { return params; };

            friend void serialize(const rms_norm_& item, std::ostream& out)
            {
                serialize("rms_norm_", out);
                serialize(item.params, out);
                serialize(item.gamma, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.eps, out);
            }

            friend void deserialize(rms_norm_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "rms_norm_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::rms_norm_.");
                deserialize(item.params, in);
                deserialize(item.gamma, in);
                deserialize(item.learning_rate_multiplier, in);
                deserialize(item.weight_decay_multiplier, in);
                deserialize(item.bias_learning_rate_multiplier, in);
                deserialize(item.bias_weight_decay_multiplier, in);
                deserialize(item.eps, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const rms_norm_& item)
            {
                out << "rms_norm";
                out << " (eps=" << item.eps << ")";
                out << " learning_rate_mult=" << item.learning_rate_multiplier;
                out << " weight_decay_mult=" << item.weight_decay_multiplier;
                out << " bias_learning_rate_mult=" << item.bias_learning_rate_multiplier;
                out << " bias_weight_decay_mult=" << item.bias_weight_decay_multiplier;
                return out;
            }

            friend void to_xml(const rms_norm_& item, std::ostream& out)
            {
                out << "<rms_norm";
                out << " eps='" << item.eps << "'";
                out << " learning_rate_mult='" << item.learning_rate_multiplier << "'";
                out << " weight_decay_mult='" << item.weight_decay_multiplier << "'";
                out << " bias_learning_rate_mult='" << item.bias_learning_rate_multiplier << "'";
                out << " bias_weight_decay_mult='" << item.bias_weight_decay_multiplier << "'";
                out << ">\n";
                out << mat(item.params);
                out << "</rms_norm>\n";
            }

        private:
            resizable_tensor params;
            alias_tensor gamma;
            resizable_tensor scale;
            resizable_tensor dscale;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            double eps;
        };

        template <typename SUBNET>
        using rms_norm = add_layer<rms_norm_, SUBNET>;

    // ----------------------------------------------------------------------------------------
        enum layer_mode
        {
            CONV_MODE = 0,
            FC_MODE = 1
        };

        const double DEFAULT_BATCH_NORM_EPS = 0.0001;

        template <
            layer_mode mode
            >
        class bn_
        {
        public:
            explicit bn_(
                unsigned long window_size,
                double eps_ = DEFAULT_BATCH_NORM_EPS
            ) :
                num_updates(0),
                running_stats_window_size(window_size),
                learning_rate_multiplier(1),
                weight_decay_multiplier(0),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(1),
                eps(eps_)
            {
                DLIB_CASSERT(window_size > 0, "The batch normalization running stats window size can't be 0.");
            }

            bn_() : bn_(100) {}

            layer_mode get_mode() const { return mode; }
            unsigned long get_running_stats_window_size () const { return running_stats_window_size; }
            void set_running_stats_window_size (unsigned long new_window_size )
            {
                DLIB_CASSERT(new_window_size > 0, "The batch normalization running stats window size can't be 0.");
                running_stats_window_size = new_window_size;
            }
            double get_eps() const { return eps; }

            double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
            double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val)  { weight_decay_multiplier  = val; }

            double get_bias_learning_rate_multiplier () const  { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier () const   { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                if (mode == FC_MODE)
                {
                    gamma = alias_tensor(1,
                                    sub.get_output().k(),
                                    sub.get_output().nr(),
                                    sub.get_output().nc());
                }
                else
                {
                    gamma = alias_tensor(1, sub.get_output().k());
                }
                beta = gamma;

                params.set_size(gamma.size()+beta.size());

                gamma(params,0) = 1;
                beta(params,gamma.size()) = 0;

                running_means.copy_size(gamma(params,0));
                running_variances.copy_size(gamma(params,0));
                running_means = 0;
                running_variances = 1;
                num_updates = 0;
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto g = gamma(params,0);
                auto b = beta(params,gamma.size());
                if (sub.get_output().num_samples() > 1)
                {
                    const double decay = 1.0 - num_updates/(num_updates+1.0);
                    ++num_updates;
                    if (num_updates > running_stats_window_size)
                        num_updates = running_stats_window_size;

                    if (mode == FC_MODE)
                        tt::batch_normalize(eps, output, means, invstds, decay, running_means, running_variances, sub.get_output(), g, b);
                    else
                        tt::batch_normalize_conv(eps, output, means, invstds, decay, running_means, running_variances, sub.get_output(), g, b);
                }
                else // we are running in testing mode so we just linearly scale the input tensor.
                {
                    if (mode == FC_MODE)
                        tt::batch_normalize_inference(eps, output, sub.get_output(), g, b, running_means, running_variances);
                    else
                        tt::batch_normalize_conv_inference(eps, output, sub.get_output(), g, b, running_means, running_variances);
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                auto g = gamma(params,0);
                auto g_grad = gamma(params_grad, 0);
                auto b_grad = beta(params_grad, gamma.size());
                if (mode == FC_MODE)
                    tt::batch_normalize_gradient(eps, gradient_input, means, invstds, sub.get_output(), g, sub.get_gradient_input(), g_grad, b_grad );
                else
                    tt::batch_normalize_conv_gradient(eps, gradient_input, means, invstds, sub.get_output(), g, sub.get_gradient_input(), g_grad, b_grad );
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const bn_& item, std::ostream& out)
            {
                if (mode == CONV_MODE)
                    serialize("bn_con2", out);
                else // if FC_MODE
                    serialize("bn_fc2", out);
                serialize(item.params, out);
                serialize(item.gamma, out);
                serialize(item.beta, out);
                serialize(item.means, out);
                serialize(item.invstds, out);
                serialize(item.running_means, out);
                serialize(item.running_variances, out);
                serialize(item.num_updates, out);
                serialize(item.running_stats_window_size, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.eps, out);
            }

            friend void deserialize(bn_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (mode == CONV_MODE)
                {
                    if (version != "bn_con2")
                        throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::bn_.");
                }
                else // must be in FC_MODE
                {
                    if (version != "bn_fc2")
                        throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::bn_.");
                }

                deserialize(item.params, in);
                deserialize(item.gamma, in);
                deserialize(item.beta, in);
                deserialize(item.means, in);
                deserialize(item.invstds, in);
                deserialize(item.running_means, in);
                deserialize(item.running_variances, in);
                deserialize(item.num_updates, in);
                deserialize(item.running_stats_window_size, in);
                deserialize(item.learning_rate_multiplier, in);
                deserialize(item.weight_decay_multiplier, in);
                deserialize(item.bias_learning_rate_multiplier, in);
                deserialize(item.bias_weight_decay_multiplier, in);
                deserialize(item.eps, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const bn_& item)
            {
                if (mode == CONV_MODE)
                    out << "bn_con  ";
                else
                    out << "bn_fc   ";
                out << " eps="<<item.eps;
                out << " running_stats_window_size="<<item.running_stats_window_size;
                out << " learning_rate_mult="<<item.learning_rate_multiplier;
                out << " weight_decay_mult="<<item.weight_decay_multiplier;
                out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                return out;
            }

            friend void to_xml(const bn_& item, std::ostream& out)
            {
                if (mode==CONV_MODE)
                    out << "<bn_con";
                else
                    out << "<bn_fc";

                out << " eps='"<<item.eps<<"'";
                out << " running_stats_window_size='"<<item.running_stats_window_size<<"'";
                out << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'";
                out << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'";
                out << " bias_learning_rate_mult='"<<item.bias_learning_rate_multiplier<<"'";
                out << " bias_weight_decay_mult='"<<item.bias_weight_decay_multiplier<<"'";
                out << ">\n";

                out << mat(item.params);

                if (mode==CONV_MODE)
                    out << "</bn_con>\n";
                else
                    out << "</bn_fc>\n";
            }

        private:

            friend class affine_;

            resizable_tensor params;
            alias_tensor gamma, beta;
            resizable_tensor means, running_means;
            resizable_tensor invstds, running_variances;
            unsigned long num_updates;
            unsigned long running_stats_window_size;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            double eps;
        };

        template <typename SUBNET>
        using bn_con = add_layer<bn_<CONV_MODE>, SUBNET>;
        template <typename SUBNET>
        using bn_fc = add_layer<bn_<FC_MODE>, SUBNET>;

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

        enum fc_bias_mode
        {
            FC_HAS_BIAS = 0,
            FC_NO_BIAS = 1
        };

        struct num_fc_outputs
        {
            num_fc_outputs(unsigned long n) : num_outputs(n) {}
            unsigned long num_outputs;
        };

        template <
            unsigned long num_outputs_,
            fc_bias_mode bias_mode
            >
        class fc_
        {
            static_assert(num_outputs_ > 0, "The number of outputs from a fc_ layer must be > 0");

        public:
            fc_(num_fc_outputs o) : num_outputs(o.num_outputs), num_inputs(0),
                learning_rate_multiplier(1),
                weight_decay_multiplier(1),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(0),
                use_bias(true)
            {}

            fc_() : fc_(num_fc_outputs(num_outputs_)) {}

            double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
            double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
            void set_weight_decay_multiplier(double val)  { weight_decay_multiplier  = val; }

            double get_bias_learning_rate_multiplier () const  { return bias_learning_rate_multiplier; }
            double get_bias_weight_decay_multiplier () const   { return bias_weight_decay_multiplier; }
            void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
            void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }
            void disable_bias() { use_bias = false; }
            bool bias_is_disabled() const { return !use_bias; }

            unsigned long get_num_outputs (
            ) const { return num_outputs; }

            void set_num_outputs(long num)
            {
                DLIB_CASSERT(num > 0);
                if (num != (long)num_outputs)
                {
                    DLIB_CASSERT(get_layer_params().size() == 0,
                        "You can't change the number of filters in fc_ if the parameter tensor has already been allocated.");
                    num_outputs = num;
                }
            }

            fc_bias_mode get_bias_mode (
            ) const { return bias_mode; }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                num_inputs = sub.get_output().nr()*sub.get_output().nc()*sub.get_output().k();
                if (bias_mode == FC_HAS_BIAS && use_bias)
                    params.set_size(num_inputs+1, num_outputs);
                else
                    params.set_size(num_inputs, num_outputs);

                dlib::rand rnd(std::rand());
                randomize_parameters(params, num_inputs+num_outputs, rnd);

                weights = alias_tensor(num_inputs, num_outputs);

                if (bias_mode == FC_HAS_BIAS && use_bias)
                {
                    biases = alias_tensor(1,num_outputs);
                    // set the initial bias values to zero
                    biases(params,weights.size()) = 0;
                }
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                DLIB_CASSERT((long)num_inputs == sub.get_output().nr()*sub.get_output().nc()*sub.get_output().k(),
                    "The size of the input tensor to this fc layer doesn't match the size the fc layer was trained with.");
                output.set_size(sub.get_output().num_samples(), num_outputs);

                auto w = weights(params, 0);
                tt::gemm(0,output, 1,sub.get_output(),false, w,false);
                if (bias_mode == FC_HAS_BIAS && use_bias)
                {
                    auto b = biases(params, weights.size());
                    tt::add(1,output,1,b);
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                // no point computing the parameter gradients if they won't be used.
                if (learning_rate_multiplier != 0)
                {
                    // compute the gradient of the weight parameters.
                    auto pw = weights(params_grad, 0);
                    tt::gemm(0,pw, 1,sub.get_output(),true, gradient_input,false);

                    if (bias_mode == FC_HAS_BIAS && use_bias)
                    {
                        // compute the gradient of the bias parameters.
                        auto pb = biases(params_grad, weights.size());
                        tt::assign_bias_gradient(pb, gradient_input);
                    }
                }

