# 91.

 C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526   Pub Date: 06/01/95

Another approach to breaking out of local minima as well as to enhance generalization ability is to introduce some noise in the inputs during training. A random number is added to each input component of the input vector as it is applied to the network. This is scaled by an overall noise factor, NF, which has a 0 to 1 range. You can add as much noise to the simulation as you want, or not any at all, by choosing NF = 0. When you are close to a solution and have reached a satisfactory minimum, you don’t want noise at that time to interfere with convergence to the minimum. We implement a noise factor that decreases with the number of cycles, as shown in the following excerpt from the backprop.cpp file.

` // update NF // gradually reduce noise to zero if (total_cycles>0.7*max_cycles)                      new_NF = 0; else if (total_cycles>0.5*max_cycles)                      new_NF = 0.25*NF; else if (total_cycles>0.3*max_cycles)                      new_NF = 0.50*NF; else if (total_cycles>0.1*max_cycles)                      new_NF = 0.75*NF; backp.set_NF(new_NF); `

The noise factor is reduced at regular intervals. The new noise factor is updated with the network class function called set_NF(float). There is a member variable in the network class called NF that holds the current value for the noise factor. The noise is added to the inputs in the input_layer member function calc_out().

Another reason for using noise is to prevent memorization by the network. You are effectively presenting a different input pattern with each cycle so it becomes hard for the network to memorize patterns.

### One Other Change—Starting Training from a Saved Weight File

Shortly, we will look at the complete listings for the backpropagation simulator. There is one other enhancement to discuss. It is often useful in long simulations to be able to start from a known point, which is from an already saved set of weights. This is a simple change in the backprop.cpp program, which is well worth the effort. As a side benefit, this feature will allow you to run a simulation with a large beta value for, say, 500 cycles, save the weights, and then start a new simulation with a smaller beta value for another 500 or more cycles. You can take preset breaks in long simulations, which you will encounter in Chapter 14. At this point, let’s look at the complete listings for the updated layer.h and layer.cpp files in Listings 13.1 and 13.2:

Listing 13.1 layer.h file updated to include noise and momentum

` // layer.h            V.Rao, H. Rao // header file for the layer class hierarchy and // the network class  // added noise and momentum #define MAX_LAYERS    5 #define MAX_VECTORS   100 class network; class Kohonen_network; class layer { protected:        int num_inputs;        int num_outputs;        float *outputs; // pointer to array of outputs        float *inputs;  // pointer to array of inputs, which                                       // are outputs of some other layer        friend network;        friend Kohonen_network;  // update for Kohonen model public:        virtual void calc_out()=0; }; class input_layer: public layer { private: float noise_factor; float * orig_outputs; public:        input_layer(int, int);        ~input_layer();        virtual void calc_out();        void set_NF(float);        friend network; }; class middle_layer; class output_layer:  public layer { protected:        float * weights;        float * output_errors; // array of errors at output        float * back_errors;   // array of errors back-propagated        float * expected_values;        // to inputs        float * cum_deltas;    // for momentum        float * past_deltas;   // for momentum        friend network; public:        output_layer(int, int);        ~output_layer();        virtual void calc_out();        void calc_error(float &);        void randomize_weights();        void update_weights(const float, const float);        void update_momentum();        void list_weights();        void write_weights(int, FILE *);        void read_weights(int, FILE *);        void list_errors();        void list_outputs(); }; class middle_layer:   public output_layer { private: public:     middle_layer(int, int);     ~middle_layer();        void calc_error(); }; class network { private: layer *layer_ptr[MAX_LAYERS];     int number_of_layers;     int layer_size[MAX_LAYERS];     float *buffer;     fpos_t position;     unsigned training; public:     network();     ~network();                void set_training(const unsigned &);                unsigned get_training_value();                void get_layer_info();                void set_up_network();                void randomize_weights();                void update_weights(const float, const float);                void update_momentum();                void write_weights(FILE *);                void read_weights(FILE *);                void list_weights();                void write_outputs(FILE *);                void list_outputs();                void list_errors();                void forward_prop();                void backward_prop(float &);                int fill_IObuffer(FILE *);                void set_up_pattern(int);                void set_NF(float); }; `

C++ Neural Networks and Fuzzy Logic
ISBN: 1558515526
EAN: 2147483647
Year: 1995
Pages: 139

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