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

    namespace dlib
    {

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

        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(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.");

            con_(
            ) :
                learning_rate_multiplier(1),
                weight_decay_multiplier(1),
                bias_learning_rate_multiplier(1),
                bias_weight_decay_multiplier(0),
                padding_y_(_padding_y),
                padding_x_(_padding_x)
            {}

            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_; }

            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 point map_input_to_output (
                point 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 point map_output_to_input (
                point 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),
                padding_y_(item.padding_y_),
                padding_x_(item.padding_x_)
            {
                // 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;
                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);

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

                filters = alias_tensor(_num_filters, sub.get_output().k(), _nr, _nc);
                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(output,
                    sub.get_output(),
                    filters(params,0),
                    _stride_y,
                    _stride_x,
                    padding_y_,
                    padding_x_
                    );

                tt::add(1,output,1,biases(params,filters.size()));
            }

            template <typename SUBNET>
            void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad)
            {
                conv.get_gradient_for_data (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 (gradient_input, sub.get_output(), filt);
                    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_4", out);
                serialize(item.params, out);
                serialize(_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);
            }

            friend void deserialize(con_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                long num_filters;
                long nr;
                long nc;
                int stride_y;
                int stride_x;
                if (version == "con_4")
                {
                    deserialize(item.params, in);
                    deserialize(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 (num_filters != _num_filters)
                    {
                        std::ostringstream sout;
                        sout << "Wrong num_filters found while deserializing dlib::con_" << std::endl;
                        sout << "expected " << _num_filters << " but found " << num_filters << std::endl;
                        throw serialization_error(sout.str());
                    }

                    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_");
                }
                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="<<_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;
                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 con_& item, std::ostream& out)
            {
                out << "<con"
                    << " num_filters='"<<_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<<"'>\n";
                out << mat(item.params);
                out << "</con>";
            }

        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;

            // 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_;

        };

        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 _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 point map_input_to_output (
                point 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 point map_output_to_input (
                point 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 point map_input_to_output (
                point 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 point map_output_to_input (
                point 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>;

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

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

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

        namespace impl
        {
            class visitor_bn_running_stats_window_size
            {
            public:

                visitor_bn_running_stats_window_size(unsigned long new_window_size_) : new_window_size(new_window_size_) {}

                template <typename T>
                void set_window_size(T&) const
                {
                    // ignore other layer detail types
                }

                template < layer_mode mode >
                void set_window_size(bn_<mode>& l) const
                {
                    l.set_running_stats_window_size(new_window_size);
                }

                template<typename input_layer_type>
                void operator()(size_t , input_layer_type& )  const
                {
                    // ignore other layers
                }

                template <typename T, typename U, typename E>
                void operator()(size_t , add_layer<T,U,E>& l)  const
                {
                    set_window_size(l.layer_details());
                }

            private:

                unsigned long new_window_size;
            };
        }

        template <typename net_type>
        void set_all_bn_running_stats_window_sizes (
            net_type& net,
            unsigned long new_window_size
        )
        {
            visit_layers(net, impl::visitor_bn_running_stats_window_size(new_window_size));
        }

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

        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)
            {}

            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; }

            unsigned long get_num_outputs (
            ) const { return num_outputs; }

            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)
                    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)
                {
                    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)
            {
                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)
                {
                    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)
                    {
                        // 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_2", 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);
            }

            friend void deserialize(fc_& item, std::istream& in)
            {
                std::string version;
                deserialize(version, in);
                if (version != "fc_2")
                    throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::fc_.");

                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);
            }

            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;
                    out << " bias_learning_rate_mult="<<item.bias_learning_rate_multiplier;
                    out << " bias_weight_decay_mult="<<item.bias_weight_decay_multiplier;
                }
                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<<"'";
                    out << ">\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;
        };

        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>;

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

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

            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>;

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

        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 point map_input_to_output (const point& p) const { return p; }
            inline point map_output_to_input (const point& 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_multiply(mat(sg), 1.0f/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; }

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

            void forward_inplace(const tensor& input, tensor& output)
            {
                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*/
            )
            {
                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);
                }
            }

            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_", out);
                serialize(item.params, out);
                serialize(item.gamma, out);
                serialize(item.beta, out);
                serialize((int)item.mode, 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_")
                    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;
            }

            friend std::ostream& operator<<(std::ostream& out, const affine_& )
            {
                out << "affine";
                return out;
            }

            friend void to_xml(const affine_& item, std::ostream& out)
            {
                out << "<affine";
                if (item.mode==CONV_MODE)
                    out << " mode='conv'";
                else
                    out << " mode='fc'";
                out << ">\n";
                out << mat(item.params);
                out << "</affine>\n";
            }

        private:
            resizable_tensor params, empty_params;
            alias_tensor gamma, beta;
            layer_mode mode;
        };

        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; }

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

            friend void deserialize(add_prev_& , 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>;

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

        class relu_
        {
        public:
            relu_()
            {
            }

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

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

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

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

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

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

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

            friend std::ostream& operator<<(std::ostream& out, const relu_& )
            {
                out << "relu";
                return out;
            }

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

        private:
            resizable_tensor params;
        };

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

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

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

            friend void deserialize(sig_& , 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_& )
            {
                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 htan_
        {
        public:
            htan_()
            {
            }

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

            inline point map_input_to_output (const point& p) const { return p; }
            inline point map_output_to_input (const point& 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_& , std::ostream& out)
            {
                serialize("htan_", out);
            }

            friend void deserialize(htan_& , 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_& )
            {
                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 softmax_
        {
        public:
            softmax_()
            {
            }

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

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

            void backward_inplace(
                const tensor& computed_output,
                const tensor& gradient_input,
                tensor& data_grad,
                tensor&
            )
            {
                tt::softmax_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_& , std::ostream& out)
            {
                serialize("softmax_", out);
            }

            friend void deserialize(softmax_& , 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_& )
            {
                out << "softmax";
                return out;
            }

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

        private:
            resizable_tensor params;
        };

        template <typename SUBNET>
        using softmax = add_layer<softmax_, 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(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(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(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(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);
            }

            point map_input_to_output(point p) const;
            point map_output_to_input(point p) const;

            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>>>>>>>>>>>>>>>>;
    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_DNn_LAYERS_H_