                // compute the gradient for the data
                auto w = weights(params, 0);
                tt::gemm(1,sub.get_gradient_input(), 1,gradient_input,false, w,true);
            }

            alias_tensor_instance get_weights()
            {
                return weights(params, 0);
            }

            alias_tensor_const_instance get_weights() const
            {
                return weights(params, 0);
            }

            alias_tensor_instance get_biases()
            {
                static_assert(bias_mode == FC_HAS_BIAS, "This fc_ layer doesn't have a bias vector "
                    "to be retrieved, as per template parameter 'bias_mode'.");
                return biases(params, weights.size());
            }

            alias_tensor_const_instance get_biases() const
            {
                static_assert(bias_mode == FC_HAS_BIAS, "This fc_ layer doesn't have a bias vector "
                    "to be retrieved, as per template parameter 'bias_mode'.");
                return biases(params, weights.size());
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const fc_& item, std::ostream& out)
            {
                serialize("fc_3", out);
                serialize(item.num_outputs, out);
                serialize(item.num_inputs, out);
                serialize(item.params, out);
                serialize(item.weights, out);
                serialize(item.biases, out);
                serialize((int)bias_mode, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.weight_decay_multiplier, out);
                serialize(item.bias_learning_rate_multiplier, out);
                serialize(item.bias_weight_decay_multiplier, out);
                serialize(item.use_bias, out);
            }

            friend void deserialize(fc_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version == "fc_2" || version == "fc_3")
                {
                    deserialize(item.num_outputs, in);
                    deserialize(item.num_inputs, in);
                    deserialize(item.params, in);
                    deserialize(item.weights, in);
                    deserialize(item.biases, in);
                    int bmode = 0;
                    deserialize(bmode, in);
                    if (bias_mode != (fc_bias_mode)bmode) throw serialization_error("Wrong fc_bias_mode found while deserializing dlib::fc_");
                    deserialize(item.learning_rate_multiplier, in);
                    deserialize(item.weight_decay_multiplier, in);
                    deserialize(item.bias_learning_rate_multiplier, in);
                    deserialize(item.bias_weight_decay_multiplier, in);
                    if (version == "fc_3")
                    {
                        deserialize(item.use_bias, in);
                    }
                }
                else
                {
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::fc_.");
                }
            }

            friend std::ostream& operator<<(std::ostream& out, const fc_& item)
            {
                if (bias_mode == FC_HAS_BIAS)
                {
                    out << "fc\t ("
                        << "num_outputs="<<item.num_outputs
                        << ")";
                    out << " learning_rate_mult="<<item.learning_rate_multiplier;
                    out << " weight_decay_mult="<<item.weight_decay_multiplier;
                    if (item.use_bias)
                    {
                        out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                        out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                    }
                    else
                    {
                        out << " use_bias=false";
                    }
                }
                else
                {
                    out << "fc_no_bias ("
                        << "num_outputs="<<item.num_outputs
                        << ")";
                    out << " learning_rate_mult="<<item.learning_rate_multiplier;
                    out << " weight_decay_mult="<<item.weight_decay_multiplier;
                }
                return out;
            }

            friend void to_xml(const fc_& item, std::ostream& out)
            {
                if (bias_mode==FC_HAS_BIAS)
                {
                    out << "<fc"
                        << " num_outputs='"<<item.num_outputs<<"'"
                        << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'"
                        << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'"
                        << " bias_learning_rate_mult='"<<item.bias_learning_rate_multiplier<<"'"
                        << " bias_weight_decay_mult='"<<item.bias_weight_decay_multiplier<<"'"
                        << " use_bias='"<<(item.use_bias?"true":"false")<<"'>\n";
                    out << mat(item.params);
                    out << "</fc>\n";
                }
                else
                {
                    out << "<fc_no_bias"
                        << " num_outputs='"<<item.num_outputs<<"'"
                        << " learning_rate_mult='"<<item.learning_rate_multiplier<<"'"
                        << " weight_decay_mult='"<<item.weight_decay_multiplier<<"'";
                    out << ">\n";
                    out << mat(item.params);
                    out << "</fc_no_bias>\n";
                }
            }

        private:

            unsigned long num_outputs;
            unsigned long num_inputs;
            resizable_tensor params;
            alias_tensor weights, biases;
            double learning_rate_multiplier;
            double weight_decay_multiplier;
            double bias_learning_rate_multiplier;
            double bias_weight_decay_multiplier;
            bool use_bias;
        };

        template <
            unsigned long num_outputs,
            typename SUBNET
            >
        using fc = add_layer<fc_<num_outputs,FC_HAS_BIAS>, SUBNET>;

        template <
            unsigned long num_outputs,
            typename SUBNET
            >
        using fc_no_bias = add_layer<fc_<num_outputs,FC_NO_BIAS>, SUBNET>;

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

        enum linear_bias_mode { LINEAR_HAS_BIAS = 0, LINEAR_NO_BIAS = 1 };

        template <
            unsigned long num_outputs_,
            linear_bias_mode bias_mode_
        >
        class linear_
        {
            static_assert(num_outputs_ > 0, "The number of outputs from a linear_ layer must be > 0");

        public:
            linear_() :
                num_outputs(num_outputs_),
                num_inputs(0),
                learning_rate_multiplier(1),
                bias_mode(bias_mode_) {
            }

            double get_learning_rate_multiplier() const { return learning_rate_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }

            unsigned long get_num_inputs() const { return num_inputs; }
            unsigned long get_num_outputs() const { return num_outputs; }
            void set_num_outputs(long num)
            {
                DLIB_CASSERT(num > 0, "The number of outputs must be > 0, but num == " << num);
                if (num != (long)num_outputs)
                {
                    DLIB_CASSERT(get_layer_params().size() == 0,
                        "You can't change the number of filters in linear_ if the parameter tensor has already been allocated.");
                    num_outputs = num;
                }
            }
            linear_bias_mode get_bias_mode() const { return bias_mode; }

            template <typename SUBNET>
            void setup(const SUBNET& sub)
            {
                num_inputs = sub.get_output().nc();
                if (bias_mode == LINEAR_HAS_BIAS)
                    params.set_size(num_inputs + 1, num_outputs);
                else
                    params.set_size(num_inputs, num_outputs);

                dlib::rand rnd(std::rand());
                randomize_parameters(params, num_inputs + num_outputs, rnd);
                weights = alias_tensor(num_inputs, num_outputs);

                if (bias_mode == LINEAR_HAS_BIAS) {
                    biases = alias_tensor(1, num_outputs);
                    biases(params, weights.size()) = 0;
                }
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                const auto& prev_output = sub.get_output();
                DLIB_CASSERT((long)num_inputs == prev_output.nc(),
                    "The size of the input tensor to this linear layer doesn't match the size the linear layer was trained with.");
                output.set_size(prev_output.num_samples(), prev_output.k(), prev_output.nr(), num_outputs);

                auto o = alias_tensor(output.num_samples() * output.k() * output.nr(), num_outputs)(output, 0);
                auto so = alias_tensor(prev_output.num_samples() * prev_output.k() * prev_output.nr(), num_inputs)(prev_output, 0);

                auto w = weights(params, 0);
                tt::gemm(0, (tensor&)o, 1, so, false, w, false);

                if (bias_mode == LINEAR_HAS_BIAS)
                {
                    auto b = biases(params, weights.size());
                    tt::add(1, (tensor&)o, 1, b);
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                auto gi = alias_tensor(gradient_input.num_samples() * gradient_input.k() * gradient_input.nr(), num_outputs)(gradient_input, 0);
                if (learning_rate_multiplier != 0)
                {
                    const auto& prev_output = sub.get_output();
                    auto pw = weights(params_grad, 0);
                    auto so = alias_tensor(prev_output.num_samples() * prev_output.k() * prev_output.nr(), num_inputs)(prev_output, 0);
                    tt::gemm(0, pw, learning_rate_multiplier, so, true, gi, false);

                    if (bias_mode == LINEAR_HAS_BIAS)
                    {
                        auto pb = biases(params_grad, weights.size());
                        tt::assign_bias_gradient(pb, gi);
                    }
                }

                const auto& prev_gradient = sub.get_gradient_input();
                auto sgi = alias_tensor(prev_gradient.num_samples() * prev_gradient.k() * prev_gradient.nr(), num_inputs)(prev_gradient, 0);
                auto w = weights(params, 0);
                tt::gemm(1, (tensor&)sgi, 1, gi, false, w, true);
            }

            alias_tensor_instance get_weights() { return weights(params, 0); }
            alias_tensor_const_instance get_weights() const { return weights(params, 0); }
            alias_tensor_instance get_biases()
            {
                static_assert(bias_mode == LINEAR_HAS_BIAS, "This linear_ layer doesn't have a bias vector "
                    "to be retrieved, as per template parameter 'bias_mode'.");
                return biases(params, weights.size());
            }
            alias_tensor_const_instance get_biases() const
            {
                static_assert(bias_mode == LINEAR_HAS_BIAS, "This linear_ layer doesn't have a bias vector "
                    "to be retrieved, as per template parameter 'bias_mode'.");
                return biases(params, weights.size());
            }

            inline dpoint map_input_to_output(const dpoint& p) const { return p; }
            inline dpoint map_output_to_input(const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const linear_& item, std::ostream& out)
            {
                serialize("linear_", out);
                serialize(item.num_outputs, out);
                serialize(item.num_inputs, out);
                serialize(item.params, out);
                serialize(item.weights, out);
                serialize(item.biases, out);
                serialize((int)item.bias_mode, out);
                serialize(item.learning_rate_multiplier, out);
            }

            friend void deserialize(linear_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version == "linear_")
                {
                    deserialize(item.num_outputs, in);
                    deserialize(item.num_inputs, in);
                    deserialize(item.params, in);
                    deserialize(item.weights, in);
                    deserialize(item.biases, in);
                    int bmode;
                    deserialize(bmode, in);
                    item.bias_mode = static_cast<linear_bias_mode>(bmode);
                    if (bias_mode_ != item.bias_mode) throw serialization_error("Wrong bias_mode found while deserializing dlib::linear_");
                    deserialize(item.learning_rate_multiplier, in);
                }
                else
                {
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::linear_.");
                }
            }

            friend std::ostream& operator<<(std::ostream& out, const linear_& item)
            {
                out << "linear\t (num_outputs=" << item.num_outputs;
                if (item.bias_mode == LINEAR_HAS_BIAS)
                    out << ", bias=true";
                else
                    out << ", bias=false";
                out << ")";
                out << " learning_rate_mult=" << item.learning_rate_multiplier;
                return out;
            }

            friend void to_xml(const linear_& item, std::ostream& out)
            {
                out << "<linear"
                    << " num_outputs='" << item.num_outputs << "'"
                    << " bias='" << ((item.bias_mode == LINEAR_HAS_BIAS) ? "true" : "false") << "'"
                    << " learning_rate_mult='" << item.learning_rate_multiplier << "'>\n";
                out << mat(item.params);
                out << "</linear>\n";
            }

        private:
            unsigned long num_inputs;
            unsigned long num_outputs;
            double learning_rate_multiplier;
            linear_bias_mode bias_mode;
            resizable_tensor params;
            alias_tensor weights, biases;
        };

        template <
            unsigned long num_outputs,
            typename SUBNET
        >
        using linear = add_layer<linear_<num_outputs, LINEAR_HAS_BIAS>, SUBNET>;

        template <
            unsigned long num_outputs,
            typename SUBNET
        >
        using linear_no_bias = add_layer<linear_<num_outputs, LINEAR_NO_BIAS>, SUBNET>;

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

        class dropout_
        {
        public:
            explicit dropout_(
                float drop_rate_ = 0.5
            ) :
                drop_rate(drop_rate_),
                rnd(std::rand())
            {
                DLIB_CASSERT(0 <= drop_rate && drop_rate <= 1);
            }

