Updating predict function to output wether raw output layer maximum number or class
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chabisik
parent
77be210265
commit
cf48d29892
3 changed files with 97 additions and 50 deletions
29
main.cpp
29
main.cpp
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@ -16,27 +16,18 @@ int main(int argc, char *argv[])
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cout << "Bonjour et bienvenu" << endl;
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Network network(3, 3);
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network.forward({1.0,1.0,1.0}, {1.0,2.0,3.0});
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Network network(15, 3);
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network.print();
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/*Neuron n(3), n1(1), n2(1), n3(1);
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forward_list<Neuron> fl;
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fl.push_front(n1);
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fl.push_front(n2);
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fl.push_front(n3);
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forward_list<Neuron>::iterator it(fl.begin());
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n.activate(it, LINEAR);
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cout << "weighted sum = " << n.get_weighted_sum() << endl;*/
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/*list<float> l;
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l.push_back(1.0);
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l.push_back(2.0);
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l.push_back(3.0);
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for(list<float>::reverse_iterator it(l.rbegin()) ; it!=l.rend() ; ++it)
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cout << endl << endl;
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for(int episode=1;episode<=100000;episode++)
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{
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cout << *it << endl;
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}*/
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network.forward({1.0,1.0,1.0}, {1.0,2.0,3.0});
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network.backward(0.001);
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}
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//network.print();
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cout << endl << endl;
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network.print();
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cout << "verdict : " << network.predict({1.0,1.0,1.0},false) << endl;
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return 0;
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}
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104
myclasses.cpp
104
myclasses.cpp
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@ -2,6 +2,7 @@
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#include <ctime>
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#include <cmath>
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#include <forward_list>
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#include <algorithm>
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#include "myclasses.h"
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using namespace std;
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@ -19,29 +20,14 @@ Neuron::Neuron(int prev_layer_size)
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derror = 0.0;
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}
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void Neuron::set_activated_output(float value)
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void Neuron::set_bias(float value)
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{
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activated_output = value;
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bias = value;
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}
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float Neuron::get_weighted_sum()
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float Neuron::get_bias()
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{
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return weighted_sum;
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}
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float Neuron::get_activated_output()
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{
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return activated_output;
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}
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void Neuron::set_derror(float value)
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{
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derror = value;
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}
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float Neuron::get_derror()
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{
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return derror;
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return bias;
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}
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void Neuron::set_nth_weight(int n, float value)
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@ -68,6 +54,31 @@ float Neuron::get_nth_weight(int n)
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return *current_weight;
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}
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float Neuron::get_weighted_sum()
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{
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return weighted_sum;
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}
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void Neuron::set_activated_output(float value)
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{
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activated_output = value;
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}
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float Neuron::get_activated_output()
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{
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return activated_output;
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}
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void Neuron::set_derror(float value)
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{
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derror = value;
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}
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float Neuron::get_derror()
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{
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return derror;
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}
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void Neuron::activate(forward_list<Neuron>::iterator &prev_layer_it, Activ activ_function)
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{
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weighted_sum = bias;
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@ -211,24 +222,67 @@ bool Network::backward(float learning_rate)
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{
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list<forward_list<Neuron>>::reverse_iterator temp_prev_layer = current_layer; //temp_prev_layer set at current layer
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temp_prev_layer++; //temp_prev_layer set now at previous layer
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int neuron_counter=0;
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for(forward_list<Neuron>::iterator current_neuron(current_layer->begin()) ; current_neuron!=current_layer->end() ; ++current_neuron)
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{//inside current neuron
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neuron_counter++;
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int neuron_counter=0;
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for(forward_list<Neuron>::iterator prev_layer_current_neuron(temp_prev_layer->begin()) ; prev_layer_current_neuron!=temp_prev_layer->end() ; ++prev_layer_current_neuron)
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{
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//current_neuron->set_nth_weight()
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current_neuron->set_derror( current_neuron->get_derror()+prev_layer_current_neuron->get_derror()*prev_layer_current_neuron->get_nth_weight(neuron_counter) );
