dlib C++ Library - mlp_kernel_1.h

    // Copyright (C) 2007  Davis E. King (davisking@users.sourceforge.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_MLp_KERNEL_1_
    #define DLIB_MLp_KERNEL_1_

    #include "../algs.h"
    #include "../serialize.h"
    #include "../matrix.h"
    #include "../rand.h"
    #include "mlp_kernel_abstract.h"
    #include <ctime>
    #include <sstream>

    namespace dlib
    {

        class mlp_kernel_1 : noncopyable
        {
            /*!
                INITIAL VALUE
                    The network is initially initialized with random weights

                CONVENTION
                    - input_layer_nodes() == input_nodes
                    - first_hidden_layer_nodes() == first_hidden_nodes
                    - second_hidden_layer_nodes() == second_hidden_nodes
                    - output_layer_nodes() == output_nodes
                    - get_alpha == alpha
                    - get_momentum() == momentum

                    - if (second_hidden_nodes == 0) then
                        - for all i and j:
                            - w1(i,j) == the weight on the link from node i in the first hidden layer
                              to input node j
                            - w3(i,j) == the weight on the link from node i in the output layer
                              to first hidden layer node j
                        - for all i and j:
                            - w1m == the momentum terms for w1 from the previous update
                            - w3m == the momentum terms for w3 from the previous update
                    - else
                        - for all i and j:
                            - w1(i,j) == the weight on the link from node i in the first hidden layer
                              to input node j
                            - w2(i,j) == the weight on the link from node i in the second hidden layer
                              to first hidden layer node j
                            - w3(i,j) == the weight on the link from node i in the output layer
                              to second hidden layer node j
                        - for all i and j:
                            - w1m == the momentum terms for w1 from the previous update
                            - w2m == the momentum terms for w2 from the previous update
                            - w3m == the momentum terms for w3 from the previous update
            !*/

        public:

            mlp_kernel_1 (
                long nodes_in_input_layer,
                long nodes_in_first_hidden_layer,
                long nodes_in_second_hidden_layer = 0,
                long nodes_in_output_layer = 1,
                double alpha_ = 0.1,
                double momentum_ = 0.8
            ) :
                input_nodes(nodes_in_input_layer),
                first_hidden_nodes(nodes_in_first_hidden_layer),
                second_hidden_nodes(nodes_in_second_hidden_layer),
                output_nodes(nodes_in_output_layer),
                alpha(alpha_),
                momentum(momentum_)
            {

                // seed the random number generator
                std::ostringstream sout;
                sout << time(0);
                rand_nums.set_seed(sout.str());

                w1.set_size(first_hidden_nodes+1, input_nodes+1);
                w1m.set_size(first_hidden_nodes+1, input_nodes+1);
                z.set_size(input_nodes+1,1);

                if (second_hidden_nodes != 0)
                {
                    w2.set_size(second_hidden_nodes+1, first_hidden_nodes+1);
                    w3.set_size(output_nodes, second_hidden_nodes+1);

                    w2m.set_size(second_hidden_nodes+1, first_hidden_nodes+1);
                    w3m.set_size(output_nodes, second_hidden_nodes+1);
                }
                else
                {
                    w3.set_size(output_nodes, first_hidden_nodes+1);

                    w3m.set_size(output_nodes, first_hidden_nodes+1);
                }

                reset();
            }

            virtual ~mlp_kernel_1 (
            ) {}

            void reset (
            )
            {
                // randomize the weights for the first layer
                for (long r = 0; r < w1.nr(); ++r)
                    for (long c = 0; c < w1.nc(); ++c)
                        w1(r,c) = rand_nums.get_random_double();

                // randomize the weights for the second layer
                for (long r = 0; r < w2.nr(); ++r)
                    for (long c = 0; c < w2.nc(); ++c)
                        w2(r,c) = rand_nums.get_random_double();

                // randomize the weights for the third layer
                for (long r = 0; r < w3.nr(); ++r)
                    for (long c = 0; c < w3.nc(); ++c)
                        w3(r,c) = rand_nums.get_random_double();

                // zero all the momentum terms
                set_all_elements(w1m,0);
                set_all_elements(w2m,0);
                set_all_elements(w3m,0);
            }

            long input_layer_nodes (
            ) const { return input_nodes; }

            long first_hidden_layer_nodes (
            ) const { return first_hidden_nodes; }

            long second_hidden_layer_nodes (
            ) const { return second_hidden_nodes; }

            long output_layer_nodes (
            ) const { return output_nodes; }

            double get_alpha (
            ) const { return alpha; }

            double get_momentum (
            ) const { return momentum; }

            template <typename EXP>
            const matrix<double> operator() (
                const matrix_exp<EXP>& in
            ) const
            {
                for (long i = 0; i < in.nr(); ++i)
                    z(i) = in(i);
                // insert the bias
                z(z.nr()-1) = -1;

                tmp1 = sigmoid(w1*z);
                // insert the bias
                tmp1(tmp1.nr()-1) = -1;

                if (second_hidden_nodes == 0)
                {
                    return sigmoid(w3*tmp1);
                }
                else
                {
                    tmp2 = sigmoid(w2*tmp1);
                    // insert the bias
                    tmp2(tmp2.nr()-1) = -1;

                    return sigmoid(w3*tmp2);
                }
            }

            template <typename EXP1, typename EXP2>
            void train (
                const matrix_exp<EXP1>& example_in,
                const matrix_exp<EXP2>& example_out
            )
            {
                for (long i = 0; i < example_in.nr(); ++i)
                    z(i) = example_in(i);
                // insert the bias
                z(z.nr()-1) = -1;

                tmp1 = sigmoid(w1*z);
                // insert the bias
                tmp1(tmp1.nr()-1) = -1;

                if (second_hidden_nodes == 0)
                {
                    o = sigmoid(w3*tmp1);