            // We have to add a copy constructor and assignment operator because the rnd object
            // is non-copyable.
            dropout_(
                const dropout_& item
            ) : drop_rate(item.drop_rate), mask(item.mask), rnd(std::rand())
            {}

            dropout_& operator= (
                const dropout_& item
            )
            {
                if (this == &item)
                    return *this;

                drop_rate = item.drop_rate;
                mask = item.mask;
                return *this;
            }

            float get_drop_rate (
            ) const { return drop_rate; }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                // create a random mask and use it to filter the data
                mask.copy_size(input);
                rnd.fill_uniform(mask);
                tt::threshold(mask, drop_rate);
                tt::multiply(false, output, input, mask);
            }

            void backward_inplace(
                const tensor& gradient_input,
                tensor& data_grad,
                tensor& /*params_grad*/
            )
            {
                if (is_same_object(gradient_input, data_grad))
                    tt::multiply(false, data_grad, mask, gradient_input);
                else
                    tt::multiply(true, data_grad, mask, gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const dropout_& item, std::ostream& out)
            {
                serialize("dropout_", out);
                serialize(item.drop_rate, out);
                serialize(item.mask, out);
            }

            friend void deserialize(dropout_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "dropout_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::dropout_.");
                deserialize(item.drop_rate, in);
                deserialize(item.mask, in);
            }

            void clean(
            )
            {
                mask.clear();
            }

            friend std::ostream& operator<<(std::ostream& out, const dropout_& item)
            {
                out << "dropout\t ("
                    << "drop_rate="<<item.drop_rate
                    << ")";
                return out;
            }

            friend void to_xml(const dropout_& item, std::ostream& out)
            {
                out << "<dropout"
                    << " drop_rate='"<<item.drop_rate<<"'";
                out << "/>\n";
            }

        private:
            float drop_rate;
            resizable_tensor mask;

            tt::tensor_rand rnd;
            resizable_tensor params; // unused
        };

        template <typename SUBNET>
        using dropout = add_layer<dropout_, SUBNET>;

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

        template <int DROP_RATE_PERCENT>
        class dropout_rate_ : public dropout_
        {
        public:
            explicit dropout_rate_() : dropout_(static_cast<float>(DROP_RATE_PERCENT) / 100.0f)
            {
                static_assert(DROP_RATE_PERCENT >= 0 && DROP_RATE_PERCENT <= 100,
                    "DROP_RATE_PERCENT must be between 0 and 100, inclusive.");
            }
        };

        template <int DROP_RATE, typename SUBNET>
        using dropout_rate = add_layer<dropout_rate_<DROP_RATE>, SUBNET>;
        template <typename SUBNET>
        using dropout_10 = add_layer<dropout_rate_<10>, SUBNET>;

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

        class multiply_
        {
        public:
            explicit multiply_(
                float val_ = 0.5
            ) :
                val(val_)
            {
            }

            multiply_ (
                const dropout_& item
            ) : val(1-item.get_drop_rate()) {}

            float get_multiply_value (
            ) const { return val; }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::affine_transform(output, input, val);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            void backward_inplace(
                const tensor& gradient_input,
                tensor& data_grad,
                tensor& /*params_grad*/
            )
            {
                if (is_same_object(gradient_input, data_grad))
                    tt::affine_transform(data_grad, gradient_input, val);
                else
                    tt::affine_transform(data_grad, data_grad, gradient_input, 1, val);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const multiply_& item, std::ostream& out)
            {
                serialize("multiply_", out);
                serialize(item.val, out);
            }

            friend void deserialize(multiply_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version == "dropout_")
                {
                    // Since we can build a multiply_ from a dropout_ we check if that's what
                    // is in the stream and if so then just convert it right here.
                    unserialize sin(version, in);
                    dropout_ temp;
                    deserialize(temp, sin);
                    item = temp;
                    return;
                }

                if (version != "multiply_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::multiply_.");
                deserialize(item.val, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const multiply_& item)
            {
                out << "multiply ("
                    << "val="<<item.val
                    << ")";
                return out;
            }

            friend void to_xml(const multiply_& item, std::ostream& out)
            {
                out << "<multiply"
                    << " val='"<<item.val<<"'";
                out << "/>\n";
            }
        private:
            float val;
            resizable_tensor params; // unused
        };

        template <typename SUBNET>
        using multiply = add_layer<multiply_, SUBNET>;

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

        class affine_
        {
        public:
            affine_(
            ) : mode(FC_MODE)
            {
            }

            affine_(
                layer_mode mode_
            ) : mode(mode_)
            {
            }

            template <
                layer_mode bnmode
                >
            affine_(
                const bn_<bnmode>& item
            )
            {
                gamma = item.gamma;
                beta = item.beta;
                mode = bnmode;

                params.copy_size(item.params);

                auto g = gamma(params,0);
                auto b = beta(params,gamma.size());

                resizable_tensor temp(item.params);
                auto sg = gamma(temp,0);
                auto sb = beta(temp,gamma.size());

                g = pointwise_divide(mat(sg), sqrt(mat(item.running_variances)+item.get_eps()));
                b = mat(sb) - pointwise_multiply(mat(g), mat(item.running_means));
            }

            layer_mode get_mode() const { return mode; }

            void disable()
            {
                params.clear();
                disabled = true;
            }

            bool is_disabled() const { return disabled; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                if (disabled)
                    return;

                if (mode == FC_MODE)
                {
                    gamma = alias_tensor(1,
                                    sub.get_output().k(),
                                    sub.get_output().nr(),
                                    sub.get_output().nc());
                }
                else
                {
                    gamma = alias_tensor(1, sub.get_output().k());
                }
                beta = gamma;

                params.set_size(gamma.size()+beta.size());

                gamma(params,0) = 1;
                beta(params,gamma.size()) = 0;
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                if (disabled)
                    return;

                auto g = gamma(params,0);
                auto b = beta(params,gamma.size());
                if (mode == FC_MODE)
                    tt::affine_transform(output, input, g, b);
                else
                    tt::affine_transform_conv(output, input, g, b);
            }

            void backward_inplace(
                const tensor& gradient_input,
                tensor& data_grad,
                tensor& /*params_grad*/
            )
            {
                if (disabled)
                    return;

                auto g = gamma(params,0);
                auto b = beta(params,gamma.size());

                // We are computing the gradient of dot(gradient_input, computed_output*g + b)
                if (mode == FC_MODE)
                {
                    if (is_same_object(gradient_input, data_grad))
                        tt::multiply(false, data_grad, gradient_input, g);
                    else
                        tt::multiply(true, data_grad, gradient_input, g);
                }
                else
                {
                    if (is_same_object(gradient_input, data_grad))
                        tt::multiply_conv(false, data_grad, gradient_input, g);
                    else
                        tt::multiply_conv(true, data_grad, gradient_input, g);
                }
            }

            alias_tensor_instance get_gamma() { return gamma(params, 0); };
            alias_tensor_const_instance get_gamma() const { return gamma(params, 0); };

            alias_tensor_instance get_beta() { return beta(params, gamma.size()); };
            alias_tensor_const_instance get_beta() const { return beta(params, gamma.size()); };

            const tensor& get_layer_params() const { return empty_params; }
            tensor& get_layer_params() { return empty_params; }

            friend void serialize(const affine_& item, std::ostream& out)
            {
                serialize("affine_2", out);
                serialize(item.params, out);
                serialize(item.gamma, out);
                serialize(item.beta, out);
                serialize((int)item.mode, out);
                serialize(item.disabled, out);
            }

            friend void deserialize(affine_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version == "bn_con2")
                {
                    // Since we can build an affine_ from a bn_ we check if that's what is in
                    // the stream and if so then just convert it right here.
                    unserialize sin(version, in);
                    bn_<CONV_MODE> temp;
                    deserialize(temp, sin);
                    item = temp;
                    return;
                }
                else if (version == "bn_fc2")
                {
                    // Since we can build an affine_ from a bn_ we check if that's what is in
                    // the stream and if so then just convert it right here.
                    unserialize sin(version, in);
                    bn_<FC_MODE> temp;
                    deserialize(temp, sin);
                    item = temp;
                    return;
                }

                if (version != "affine_" && version != "affine_2")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::affine_.");
                deserialize(item.params, in);
                deserialize(item.gamma, in);
                deserialize(item.beta, in);
                int mode;
                deserialize(mode, in);
                item.mode = (layer_mode)mode;
                if (version == "affine_2")
                    deserialize(item.disabled, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const affine_& item)
            {
                out << "affine";
                if (item.disabled)
                    out << "\t (disabled)";
                return out;
            }

            friend void to_xml(const affine_& item, std::ostream& out)
            {
                if (item.mode==CONV_MODE)
                    out << "<affine_con";
                else
                    out << "<affine_fc";
                if (item.disabled)
                    out << " disabled='"<< std::boolalpha << item.disabled << "'";
                out << ">\n";
                out << mat(item.params);

                if (item.mode==CONV_MODE)
                    out << "</affine_con>\n";
                else
                    out << "</affine_fc>\n";
            }

        private:
            resizable_tensor params, empty_params;
            alias_tensor gamma, beta;
            layer_mode mode;
            bool disabled = false;
        };

        template <typename SUBNET>
        using affine = add_layer<affine_, SUBNET>;

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

        template <
            template<typename> class tag
            >
        class add_prev_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            add_prev_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto&& t1 = sub.get_output();
                auto&& t2 = layer<tag>(sub).get_output();
                output.set_size(std::max(t1.num_samples(),t2.num_samples()),
                                std::max(t1.k(),t2.k()),
                                std::max(t1.nr(),t2.nr()),
                                std::max(t1.nc(),t2.nc()));
                tt::add(output, t1, t2);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                // The gradient just flows backwards to the two layers that forward() added
                // together.
                tt::add(sub.get_gradient_input(), sub.get_gradient_input(), gradient_input);
                tt::add(layer<tag>(sub).get_gradient_input(), layer<tag>(sub).get_gradient_input(), gradient_input);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            friend void serialize(const add_prev_& /*item*/, std::ostream& out)
            {
                serialize("add_prev_", out);
            }

            friend void deserialize(add_prev_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "add_prev_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::add_prev_.");
            }
            friend std::ostream& operator<<(std::ostream& out, const add_prev_& /*item*/)
            {
                out << "add_prev"<<id;
                return out;
            }

            friend void to_xml(const add_prev_& /*item*/, std::ostream& out)
            {
                out << "<add_prev tag='"<<id<<"'/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using add_prev = add_layer<add_prev_<tag>, SUBNET>;

        template <typename SUBNET> using add_prev1  = add_prev<tag1, SUBNET>;
        template <typename SUBNET> using add_prev2  = add_prev<tag2, SUBNET>;
        template <typename SUBNET> using add_prev3  = add_prev<tag3, SUBNET>;
        template <typename SUBNET> using add_prev4  = add_prev<tag4, SUBNET>;
        template <typename SUBNET> using add_prev5  = add_prev<tag5, SUBNET>;
        template <typename SUBNET> using add_prev6  = add_prev<tag6, SUBNET>;
        template <typename SUBNET> using add_prev7  = add_prev<tag7, SUBNET>;
        template <typename SUBNET> using add_prev8  = add_prev<tag8, SUBNET>;
        template <typename SUBNET> using add_prev9  = add_prev<tag9, SUBNET>;
        template <typename SUBNET> using add_prev10 = add_prev<tag10, SUBNET>;

        using add_prev1_  = add_prev_<tag1>;
        using add_prev2_  = add_prev_<tag2>;
        using add_prev3_  = add_prev_<tag3>;
        using add_prev4_  = add_prev_<tag4>;
        using add_prev5_  = add_prev_<tag5>;
        using add_prev6_  = add_prev_<tag6>;
        using add_prev7_  = add_prev_<tag7>;
        using add_prev8_  = add_prev_<tag8>;
        using add_prev9_  = add_prev_<tag9>;
        using add_prev10_ = add_prev_<tag10>;