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neuron_counter++;
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current_neuron->set_nth_weight( neuron_counter, current_neuron->get_nth_weight(neuron_counter)-learning_rate*current_neuron->get_derror()*prev_layer_current_neuron->get_activated_output() );
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}
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current_neuron->set_bias( current_neuron->get_bias()-learning_rate*current_neuron->get_derror() );
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}
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}
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}
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return true;
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}
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float Network::predict(const std::vector<float> &input)
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bool neuron_cmp(Neuron a, Neuron b){return a.get_activated_output()<b.get_activated_output();}
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float Network::predict(const std::vector<float> &input, bool as_raw)
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{
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return 0.0;
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int layer_counter = 0;
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for(list<forward_list<Neuron>>::iterator current_layer(layers.begin()) ; current_layer!=layers.end() ; ++current_layer)
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{//inside current layer
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layer_counter++;
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if(layer_counter==1)
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{
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int i=0;
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for(forward_list<Neuron>::iterator current_neuron(current_layer->begin()) ; current_neuron!=current_layer->end() ; ++current_neuron)
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{//inside current neuron
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current_neuron->set_activated_output( input.at(i) );
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i++;
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}
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}else if(layer_counter==layers.size())
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{
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list<forward_list<Neuron>>::iterator temp = current_layer;
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temp--; //previous layer
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for(forward_list<Neuron>::iterator current_neuron(current_layer->begin()) ; current_neuron!=current_layer->end() ; ++current_neuron)
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{//inside current neuron
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forward_list<Neuron>::iterator prev_layer_it(temp->begin());
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current_neuron->activate(prev_layer_it, o_activ);
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}
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}else
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{
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list<forward_list<Neuron>>::iterator temp_prev_layer = current_layer; //temp_prev_layer set at current layer
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temp_prev_layer--; ////temp_prev_layer set now at previous layer
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for(forward_list<Neuron>::iterator current_neuron(current_layer->begin()) ; current_neuron!=current_layer->end() ; ++current_neuron)
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{//inside current neuron
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forward_list<Neuron>::iterator prev_layer_it(temp_prev_layer->begin());
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current_neuron->activate(prev_layer_it, h_activ);
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}
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}
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}
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list<forward_list<Neuron>>::iterator output_layer = layers.end(); output_layer--;
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if(as_raw)
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{
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return max_element(output_layer->begin(), output_layer->end(), neuron_cmp)->get_activated_output();
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}else
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{
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return distance( output_layer->begin(), max_element(output_layer->begin(),output_layer->end(),neuron_cmp) );
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}
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}
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void Network::print()
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14
myclasses.h
14
myclasses.h
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@ -15,15 +15,15 @@ class Neuron
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{
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public:
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Neuron(int prev_layer_size); //prev_layer_size = number of weights
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//void set_weighted_sum(float weighted_sum);
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void set_bias(float value);
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float get_bias();
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void set_nth_weight(int n, float value);
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float get_nth_weight(int n);
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float get_weighted_sum();
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void set_activated_output(float value);
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float get_activated_output();
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void set_derror(float value);
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float get_derror();
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void set_nth_weight(int n, float value);
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float get_nth_weight(int n);
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//std::forward_list<float> &get_weights();
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void activate(std::forward_list<Neuron>::iterator &prev_layer_it, Activ activ_function=LINEAR);
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private:
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std::forward_list<float> weights;
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@ -40,12 +40,14 @@ public:
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Network(int n_layers, int n_neurons);
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Network(const std::vector<int> &n_neurons, Activ h_activ=RELU, Activ o_activ=SIGMOID);
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float predict(const std::vector<float> &input);
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float predict(const std::vector<float> &input, bool as_raw=true);
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void print();
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//to be deleted
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bool forward(const std::vector<float> &input, const std::vector<float> &target);
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bool set_errors(const std::vector<float> &target);
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bool backward(float learning_rate);
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private:
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std::list<std::forward_list<Neuron>> layers;
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Activ h_activ;
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@ -53,7 +55,7 @@ private:
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//bool forward(const std::vector<float> &input, const std::vector<float> &target);
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//bool set_errors(const std::vector<float> &target);
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bool backward(float learning_rate);
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//bool backward(float learning_rate);
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};
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