                    // now compute the errors and propagate them backwards though the network
                    e3 = pointwise_multiply(example_out-o, uniform_matrix<double>(output_nodes,1,1.0)-o, o);
                    e1 = pointwise_multiply(tmp1, uniform_matrix<double>(first_hidden_nodes+1,1,1.0) - tmp1, trans(w3)*e3 );

                    // compute the new weight updates
                    w3m = alpha * e3*trans(tmp1) + w3m*momentum;
                    w1m = alpha * e1*trans(z)    + w1m*momentum;

                    // now update the weights
                    w1 += w1m;
                    w3 += w3m;
                }
                else
                {
                    tmp2 = sigmoid(w2*tmp1);
                    // insert the bias
                    tmp2(tmp2.nr()-1) = -1;

                    o = sigmoid(w3*tmp2);

                    // now compute the errors and propagate them backwards though the network
                    e3 = pointwise_multiply(example_out-o, uniform_matrix<double>(output_nodes,1,1.0)-o, o);
                    e2 = pointwise_multiply(tmp2, uniform_matrix<double>(second_hidden_nodes+1,1,1.0) - tmp2, trans(w3)*e3 );
                    e1 = pointwise_multiply(tmp1, uniform_matrix<double>(first_hidden_nodes+1,1,1.0) - tmp1, trans(w2)*e2 );

                    // compute the new weight updates
                    w3m = alpha * e3*trans(tmp2) + w3m*momentum;
                    w2m = alpha * e2*trans(tmp1) + w2m*momentum;
                    w1m = alpha * e1*trans(z)    + w1m*momentum;

                    // now update the weights
                    w1 += w1m;
                    w2 += w2m;
                    w3 += w3m;
                }
            }

            template <typename EXP>
            void train (
                const matrix_exp<EXP>& example_in,
                double example_out
            )
            {
                matrix<double,1,1> e_out;
                e_out(0) = example_out;
                train(example_in,e_out);
            }

            double get_average_change (
            ) const
            {
                // sum up all the weight changes
                double delta = sum(abs(w1m)) + sum(abs(w2m)) + sum(abs(w3m));

                // divide by the number of weights
                delta /=  w1m.nr()*w1m.nc() +
                    w2m.nr()*w2m.nc() +
                    w3m.nr()*w3m.nc();

                return delta;
            }

            void swap (
                mlp_kernel_1& item
            )
            {
                exchange(input_nodes, item.input_nodes);
                exchange(first_hidden_nodes, item.first_hidden_nodes);
                exchange(second_hidden_nodes, item.second_hidden_nodes);
                exchange(output_nodes, item.output_nodes);
                exchange(alpha, item.alpha);
                exchange(momentum, item.momentum);

                w1.swap(item.w1);
                w2.swap(item.w2);
                w3.swap(item.w3);

                w1m.swap(item.w1m);
                w2m.swap(item.w2m);
                w3m.swap(item.w3m);

                // even swap the temporary matrices because this may ultimately result in
                // fewer calls to new and delete.
                e1.swap(item.e1);
                e2.swap(item.e2);
                e3.swap(item.e3);
                z.swap(item.z);
                tmp1.swap(item.tmp1);
                tmp2.swap(item.tmp2);
                o.swap(item.o);
            }

            friend void serialize (
                const mlp_kernel_1& item,
                std::ostream& out
            )
            {
                try
                {
                    serialize(item.input_nodes, out);
                    serialize(item.first_hidden_nodes, out);
                    serialize(item.second_hidden_nodes, out);
                    serialize(item.output_nodes, out);
                    serialize(item.alpha, out);
                    serialize(item.momentum, out);

                    serialize(item.w1, out);
                    serialize(item.w2, out);
                    serialize(item.w3, out);

                    serialize(item.w1m, out);
                    serialize(item.w2m, out);
                    serialize(item.w3m, out);
                }
                catch (serialization_error& e)
                {
                    throw serialization_error(e.info + "\n   while serializing object of type mlp_kernel_1");
                }
            }

            friend void deserialize (
                mlp_kernel_1& item,
                std::istream& in
            )
            {
                try
                {
                    deserialize(item.input_nodes, in);
                    deserialize(item.first_hidden_nodes, in);
                    deserialize(item.second_hidden_nodes, in);
                    deserialize(item.output_nodes, in);
                    deserialize(item.alpha, in);
                    deserialize(item.momentum, in);

                    deserialize(item.w1, in);
                    deserialize(item.w2, in);
                    deserialize(item.w3, in);

                    deserialize(item.w1m, in);
                    deserialize(item.w2m, in);
                    deserialize(item.w3m, in);

                    item.z.set_size(item.input_nodes+1,1);
                }
                catch (serialization_error& e)
                {
                    // give item a reasonable value since the deserialization failed
                    mlp_kernel_1(1,1).swap(item);
                    throw serialization_error(e.info + "\n   while deserializing object of type mlp_kernel_1");
                }
            }

        private:

            long input_nodes;
            long first_hidden_nodes;
            long second_hidden_nodes;
            long output_nodes;
            double alpha;
            double momentum;

            matrix<double> w1;
            matrix<double> w2;
            matrix<double> w3;

            matrix<double> w1m;
            matrix<double> w2m;
            matrix<double> w3m;

            rand::float_1a rand_nums;

            // temporary storage
            mutable matrix<double> e1, e2, e3;
            mutable matrix<double> z, tmp1, tmp2, o;
        };

        inline void swap (
            mlp_kernel_1& a,
            mlp_kernel_1& b
        ) { a.swap(b); }

    }

    #endif // DLIB_MLp_KERNEL_1_