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

        template <
            template<typename> class tag
            >
        class mult_prev_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            mult_prev_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto&& t1 = sub.get_output();
                auto&& t2 = layer<tag>(sub).get_output();
                output.set_size(std::max(t1.num_samples(),t2.num_samples()),
                                std::max(t1.k(),t2.k()),
                                std::max(t1.nr(),t2.nr()),
                                std::max(t1.nc(),t2.nc()));
                tt::multiply_zero_padded(false, output, t1, t2);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto&& t1 = sub.get_output();
                auto&& t2 = layer<tag>(sub).get_output();
                // The gradient just flows backwards to the two layers that forward()
                // multiplied together.
                tt::multiply_zero_padded(true, sub.get_gradient_input(), t2, gradient_input);
                tt::multiply_zero_padded(true, layer<tag>(sub).get_gradient_input(), t1, gradient_input);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            friend void serialize(const mult_prev_& /*item*/, std::ostream& out)
            {
                serialize("mult_prev_", out);
            }

            friend void deserialize(mult_prev_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "mult_prev_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::mult_prev_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const mult_prev_& /*item*/)
            {
                out << "mult_prev"<<id;
                return out;
            }

            friend void to_xml(const mult_prev_& /*item*/, std::ostream& out)
            {
                out << "<mult_prev tag='"<<id<<"'/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using mult_prev = add_layer<mult_prev_<tag>, SUBNET>;

        template <typename SUBNET> using mult_prev1  = mult_prev<tag1, SUBNET>;
        template <typename SUBNET> using mult_prev2  = mult_prev<tag2, SUBNET>;
        template <typename SUBNET> using mult_prev3  = mult_prev<tag3, SUBNET>;
        template <typename SUBNET> using mult_prev4  = mult_prev<tag4, SUBNET>;
        template <typename SUBNET> using mult_prev5  = mult_prev<tag5, SUBNET>;
        template <typename SUBNET> using mult_prev6  = mult_prev<tag6, SUBNET>;
        template <typename SUBNET> using mult_prev7  = mult_prev<tag7, SUBNET>;
        template <typename SUBNET> using mult_prev8  = mult_prev<tag8, SUBNET>;
        template <typename SUBNET> using mult_prev9  = mult_prev<tag9, SUBNET>;
        template <typename SUBNET> using mult_prev10 = mult_prev<tag10, SUBNET>;

        using mult_prev1_  = mult_prev_<tag1>;
        using mult_prev2_  = mult_prev_<tag2>;
        using mult_prev3_  = mult_prev_<tag3>;
        using mult_prev4_  = mult_prev_<tag4>;
        using mult_prev5_  = mult_prev_<tag5>;
        using mult_prev6_  = mult_prev_<tag6>;
        using mult_prev7_  = mult_prev_<tag7>;
        using mult_prev8_  = mult_prev_<tag8>;
        using mult_prev9_  = mult_prev_<tag9>;
        using mult_prev10_ = mult_prev_<tag10>;

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

        template <
            template<typename> class tag
            >
        class multm_prev_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            multm_prev_() {}
            template <typename SUBNET> void setup(const SUBNET& /*sub*/) {}

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto& t1 = sub.get_output();
                auto& t2 = layer<tag>(sub).get_output();
                output.set_size(t1.num_samples(), t1.k(), t1.nr(), t2.nc());

                tt::gemm(0, output, 1, t1, false, t2, false, operation_mode::PLANE_WISE);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto& t1 = sub.get_output();
                auto& t2 = layer<tag>(sub).get_output();
                auto& prev = sub.get_gradient_input();
                auto& prev_tag = layer<tag>(sub).get_gradient_input();

                tt::gemm(1, prev, 1, gradient_input, false, t2, true, operation_mode::PLANE_WISE);
                tt::gemm(1, prev_tag, 1, t1, true, gradient_input, false, operation_mode::PLANE_WISE);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            inline dpoint map_input_to_output(const dpoint& p) const { return p; }
            inline dpoint map_output_to_input(const dpoint& p) const { return p; }

            friend void serialize(const multm_prev_& /*item*/, std::ostream& out)
            {
                serialize("multm_prev_", out);
            }
            friend void deserialize(multm_prev_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "multm_prev_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::multm_prev_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const multm_prev_& /*item*/)
            {
                out << "multm_prev" << id;
                return out;
            }
            friend void to_xml(const multm_prev_& /*item*/, std::ostream& out)
            {
                out << "<multm_prev tag='" << id << "'/>\n";
            }

        private:
            resizable_tensor params; // unused
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using multm_prev = add_layer<multm_prev_<tag>, SUBNET>;

        template <typename SUBNET> using multm_prev1 = multm_prev<tag1, SUBNET>;
        template <typename SUBNET> using multm_prev2 = multm_prev<tag2, SUBNET>;
        template <typename SUBNET> using multm_prev3 = multm_prev<tag3, SUBNET>;
        template <typename SUBNET> using multm_prev4 = multm_prev<tag4, SUBNET>;
        template <typename SUBNET> using multm_prev5 = multm_prev<tag5, SUBNET>;
        template <typename SUBNET> using multm_prev6 = multm_prev<tag6, SUBNET>;
        template <typename SUBNET> using multm_prev7 = multm_prev<tag7, SUBNET>;
        template <typename SUBNET> using multm_prev8 = multm_prev<tag8, SUBNET>;
        template <typename SUBNET> using multm_prev9 = multm_prev<tag9, SUBNET>;
        template <typename SUBNET> using multm_prev10 = multm_prev<tag10, SUBNET>;
        using multm_prev1_ = multm_prev_<tag1>;
        using multm_prev2_ = multm_prev_<tag2>;
        using multm_prev3_ = multm_prev_<tag3>;
        using multm_prev4_ = multm_prev_<tag4>;
        using multm_prev5_ = multm_prev_<tag5>;
        using multm_prev6_ = multm_prev_<tag6>;
        using multm_prev7_ = multm_prev_<tag7>;
        using multm_prev8_ = multm_prev_<tag8>;
        using multm_prev9_ = multm_prev_<tag9>;
        using multm_prev10_ = multm_prev_<tag10>;

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

        template <
            template<typename> class tag
            >
        class resize_prev_to_tagged_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            resize_prev_to_tagged_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto& prev = sub.get_output();
                auto& tagged = layer<tag>(sub).get_output();

                DLIB_CASSERT(prev.num_samples() == tagged.num_samples());

                output.set_size(prev.num_samples(),
                                prev.k(),
                                tagged.nr(),
                                tagged.nc());

                if (prev.nr() == tagged.nr() && prev.nc() == tagged.nc())
                {
                    tt::copy_tensor(false, output, 0, prev, 0, prev.k());
                }
                else
                {
                    tt::resize_bilinear(output, prev);
                }
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto& prev = sub.get_gradient_input();

                DLIB_CASSERT(prev.k() == gradient_input.k());
                DLIB_CASSERT(prev.num_samples() == gradient_input.num_samples());

                if (prev.nr() == gradient_input.nr() && prev.nc() == gradient_input.nc())
                {
                    tt::copy_tensor(true, prev, 0, gradient_input, 0, prev.k());
                }
                else
                {
                    tt::resize_bilinear_gradient(prev, gradient_input);
                }
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            friend void serialize(const resize_prev_to_tagged_& /*item*/, std::ostream& out)
            {
                serialize("resize_prev_to_tagged_", out);
            }

            friend void deserialize(resize_prev_to_tagged_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "resize_prev_to_tagged_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::resize_prev_to_tagged_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const resize_prev_to_tagged_& /*item*/)
            {
                out << "resize_prev_to_tagged"<<id;
                return out;
            }

            friend void to_xml(const resize_prev_to_tagged_& /*item*/, std::ostream& out)
            {
                out << "<resize_prev_to_tagged tag='"<<id<<"'/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using resize_prev_to_tagged = add_layer<resize_prev_to_tagged_<tag>, SUBNET>;

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

        template <
            template<typename> class tag
            >
        class scale_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            scale_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto&& scales = sub.get_output();
                auto&& src = layer<tag>(sub).get_output();
                DLIB_CASSERT(scales.num_samples() == src.num_samples() &&
                             scales.k()           == src.k() &&
                             scales.nr()          == 1 &&
                             scales.nc()          == 1,
                             "scales.k(): " << scales.k() <<
                             "\nsrc.k(): " << src.k()
                             );

                output.copy_size(src);
                tt::scale_channels(false, output, src, scales);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto&& scales = sub.get_output();
                auto&& src = layer<tag>(sub).get_output();
                // The gradient just flows backwards to the two layers that forward()
                // read from.
                tt::scale_channels(true, layer<tag>(sub).get_gradient_input(), gradient_input, scales);

                if (reshape_src.num_samples() != src.num_samples())
                {
                    reshape_scales = alias_tensor(src.num_samples()*src.k());
                    reshape_src = alias_tensor(src.num_samples()*src.k(),src.nr()*src.nc());
                }

                auto&& scales_grad = sub.get_gradient_input();
                auto sgrad = reshape_scales(scales_grad);
                tt::dot_prods(true, sgrad, reshape_src(src), reshape_src(gradient_input));
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const scale_& item, std::ostream& out)
            {
                serialize("scale_", out);
                serialize(item.reshape_scales, out);
                serialize(item.reshape_src, out);
            }

            friend void deserialize(scale_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "scale_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::scale_.");
                deserialize(item.reshape_scales, in);
                deserialize(item.reshape_src, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const scale_& /*item*/)
            {
                out << "scale"<<id;
                return out;
            }

            friend void to_xml(const scale_& /*item*/, std::ostream& out)
            {
                out << "<scale tag='"<<id<<"'/>\n";
            }

        private:
            alias_tensor reshape_scales;
            alias_tensor reshape_src;
            resizable_tensor params;
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using scale = add_layer<scale_<tag>, SUBNET>;

        template <typename SUBNET> using scale1  = scale<tag1, SUBNET>;
        template <typename SUBNET> using scale2  = scale<tag2, SUBNET>;
        template <typename SUBNET> using scale3  = scale<tag3, SUBNET>;
        template <typename SUBNET> using scale4  = scale<tag4, SUBNET>;
        template <typename SUBNET> using scale5  = scale<tag5, SUBNET>;
        template <typename SUBNET> using scale6  = scale<tag6, SUBNET>;
        template <typename SUBNET> using scale7  = scale<tag7, SUBNET>;
        template <typename SUBNET> using scale8  = scale<tag8, SUBNET>;
        template <typename SUBNET> using scale9  = scale<tag9, SUBNET>;
        template <typename SUBNET> using scale10 = scale<tag10, SUBNET>;

        using scale1_  = scale_<tag1>;
        using scale2_  = scale_<tag2>;
        using scale3_  = scale_<tag3>;
        using scale4_  = scale_<tag4>;
        using scale5_  = scale_<tag5>;
        using scale6_  = scale_<tag6>;
        using scale7_  = scale_<tag7>;
        using scale8_  = scale_<tag8>;
        using scale9_  = scale_<tag9>;
        using scale10_ = scale_<tag10>;

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

        template <
            template<typename> class tag
            >
        class scale_prev_
        {
        public:
            const static unsigned long id = tag_id<tag>::id;

            scale_prev_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto&& src = sub.get_output();
                auto&& scales = layer<tag>(sub).get_output();
                DLIB_CASSERT(scales.num_samples() == src.num_samples() &&
                             scales.k()           == src.k() &&
                             scales.nr()          == 1 &&
                             scales.nc()          == 1,
                             "scales.k(): " << scales.k() <<
                             "\nsrc.k(): " << src.k()
                             );

                output.copy_size(src);
                tt::scale_channels(false, output, src, scales);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto&& src = sub.get_output();
                auto&& scales = layer<tag>(sub).get_output();
                tt::scale_channels(true, sub.get_gradient_input(), gradient_input, scales);

                if (reshape_src.num_samples() != src.num_samples())
                {
                    reshape_scales = alias_tensor(src.num_samples()*src.k());
                    reshape_src = alias_tensor(src.num_samples()*src.k(),src.nr()*src.nc());
                }

                auto&& scales_grad = layer<tag>(sub).get_gradient_input();
                auto sgrad = reshape_scales(scales_grad);
                tt::dot_prods(true, sgrad, reshape_src(src), reshape_src(gradient_input));
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            friend void serialize(const scale_prev_& item, std::ostream& out)
            {
                serialize("scale_prev_", out);
                serialize(item.reshape_scales, out);
                serialize(item.reshape_src, out);
            }

            friend void deserialize(scale_prev_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "scale_prev_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::scale_prev_.");
                deserialize(item.reshape_scales, in);
                deserialize(item.reshape_src, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const scale_prev_& /*item*/)
            {
                out << "scale_prev"<<id;
                return out;
            }

            friend void to_xml(const scale_prev_& /*item*/, std::ostream& out)
            {
                out << "<scale_prev tag='"<<id<<"'/>\n";
            }

        private:
            alias_tensor reshape_scales;
            alias_tensor reshape_src;
            resizable_tensor params;
        };

        template <
            template<typename> class tag,
            typename SUBNET
            >
        using scale_prev = add_layer<scale_prev_<tag>, SUBNET>;

        template <typename SUBNET> using scale_prev1  = scale_prev<tag1, SUBNET>;
        template <typename SUBNET> using scale_prev2  = scale_prev<tag2, SUBNET>;
        template <typename SUBNET> using scale_prev3  = scale_prev<tag3, SUBNET>;
        template <typename SUBNET> using scale_prev4  = scale_prev<tag4, SUBNET>;
        template <typename SUBNET> using scale_prev5  = scale_prev<tag5, SUBNET>;
        template <typename SUBNET> using scale_prev6  = scale_prev<tag6, SUBNET>;
        template <typename SUBNET> using scale_prev7  = scale_prev<tag7, SUBNET>;
        template <typename SUBNET> using scale_prev8  = scale_prev<tag8, SUBNET>;
        template <typename SUBNET> using scale_prev9  = scale_prev<tag9, SUBNET>;
        template <typename SUBNET> using scale_prev10 = scale_prev<tag10, SUBNET>;

        using scale_prev1_  = scale_prev_<tag1>;
        using scale_prev2_  = scale_prev_<tag2>;
        using scale_prev3_  = scale_prev_<tag3>;
        using scale_prev4_  = scale_prev_<tag4>;
        using scale_prev5_  = scale_prev_<tag5>;
        using scale_prev6_  = scale_prev_<tag6>;
        using scale_prev7_  = scale_prev_<tag7>;
        using scale_prev8_  = scale_prev_<tag8>;
        using scale_prev9_  = scale_prev_<tag9>;
        using scale_prev10_ = scale_prev_<tag10>;

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

        class relu_
        {
        public:
            relu_()
            {
            }

            void disable()
            {
                params.clear();
                disabled = true;
            }

            bool is_disabled() const { return disabled; }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                if (disabled)
                    return;

                tt::relu(output, input);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                if (disabled)
                    return;

                tt::relu_gradient(data_grad, computed_output, gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const relu_& item, std::ostream& out)
            {
                serialize("relu_2", out);
                serialize(item.disabled, out);
            }

            friend void deserialize(relu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version == "relu_2")
                {
                    deserialize(item.disabled, in);
                    return;
                }
                if (version != "relu_" && version != "relu_2")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::relu_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const relu_& item)
            {
                out << "relu";
                if (item.disabled)
                {
                    out << "\t (disabled)";
                }
                return out;
            }

            friend void to_xml(const relu_& item, std::ostream& out)
            {
                out << "<relu";
                if (item.disabled)
                {
                    out << " disabled='"<< std::boolalpha << item.disabled << "'";
                }
                out << "/>\n";
            }

        private:
            resizable_tensor params;
            bool disabled = false;
        };

        template <typename SUBNET>
        using relu = add_layer<relu_, SUBNET>;

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

        class prelu_
        {
        public:
            explicit prelu_(
                float initial_param_value_ = 0.25
            ) : initial_param_value(initial_param_value_)
            {
            }

            float get_initial_param_value (
            ) const { return initial_param_value; }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
                params.set_size(1);
                params = initial_param_value;
            }

            template <typename SUBNET>
            void forward(
                const SUBNET& sub,
                resizable_tensor& data_output
            )
            {
                data_output.copy_size(sub.get_output());
                tt::prelu(data_output, sub.get_output(), params);
            }

            template <typename SUBNET>
            void backward(
                const tensor& gradient_input,
                SUBNET& sub,
                tensor& params_grad
            )
            {
                tt::prelu_gradient(sub.get_gradient_input(), sub.get_output(),
                    gradient_input, params, params_grad);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const prelu_& item, std::ostream& out)
            {
                serialize("prelu_", out);
                serialize(item.params, out);
                serialize(item.initial_param_value, out);
            }

            friend void deserialize(prelu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "prelu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::prelu_.");
                deserialize(item.params, in);
                deserialize(item.initial_param_value, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const prelu_& item)
            {
                out << "prelu\t ("
                    << "initial_param_value="<<item.initial_param_value
                    << ")";
                return out;
            }

            friend void to_xml(const prelu_& item, std::ostream& out)
            {
                out << "<prelu initial_param_value='"<<item.initial_param_value<<"'>\n";
                out << mat(item.params);
                out << "</prelu>\n";
            }

        private:
            resizable_tensor params;
            float initial_param_value;
        };

        template <typename SUBNET>
        using prelu = add_layer<prelu_, SUBNET>;

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

        class leaky_relu_
        {
        public:
            explicit leaky_relu_(
                float alpha_ = 0.01f
            ) : alpha(alpha_)
            {
            }

            float get_alpha(
            ) const {
                return alpha;
            }

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::leaky_relu(output, input, alpha);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::leaky_relu_gradient(data_grad, computed_output, gradient_input, alpha);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const leaky_relu_& item, std::ostream& out)
            {
                serialize("leaky_relu_", out);
                serialize(item.alpha, out);
            }

            friend void deserialize(leaky_relu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "leaky_relu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::leaky_relu_.");
                deserialize(item.alpha, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const leaky_relu_& item)
            {
                out << "leaky_relu\t("
                    << "alpha=" << item.alpha
                    << ")";
                return out;
            }

            friend void to_xml(const leaky_relu_& item, std::ostream& out)
            {
                out << "<leaky_relu alpha='"<< item.alpha << "'/>\n";
            }

        private:
            resizable_tensor params;
            float alpha;
        };

        template <typename SUBNET>
        using leaky_relu = add_layer<leaky_relu_, SUBNET>;

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

        class sig_
        {
        public:
            sig_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::sigmoid(output, input);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::sigmoid_gradient(data_grad, computed_output, gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const sig_& /*item*/, std::ostream& out)
            {
                serialize("sig_", out);
            }

            friend void deserialize(sig_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "sig_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::sig_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const sig_& /*item*/)
            {
                out << "sig";
                return out;
            }

            friend void to_xml(const sig_& /*item*/, std::ostream& out)
            {
                out << "<sig/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using sig = add_layer<sig_, SUBNET>;

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

        class mish_
        {
        public:
            mish_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(
                const SUBNET& sub,
                resizable_tensor& data_output
            )
            {
                data_output.copy_size(sub.get_output());
                tt::mish(data_output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(
                const tensor& gradient_input,
                SUBNET& sub,
                tensor&
            )
            {
                tt::mish_gradient(sub.get_gradient_input(), sub.get_output(), gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const mish_& /*item*/, std::ostream& out)
            {
                serialize("mish_", out);
            }

            friend void deserialize(mish_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "mish_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::mish_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const mish_& /*item*/)
            {
                out << "mish";
                return out;
            }

            friend void to_xml(const mish_& /*item*/, std::ostream& out)
            {
                out << "<mish/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using mish = add_layer<mish_, SUBNET>;

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

        class htan_
        {
        public:
            htan_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::tanh(output, input);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::tanh_gradient(data_grad, computed_output, gradient_input);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const htan_& /*item*/, std::ostream& out)
            {
                serialize("htan_", out);
            }

            friend void deserialize(htan_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "htan_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::htan_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const htan_& /*item*/)
            {
                out << "htan";
                return out;
            }

            friend void to_xml(const htan_& /*item*/, std::ostream& out)
            {
                out << "<htan/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using htan = add_layer<htan_, SUBNET>;

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

        class clipped_relu_
        {
        public:
            clipped_relu_(
                const float ceiling_ = 6.0f
            ) : ceiling(ceiling_)
            {
            }

            float get_ceiling(
            ) const {
                return ceiling;
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::clipped_relu(output, input, ceiling);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::clipped_relu_gradient(data_grad, computed_output, gradient_input, ceiling);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const clipped_relu_& item, std::ostream& out)
            {
                serialize("clipped_relu_", out);
                serialize(item.ceiling, out);
            }

            friend void deserialize(clipped_relu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "clipped_relu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::clipped_relu_.");
                deserialize(item.ceiling, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const clipped_relu_& item)
            {
                out << "clipped_relu\t("
                    << "ceiling=" << item.ceiling
                    << ")";
                return out;
            }

            friend void to_xml(const clipped_relu_& item, std::ostream& out)
            {
                out << "<clipped_relu ceiling='" << item.ceiling << "'/>\n";
            }

        private:
            resizable_tensor params;
            float ceiling;
        };

        template <typename SUBNET>
        using clipped_relu = add_layer<clipped_relu_, SUBNET>;

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

        class elu_
        {
        public:
            elu_(
                const float alpha_ = 1.0f
            ) : alpha(alpha_)
            {
            }

            float get_alpha(
            ) const {
                return alpha;
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::elu(output, input, alpha);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::elu_gradient(data_grad, computed_output, gradient_input, alpha);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const elu_& item, std::ostream& out)
            {
                serialize("elu_", out);
                serialize(item.alpha, out);
            }

            friend void deserialize(elu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "elu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::elu_.");
                deserialize(item.alpha, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const elu_& item)
            {
                out << "elu\t ("
                    << "alpha=" << item.alpha
                    << ")";
                return out;
            }

            friend void to_xml(const elu_& item, std::ostream& out)
            {
                out << "<elu alpha='" << item.alpha << "'/>\n";
            }

        private:
            resizable_tensor params;
            float alpha;
        };

        template <typename SUBNET>
        using elu = add_layer<elu_, SUBNET>;

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

        class gelu_
        {
        public:
            gelu_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(
                const SUBNET& sub,
                resizable_tensor& data_output
            )
            {
                data_output.copy_size(sub.get_output());
                tt::gelu(data_output, sub.get_output());
            }

            template <typename SUBNET>
            void backward(
                const tensor& gradient_input,
                SUBNET& sub,
                tensor&
            )
            {
                tt::gelu_gradient(sub.get_gradient_input(), sub.get_output(), gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const gelu_& /*item*/, std::ostream& out)
            {
                serialize("gelu_", out);
            }

            friend void deserialize(gelu_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "gelu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::gelu_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const gelu_& /*item*/)
            {
                out << "gelu";
                return out;
            }

            friend void to_xml(const gelu_& /*item*/, std::ostream& out)
            {
                out << "<gelu/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using gelu = add_layer<gelu_, SUBNET>;

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

        class smelu_
        {
        public:
            explicit smelu_(
                float beta_ = 1
            ) : beta(beta_)
            {
            }

            float get_beta(
            ) const {
                return beta;
            }

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::smelu(output, input, beta);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::smelu_gradient(data_grad, computed_output, gradient_input, beta);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const smelu_& item, std::ostream& out)
            {
                serialize("smelu_", out);
                serialize(item.beta, out);
            }

            friend void deserialize(smelu_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "smelu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::smelu_.");
                deserialize(item.beta, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const smelu_& item)
            {
                out << "smelu\t ("
                    << "beta=" << item.beta
                    << ")";
                return out;
            }

            friend void to_xml(const smelu_& item, std::ostream& out)
            {
                out << "<smelu beta='"<< item.beta << "'/>\n";
            }

        private:
            resizable_tensor params;
            float beta;
        };

        template <typename SUBNET>
        using smelu = add_layer<smelu_, SUBNET>;

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

        class silu_
        {
        public:
            silu_(
            )
            {
            }

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(
                const SUBNET& sub,
                resizable_tensor& data_ouput)
            {
                data_ouput.copy_size(sub.get_output());
                tt::silu(data_ouput, sub.get_output());
            }

            template <typename SUBNET>
            void backward(
                const tensor& gradient_input,
                SUBNET& sub,
                tensor&
            )
            {
                tt::silu_gradient(sub.get_gradient_input(), sub.get_output(), gradient_input);
            }

            inline dpoint map_input_to_output (const dpoint& p) const { return p; }
            inline dpoint map_output_to_input (const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const silu_& /*item*/, std::ostream& out)
            {
                serialize("silu_", out);
            }

            friend void deserialize(silu_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "silu_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::silu_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const silu_& /*item*/)
            {
                out << "silu";
                return out;
            }

            friend void to_xml(const silu_& /*item*/, std::ostream& out)
            {
                out << "<silu/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using silu = add_layer<silu_, SUBNET>;

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

        template <operation_mode s_mode_>
        class softmax_
        {
        public:
            softmax_() {}

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/) {}

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::softmax(output, input, s_mode_);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor& /*params_grad*/
            )
            {
                tt::softmax_gradient(data_grad, computed_output, gradient_input, s_mode_);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const softmax_& /*item*/, std::ostream& out)
            {
                serialize("softmax_", out);
            }

            friend void deserialize(softmax_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "softmax_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::softmax_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const softmax_& /*item*/)
            {
                out << "softmax (mode=" << (s_mode_ == operation_mode::CHANNEL_WISE
                    ? "channel_wise" : "plane_wise") << ")";
                return out;
            }

            friend void to_xml(const softmax_& /*item*/, std::ostream& out)
            {
                out << "<softmax mode='" << (s_mode_ == operation_mode::CHANNEL_WISE
                    ? "channel_wise" : "plane_wise") << "'/>\n";
            }

        private:
            resizable_tensor params; // unused
        };

        template <typename SUBNET>
        using softmax = add_layer<softmax_<operation_mode::CHANNEL_WISE>, SUBNET>;

        template <typename SUBNET>
        using softmaxm = add_layer<softmax_<operation_mode::PLANE_WISE>, SUBNET>;

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

        class softmax_all_
        {
        public:
            softmax_all_()
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::softmax_all(output, input);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::softmax_all_gradient(data_grad, computed_output, gradient_input);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const softmax_all_& /*item*/, std::ostream& out)
            {
                serialize("softmax_all_", out);
            }

            friend void deserialize(softmax_all_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "softmax_all_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::softmax_all_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const softmax_all_& /*item*/)
            {
                out << "softmax_all";
                return out;
            }

            friend void to_xml(const softmax_all_& /*item*/, std::ostream& out)
            {
                out << "<softmax_all/>\n";
            }

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using softmax_all = add_layer<softmax_all_, SUBNET>;

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

        namespace impl
        {
            template <template<typename> class TAG_TYPE, template<typename> class... TAG_TYPES>
            struct concat_helper_impl{

                constexpr static size_t tag_count() {return 1 + concat_helper_impl<TAG_TYPES...>::tag_count();}
                static void list_tags(std::ostream& out)
                {
                    out << tag_id<TAG_TYPE>::id << (tag_count() > 1 ? "," : "");
                    concat_helper_impl<TAG_TYPES...>::list_tags(out);
                }

                template<typename SUBNET>
                static void resize_out(resizable_tensor& out, const SUBNET& sub, long sum_k)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_output();
                    concat_helper_impl<TAG_TYPES...>::resize_out(out, sub, sum_k + t.k());
                }
                template<typename SUBNET>
                static void concat(tensor& out, const SUBNET& sub, size_t k_offset)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_output();
                    tt::copy_tensor(false, out, k_offset, t, 0, t.k());
                    k_offset += t.k();
                    concat_helper_impl<TAG_TYPES...>::concat(out, sub, k_offset);
                }
                template<typename SUBNET>
                static void split(const tensor& input, SUBNET& sub, size_t k_offset)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_gradient_input();
                    tt::copy_tensor(true, t, 0, input, k_offset, t.k());
                    k_offset += t.k();
                    concat_helper_impl<TAG_TYPES...>::split(input, sub, k_offset);
                }
            };
            template <template<typename> class TAG_TYPE>
            struct concat_helper_impl<TAG_TYPE>{
                constexpr static size_t tag_count() {return 1;}
                static void list_tags(std::ostream& out)
                {
                    out << tag_id<TAG_TYPE>::id;
                }

                template<typename SUBNET>
                static void resize_out(resizable_tensor& out, const SUBNET& sub, long sum_k)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_output();
                    out.set_size(t.num_samples(), t.k() + sum_k, t.nr(), t.nc());
                }
                template<typename SUBNET>
                static void concat(tensor& out, const SUBNET& sub, size_t k_offset)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_output();
                    tt::copy_tensor(false, out, k_offset, t, 0, t.k());
                }
                template<typename SUBNET>
                static void split(const tensor& input, SUBNET& sub, size_t k_offset)
                {
                    auto& t = layer<TAG_TYPE>(sub).get_gradient_input();
                    tt::copy_tensor(true, t, 0, input, k_offset, t.k());
                }
            };
        }
        // concat layer
        template<
            template<typename> class... TAG_TYPES
            >
        class concat_
        {
            static void list_tags(std::ostream& out) { impl::concat_helper_impl<TAG_TYPES...>::list_tags(out);};

        public:
            constexpr static size_t tag_count() {return impl::concat_helper_impl<TAG_TYPES...>::tag_count();};

            template <typename SUBNET>
            void setup (const SUBNET&)
            {
                // do nothing
            }
            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                // the total depth of result is the sum of depths from all tags
                impl::concat_helper_impl<TAG_TYPES...>::resize_out(output, sub, 0);

                // copy output from each tag into different part result
                impl::concat_helper_impl<TAG_TYPES...>::concat(output, sub, 0);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor&)
            {
                // Gradient is split into parts for each tag layer
                impl::concat_helper_impl<TAG_TYPES...>::split(gradient_input, sub, 0);
            }

            dpoint map_input_to_output(dpoint p) const { return p; }
            dpoint map_output_to_input(dpoint p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const concat_& /*item*/, std::ostream& out)
            {
                serialize("concat_", out);
                size_t count = tag_count();
                serialize(count, out);
            }

            friend void deserialize(concat_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "concat_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::concat_.");
                size_t count_tags;
                deserialize(count_tags, in);
                if (count_tags != tag_count())
                    throw serialization_error("Invalid count of tags "+ std::to_string(count_tags) +", expecting " +
                                              std::to_string(tag_count()) +
                                                      " found while deserializing dlib::concat_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const concat_& /*item*/)
            {
                out << "concat\t (";
                list_tags(out);
                out << ")";
                return out;
            }

            friend void to_xml(const concat_& /*item*/, std::ostream& out)
            {
                out << "<concat tags='";
                list_tags(out);
                out << "'/>\n";
            }

        private:
            resizable_tensor params; // unused
        };

        // concat layer definitions
        template <template<typename> class TAG1,
                template<typename> class TAG2,
                typename SUBNET>
        using concat2 = add_layer<concat_<TAG1, TAG2>, SUBNET>;

        template <template<typename> class TAG1,
                template<typename> class TAG2,
                template<typename> class TAG3,
                typename SUBNET>
        using concat3 = add_layer<concat_<TAG1, TAG2, TAG3>, SUBNET>;

        template <template<typename> class TAG1,
                template<typename> class TAG2,
                template<typename> class TAG3,
                template<typename> class TAG4,
                typename SUBNET>
        using concat4 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4>, SUBNET>;

        template <template<typename> class TAG1,
                template<typename> class TAG2,
                template<typename> class TAG3,
                template<typename> class TAG4,
                template<typename> class TAG5,
                typename SUBNET>
        using concat5 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4, TAG5>, SUBNET>;

        // inception layer will use tags internally. If user will use tags too, some conflicts
        // possible to exclude them, here are new tags specially for inceptions
        template <typename SUBNET> using itag0  = add_tag_layer< 1000 + 0, SUBNET>;
        template <typename SUBNET> using itag1  = add_tag_layer< 1000 + 1, SUBNET>;
        template <typename SUBNET> using itag2  = add_tag_layer< 1000 + 2, SUBNET>;
        template <typename SUBNET> using itag3  = add_tag_layer< 1000 + 3, SUBNET>;
        template <typename SUBNET> using itag4  = add_tag_layer< 1000 + 4, SUBNET>;
        template <typename SUBNET> using itag5  = add_tag_layer< 1000 + 5, SUBNET>;
        // skip to inception input
        template <typename SUBNET> using iskip  = add_skip_layer< itag0, SUBNET>;

        // here are some templates to be used for creating inception layer groups
        template <template<typename>class B1,
                template<typename>class B2,
                typename SUBNET>
        using inception2 = concat2<itag1, itag2, itag1<B1<iskip< itag2<B2< itag0<SUBNET>>>>>>>;

        template <template<typename>class B1,
                template<typename>class B2,
                template<typename>class B3,
                typename SUBNET>
        using inception3 = concat3<itag1, itag2, itag3, itag1<B1<iskip< itag2<B2<iskip< itag3<B3<  itag0<SUBNET>>>>>>>>>>;

        template <template<typename>class B1,
                template<typename>class B2,
                template<typename>class B3,
                template<typename>class B4,
                typename SUBNET>
        using inception4 = concat4<itag1, itag2, itag3, itag4,
                    itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip<  itag4<B4<  itag0<SUBNET>>>>>>>>>>>>>;

        template <template<typename>class B1,
                template<typename>class B2,
                template<typename>class B3,
                template<typename>class B4,
                template<typename>class B5,
                typename SUBNET>
        using inception5 = concat5<itag1, itag2, itag3, itag4, itag5,
                    itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip<  itag4<B4<iskip<  itag5<B5<  itag0<SUBNET>>>>>>>>>>>>>>>>;

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

        const double DEFAULT_L2_NORM_EPS = 1e-5;

        class l2normalize_
        {
        public:
            explicit l2normalize_(
                double eps_ = DEFAULT_L2_NORM_EPS
            ) :
                eps(eps_)
            {
            }

            double get_eps() const { return eps; }

            template <typename SUBNET>
            void setup (const SUBNET& /*sub*/)
            {
            }

            void forward_inplace(const tensor& input, tensor& output)
            {
                tt::inverse_norms(norm, input, eps);
                tt::scale_rows(output, input, norm);
            }

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor& /*params_grad*/
            )
            {
                if (is_same_object(gradient_input, data_grad))
                {
                    tt::dot_prods(temp, gradient_input, computed_output);
                    tt::scale_rows2(0, data_grad, gradient_input, computed_output, temp, norm);
                }
                else
                {
                    tt::dot_prods(temp, gradient_input, computed_output);
                    tt::scale_rows2(1, data_grad, gradient_input, computed_output, temp, norm);
                }
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const l2normalize_& item, std::ostream& out)
            {
                serialize("l2normalize_", out);
                serialize(item.eps, out);
            }

            friend void deserialize(l2normalize_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "l2normalize_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::l2normalize_.");
                deserialize(item.eps, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const l2normalize_& item)
            {
                out << "l2normalize";
                out << " eps="<<item.eps;
                return out;
            }

            friend void to_xml(const l2normalize_& item, std::ostream& out)
            {
                out << "<l2normalize";
                out << " eps='"<<item.eps<<"'";
                out << "/>\n";
            }
        private:
            double eps;

            resizable_tensor params; // unused
            // Here only to avoid reallocation and as a cache between forward/backward
            // functions.
            resizable_tensor norm;
            resizable_tensor temp;
        };

        template <typename SUBNET>
        using l2normalize = add_layer<l2normalize_, SUBNET>;

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

        template <
            long _offset,
            long _k,
            long _nr,
            long _nc
            >
        class extract_
        {
            static_assert(_offset >= 0, "The offset must be >= 0.");
            static_assert(_k > 0,  "The number of channels must be > 0.");
            static_assert(_nr > 0, "The number of rows must be > 0.");
            static_assert(_nc > 0, "The number of columns must be > 0.");
        public:
            extract_(
            )
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                DLIB_CASSERT((long)sub.get_output().size() >= sub.get_output().num_samples()*(_offset+_k*_nr*_nc),
                    "The tensor we are trying to extract from the input tensor is too big to fit into the input tensor.");

                aout = alias_tensor(sub.get_output().num_samples(), _k*_nr*_nc);
                ain = alias_tensor(sub.get_output().num_samples(),  sub.get_output().size()/sub.get_output().num_samples());
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                if (aout.num_samples() != sub.get_output().num_samples())
                {
                    aout = alias_tensor(sub.get_output().num_samples(), _k*_nr*_nc);
                    ain = alias_tensor(sub.get_output().num_samples(),  sub.get_output().size()/sub.get_output().num_samples());
                }

                output.set_size(sub.get_output().num_samples(), _k, _nr, _nc);
                auto out = aout(output,0);
                auto in = ain(sub.get_output(),0);
                tt::copy_tensor(false, out, 0, in, _offset, _k*_nr*_nc);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto out = ain(sub.get_gradient_input(),0);
                auto in = aout(gradient_input,0);
                tt::copy_tensor(true, out, _offset, in, 0, _k*_nr*_nc);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const extract_& /*item*/, std::ostream& out)
            {
                serialize("extract_", out);
                serialize(_offset, out);
                serialize(_k, out);
                serialize(_nr, out);
                serialize(_nc, out);
            }

            friend void deserialize(extract_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "extract_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::extract_.");

                long offset;
                long k;
                long nr;
                long nc;
                deserialize(offset, in);
                deserialize(k, in);
                deserialize(nr, in);
                deserialize(nc, in);

                if (offset != _offset) throw serialization_error("Wrong offset found while deserializing dlib::extract_");
                if (k != _k)   throw serialization_error("Wrong k found while deserializing dlib::extract_");
                if (nr != _nr) throw serialization_error("Wrong nr found while deserializing dlib::extract_");
                if (nc != _nc) throw serialization_error("Wrong nc found while deserializing dlib::extract_");
            }

            friend std::ostream& operator<<(std::ostream& out, const extract_& /*item*/)
            {
                out << "extract\t ("
                    << "offset="<<_offset
                    << ", k="<<_k
                    << ", nr="<<_nr
                    << ", nc="<<_nc
                    << ")";
                return out;
            }

            friend void to_xml(const extract_& /*item*/, std::ostream& out)
            {
                out << "<extract";
                out << " offset='"<<_offset<<"'";
                out << " k='"<<_k<<"'";
                out << " nr='"<<_nr<<"'";
                out << " nc='"<<_nc<<"'";
                out << "/>\n";
            }
        private:
            alias_tensor aout, ain;

            resizable_tensor params; // unused
        };

        template <
            long offset,
            long k,
            long nr,
            long nc,
            typename SUBNET
            >
        using extract = add_layer<extract_<offset,k,nr,nc>, SUBNET>;

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

        template <
            long _offset_k,
            long _offset_nr,
            long _offset_nc,
            long _k,
            long _nr,
            long _nc
            >
        class slice_
        {
            static_assert(_offset_k >= 0, "The channel offset must be >= 0.");
            static_assert(_offset_nr >= 0, "The row offset must be >= 0.");
            static_assert(_offset_nc >= 0, "The column offset must be >= 0.");
            static_assert(_k > 0,  "The number of channels must be > 0.");
            static_assert(_nr > 0, "The number of rows must be > 0.");
            static_assert(_nc > 0, "The number of columns must be > 0.");
        public:
            slice_(
            )
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                DLIB_CASSERT((long)sub.get_output().size() >= sub.get_output().num_samples()*(_offset_k+_offset_nr+_offset_nc+_k*_nr*_nc),
                    "The tensor we are trying to slice from the input tensor is too big to fit into the input tensor.");
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                output.set_size(sub.get_output().num_samples(), _k, _nr, _nc);
                tt::copy_tensor(false, output, 0, 0, 0, sub.get_output(), _offset_k, _offset_nr, _offset_nc, _k, _nr, _nc);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                tt::copy_tensor(true, sub.get_gradient_input(), _offset_k, _offset_nr, _offset_nc, gradient_input, 0, 0, 0, _k, _nr, _nc);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const slice_& /*item*/, std::ostream& out)
            {
                serialize("slice_", out);
                serialize(_offset_k, out);
                serialize(_offset_nr, out);
                serialize(_offset_nc, out);
                serialize(_k, out);
                serialize(_nr, out);
                serialize(_nc, out);
            }

            friend void deserialize(slice_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "slice_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::slice_.");

                long offset_k;
                long offset_nr;
                long offset_nc;
                long k;
                long nr;
                long nc;
                deserialize(offset_k, in);
                deserialize(offset_nr, in);
                deserialize(offset_nc, in);
                deserialize(k, in);
                deserialize(nr, in);
                deserialize(nc, in);

                if (offset_k != _offset_k) throw serialization_error("Wrong offset_k found while deserializing dlib::slice_");
                if (offset_nr != _offset_nr) throw serialization_error("Wrong offset_nr found while deserializing dlib::slice_");
                if (offset_nc != _offset_nc) throw serialization_error("Wrong offset_nc found while deserializing dlib::slice_");
                if (k != _k)   throw serialization_error("Wrong k found while deserializing dlib::slice_");
                if (nr != _nr) throw serialization_error("Wrong nr found while deserializing dlib::slice_");
                if (nc != _nc) throw serialization_error("Wrong nc found while deserializing dlib::slice_");
            }

            friend std::ostream& operator<<(std::ostream& out, const slice_& /*item*/)
            {
                out << "slice\t ("
                    << "offset_k="<<_offset_k
                    << "offset_nr="<<_offset_nr
                    << "offset_nc="<<_offset_nc
                    << ", k="<<_k
                    << ", nr="<<_nr
                    << ", nc="<<_nc
                    << ")";
                return out;
            }

            friend void to_xml(const slice_& /*item*/, std::ostream& out)
            {
                out << "<slice";
                out << " offset_k='"<<_offset_k<<"'";
                out << " offset_nr='"<<_offset_nr<<"'";
                out << " offset_nr='"<<_offset_nc<<"'";
                out << " k='"<<_k<<"'";
                out << " nr='"<<_nr<<"'";
                out << " nc='"<<_nc<<"'";
                out << "/>\n";
            }
        private:
            resizable_tensor params; // unused
        };

        template <
            long offset_k,
            long offset_nr,
            long offset_nc,
            long k,
            long nr,
            long nc,
            typename SUBNET
            >
        using slice = add_layer<slice_<offset_k,offset_nr,offset_nc,k,nr,nc>, SUBNET>;

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

        template <long long row_stride = 2, long long col_stride = 2>
        class reorg_
        {
            static_assert(row_stride >= 1, "The row_stride must be >= 1");
            static_assert(row_stride >= 1, "The col_stride must be >= 1");

        public:
            reorg_(
            )
            {
            }

            template <typename SUBNET>
            void setup (const SUBNET& sub)
            {
                DLIB_CASSERT(sub.get_output().nr() % row_stride == 0);
                DLIB_CASSERT(sub.get_output().nc() % col_stride == 0);
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                output.set_size(
                    sub.get_output().num_samples(),
                    sub.get_output().k() * col_stride * row_stride,
                    sub.get_output().nr() / row_stride,
                    sub.get_output().nc() / col_stride
                );
                tt::reorg(false, output, row_stride, col_stride, sub.get_output());
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                tt::reorg_gradient(true, sub.get_gradient_input(), row_stride, col_stride, gradient_input);
            }

            inline dpoint map_input_to_output (dpoint p) const
            {
                p.x() = p.x() / col_stride;
                p.y() = p.y() / row_stride;
                return p;
            }
            inline dpoint map_output_to_input (dpoint p) const
            {
                p.x() = p.x() * col_stride;
                p.y() = p.y() * row_stride;
                return p;
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const reorg_& /*item*/, std::ostream& out)
            {
                serialize("reorg_", out);
                serialize(row_stride, out);
                serialize(col_stride, out);
            }

            friend void deserialize(reorg_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "reorg_")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::reorg_.");
                long long rs;
                long long cs;
                deserialize(rs, in);
                deserialize(cs, in);
                if (rs != row_stride) throw serialization_error("Wrong row_stride found while deserializing dlib::reorg_");
                if (cs != col_stride) throw serialization_error("Wrong col_stride found while deserializing dlib::reorg_");
            }

            friend std::ostream& operator<<(std::ostream& out, const reorg_& /*item*/)
            {
                out << "reorg\t ("
                    << "row_stride=" << row_stride
                    << ", col_stride=" << col_stride
                    << ")";
                return out;
            }

            friend void to_xml(const reorg_ /*item*/, std::ostream& out)
            {
                out << "<reorg";
                out << " row_stride='" << row_stride << "'";
                out << " col_stride='" << col_stride << "'";
                out << "/>\n";
            }

        private:
            resizable_tensor params; // unused

        };

        template <typename SUBNET>
        using reorg = add_layer<reorg_<2, 2>, SUBNET>;

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

        class transpose_ {
        public:
            transpose_() {}
            template <typename SUBNET> void setup(const SUBNET& /*sub*/) {}

            template <typename SUBNET> void forward(const SUBNET& sub, resizable_tensor& output) {
                auto& prev = sub.get_output();

                output.set_size(prev.num_samples(), prev.k(), prev.nc(), prev.nr());
                tt::transpose(false, output, prev);
            }

            template <typename SUBNET> void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/) {
                auto& prev = sub.get_gradient_input();
                tt::transpose(true, prev, gradient_input);
            }

            inline dpoint map_input_to_output(dpoint p) const
            {
                dpoint temp_p;
                temp_p.x() = p.y();
                temp_p.y() = p.x();
                return temp_p;
            }
            inline dpoint map_output_to_input(dpoint p) const
            {
                dpoint temp_p;
                temp_p.x() = p.y();
                temp_p.y() = p.x();
                return temp_p;
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const transpose_& /*item*/, std::ostream& out) {
                serialize("transpose_", out);
            }
            friend void deserialize(transpose_& /*item*/, std::istream& in) {
                std::string version;
                deserialize(version, in);
                if (version != "transpose_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::transpose_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const transpose_& /*item*/) {
                out << "transpose";
                return out;
            }
            friend void to_xml(const transpose_& /*item*/, std::ostream& out) {
                out << "<transpose />\n";
            }

        private:
            dlib::resizable_tensor params; // unused
        };

        template <typename SUBNET> using transpose = add_layer<transpose_, SUBNET>;

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

        class positional_encodings_ {
        public:
            positional_encodings_(unsigned long sequence_dim_ = 1, unsigned long embedding_dim_ = 1) :
                sequence_dim(sequence_dim_), embedding_dim(embedding_dim_)
            {
            }
            positional_encodings_(const positional_encodings_& item) :
                pe(item.pe), sequence_dim(item.sequence_dim), embedding_dim(item.embedding_dim)
            {
            }
            positional_encodings_& operator= (const positional_encodings_& item) {
                if (this == &item) return *this;
                pe = item.pe;
                sequence_dim = item.sequence_dim;
                embedding_dim = item.embedding_dim;
                return *this;
            }

            template <typename SUBNET>
            void setup(const SUBNET& sub)
            {
                auto& prev = sub.get_output();

                sequence_dim = prev.nr();
                embedding_dim = prev.nc();
                const unsigned long ns = prev.num_samples();
                const unsigned long nk = prev.k();
                const float n = 10000.0f;

                pe.set_size(ns, nk, sequence_dim, embedding_dim);
                for (unsigned long s = 0; s < ns; ++s)
                {
                    for (unsigned long k = 0; k < nk; ++k)
                    {
                        for (unsigned long r = 0; r < sequence_dim; ++r)
                        {
                            for (unsigned long c = 0; c < embedding_dim; ++c)
                            {
                                float theta = static_cast<float>(r) / std::pow(n, static_cast<float>(c) / embedding_dim);
                                if (c % 2 == 0) pe.host()[tensor_index(pe, s, k, r, c)] = std::sin(theta);
                                else pe.host()[tensor_index(pe, s, k, r, c)] = std::cos(theta);
                            }
                        }
                    }
                }
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                const auto& prev_output = sub.get_output();
                if (!have_same_dimensions(pe, prev_output)) setup(sub);

                output.set_size(prev_output.num_samples(), prev_output.k(), sequence_dim, embedding_dim);
                tt::add(output, prev_output, pe);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto& prev_grad = sub.get_gradient_input();
                tt::add(prev_grad, prev_grad, gradient_input);
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            const tensor& get_positional_encodings() const { return pe; }
            tensor& get_positional_encodings() { return pe; }

            friend void serialize(const positional_encodings_& /*item*/, std::ostream& out)
            {
                serialize("positional_encodings_", out);
            }
            friend void deserialize(positional_encodings_& /*item*/, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "positional_encodings_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::positional_encodings_.");
            }

            friend std::ostream& operator<<(std::ostream& out, const positional_encodings_& /*item*/)
            {
                out << "positional_encodings";
                return out;
            }
            friend void to_xml(const positional_encodings_& /*item*/, std::ostream& out)
            {
                out << "<positional_encodings />\n";
            }

        private:
            resizable_tensor params; // unused
            resizable_tensor pe;
            unsigned long sequence_dim, embedding_dim;
        };

        template <typename SUBNET>
        using positional_encodings = add_layer<positional_encodings_, SUBNET>;

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

        template<
            unsigned long num_embeddings_,
            unsigned long embedding_dim_
            >
        class embeddings_
        {
            static_assert(num_embeddings_ > 0, "The size of the embedding dictionary must be > 0");
            static_assert(embedding_dim_ > 0, "The size of each embedding vector must be > 0");

        public:
            embeddings_() : num_embeddings(num_embeddings_),
                embedding_dim(embedding_dim_),
                learning_rate_multiplier(1.0f),
                scale_by_freq(true)
            {
            }

            double get_learning_rate_multiplier() const { return learning_rate_multiplier; }
            void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }

            void set_scale_by_freq(bool val) { scale_by_freq = val; }
            bool get_scale_by_freq() const { return scale_by_freq; }

            unsigned long get_num_embeddings() const { return num_embeddings; }
            void set_num_embeddings(unsigned long num)
            {
                DLIB_CASSERT(num > 0);
                if (num != num_embeddings)
                {
                    DLIB_CASSERT(get_embeddings().size() == 0,
                        "It is not possible to change the size of the embedding dictionary if the parameter has already been assigned.");
                }
            }

            unsigned long get_embedding_dim() const { return embedding_dim; }
            void set_embedding_dim(unsigned long dim)
            {
                DLIB_CASSERT(dim > 0);
                if (dim != embedding_dim)
                {
                    DLIB_CASSERT(get_embeddings().size() == 0,
                        "It is not possible to change the size of the embedding dictionary if the parameter has already been assigned.");
                }
            }

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/)
            {
                embs.set_size(num_embeddings, embedding_dim);
                tt::tensor_rand rnd(std::rand());
                rnd.fill_gaussian(embs);
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                const auto& prev = sub.get_output();
                output.set_size(prev.num_samples(), prev.k(), prev.nr(), embedding_dim);

                tt::embeddings(output, prev, embs);
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                // Because this class is expected to be directly after an <input> layer,
                // it's not necessary to propagate the gradient.
                // Additionally, this layer is treated as constant during backpropagation,
                // so it technically doesn't contribute to the gradient computation.
                if (learning_rate_multiplier != 0)
                {
                    auto& prev_src = sub.get_output();

                    calc_token_freqs(prev_src, gradient_input);
                    tt::embeddings_gradient(prev_src, gradient_input, embs, freqs, learning_rate_multiplier, scale_by_freq);
                }
            }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            const tensor& get_embeddings() const { return embs; }
            tensor& get_embeddings() { return embs; }

            friend void serialize(const embeddings_& item, std::ostream& out)
            {
                serialize("embeddings_", out);
                serialize(item.embs, out);
                serialize(item.num_embeddings, out);
                serialize(item.embedding_dim, out);
                serialize(item.learning_rate_multiplier, out);
                serialize(item.scale_by_freq, out);
            }
            friend void deserialize(embeddings_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "embeddings_")
                    throw serialization_error("Unexpected version found while deserializing dlib::embeddings_.");
                deserialize(item.embs, in);
                deserialize(item.num_embeddings, in);
                deserialize(item.embedding_dim, in);
                deserialize(item.learning_rate_multiplier, in);
                deserialize(item.scale_by_freq, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const embeddings_& item)
            {
                out << "embeddings (num_embeddings=" << item.num_embeddings
                    << ", embedding_dim=" << item.embedding_dim
                    << ") learning_rate_mult=" << item.learning_rate_multiplier;
                return out;
            }
            friend void to_xml(const embeddings_& item, std::ostream& out)
            {
                out << "<embeddings num_embeddings='" << item.num_embeddings
                    << "' embedding_dim='" << item.embedding_dim
                    << "' learning_rate_mult='"
                    << item.learning_rate_multiplier << "'>\n";
                out << mat(item.embs);
                out << "</embeddings>\n";
            }

        private:
            void calc_token_freqs(const tensor& prev, const tensor& input) {
                if (freqs.size() == 0) freqs.set_size(num_embeddings, 1, 1, 1);
                freqs = 0;

                const float* prev_data = prev.host();
                float* freqs_data = freqs.host();
                for (long s = 0; s < input.num_samples(); ++s)
                {
                    for (long k = 0; k < input.k(); ++k)
                    {
                        for (long r = 0; r < input.nr(); ++r)
                        {
                            const unsigned long token_idx = static_cast<unsigned long>(prev_data[tensor_index(prev, s, k, r, 0)]);
                            if (token_idx < num_embeddings) freqs_data[tensor_index(freqs, token_idx, 0, 0, 0)]++;
                        }
                    }
                }
            }

            resizable_tensor params; // unused
            resizable_tensor embs, freqs;
            unsigned long num_embeddings, embedding_dim;
            double learning_rate_multiplier;
            bool scale_by_freq;
        };

        template <
            unsigned long nb_embeddings,
            unsigned long embedding_length,
            typename SUBNET
            >
        using embeddings = add_layer<embeddings_<nb_embeddings, embedding_length>, SUBNET>;

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

        struct neg_infinity_tag {};
        struct zero_tag {};

        template<typename T>
        struct is_special_value : std::false_type {};
        template<>
        struct is_special_value<neg_infinity_tag> : std::true_type {};
        template<>
        struct is_special_value<zero_tag> : std::true_type {};

        template<long diag_, typename tag_, long num_ = 0, long den_ = 1>
        class tril_
        {
        public:
            tril_(): diag(diag_), diag_value(compute_diag_value()) {}

            template <typename SUBNET>
            void setup(const SUBNET& /*sub*/)
            {
            }

            template <typename SUBNET>
            void forward(const SUBNET& sub, resizable_tensor& output)
            {
                auto& prev = sub.get_output();
                output.set_size(prev.num_samples(), prev.k(), prev.nr(), prev.nc());

                check_mask(prev);
                tt::multiply(false, output, prev, binary_mask);
                if (diag_value != 0.0f) tt::add(1, output, 1, output_mask);
            }
            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
            {
                auto& prev_grad = sub.get_gradient_input();
                tt::multiply(true, prev_grad, gradient_input, binary_mask);
            }

            inline dpoint map_input_to_output(const dpoint& p) const { return p; }
            inline dpoint map_output_to_input(const dpoint& p) const { return p; }

            const tensor& get_layer_params() const { return params; }
            tensor& get_layer_params() { return params; }

            friend void serialize(const tril_& item, std::ostream& out)
            {
                serialize("tril_", out);
                serialize(item.diag, out);
                serialize(item.diag_value, out);
            }
            friend void deserialize(tril_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "tril_")
                    throw serialization_error("Unexpected version '" + version + "' found while deserializing dlib::tril_.");
                deserialize(item.diag, in);
                deserialize(item.diag_value, in);
            }

            friend std::ostream& operator<<(std::ostream& out, const tril_& item)
            {
                out << "tril (diag=" << item.diag << ", diag_value=" << item.diag_value << ")";
                return out;
            }
            friend void to_xml(const tril_& item, std::ostream& out)
            {
                out << "<tril diag='" << item.diag << "' diag_value='" << item.diag_value << "'/>\n";
            }

        private:
            float compute_diag_value() const {
                if (std::is_same<tag_, neg_infinity_tag>::value)
                    return -std::numeric_limits<float>::infinity();
                else if (std::is_same<tag_, zero_tag>::value)
                    return 0.0f;
                else
                    return static_cast<float>(num_) / static_cast<float>(den_);
            }

            void check_mask(const tensor& t)
            {
                if (!have_same_dimensions(binary_mask, t)) {
                    binary_mask.copy_size(t);
                    binary_mask = 1;
                    if (diag_value != 0.0f) {
                        output_mask.copy_size(t);
                        output_mask = 0;
                    }
                    for (long s = 0; s < output_mask.num_samples(); ++s)
                    {
                        for (long k = 0; k < output_mask.k(); ++k)
                        {
                            for (long r = 0; r < output_mask.nr(); ++r)
                            {
                                for (long c = std::max(r + diag + 1, 0L); c < output_mask.nc(); ++c)
                                {
                                    if (diag_value != 0.0f) output_mask.host()[tensor_index(output_mask, s, k, r, c)] = diag_value;
                                    binary_mask.host()[tensor_index(binary_mask, s, k, r, c)] = 0;
                                }
                            }
                        }
                    }
                }
            }

            template <typename T>
            struct always_false : std::false_type {};

            resizable_tensor params; // unused
            resizable_tensor binary_mask, output_mask;
            long diag;
            float diag_value;
        };

        template <typename SUBNET>
        using tril = add_layer<tril_<0, zero_tag>, SUBNET>;

        template <typename SUBNET>
        using tril_mask = add_layer<tril_<0, neg_infinity_tag>, SUBNET>;

        template <long diag, long num, long den, typename SUBNET>
        using tril_diag = add_layer<tril_<diag, void, num, den>, SUBNET>;

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

    }

    #endif // DLIB_DNn_LAYERS_H_