Updating ANN errors backpropagate function
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parent
f7193b3e33
commit
77be210265
3 changed files with 58 additions and 5 deletions
6
main.cpp
6
main.cpp
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@ -16,8 +16,8 @@ int main(int argc, char *argv[])
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cout << "Bonjour et bienvenu" << endl;
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Network network(2, 5);
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network.forward({1.0,1.0,1.0,1.0,1.0}, {1.0,1.0,1.0,1.0,1.0});
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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.print();
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/*Neuron n(3), n1(1), n2(1), n3(1);
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@ -37,6 +37,6 @@ int main(int argc, char *argv[])
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{
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cout << *it << endl;
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}*/
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return 0;
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}
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@ -44,6 +44,30 @@ float Neuron::get_derror()
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return derror;
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}
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void Neuron::set_nth_weight(int n, float value)
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{
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int i=1;
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forward_list<float>::iterator current_weight(weights.begin());
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while(i<n)
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{
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current_weight++;
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i++;
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}
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*current_weight = value;
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}
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float Neuron::get_nth_weight(int n)
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{
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int i=1;
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forward_list<float>::iterator current_weight(weights.begin());
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while(i<n)
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{
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current_weight++;
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i++;
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}
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return *current_weight;
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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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@ -139,6 +163,7 @@ bool Network::forward(const std::vector<float> &input, const std::vector<float>
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}
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}
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}
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set_errors(target);
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return true;
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}
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@ -160,13 +185,16 @@ bool Network::set_errors(const std::vector<float> &target)
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{
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list<forward_list<Neuron>>::reverse_iterator temp_next_layer = current_layer; //temp_next_layer set at current layer
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temp_next_layer--; //temp_next_layer set now at next 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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current_neuron->set_derror(0.0);
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for(forward_list<Neuron>::iterator next_layer_current_neuron(temp_next_layer->begin()) ; next_layer_current_neuron!=temp_next_layer->end() ; ++next_layer_current_neuron)
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{
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//
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current_neuron->set_derror( current_neuron->get_derror()+next_layer_current_neuron->get_derror()*next_layer_current_neuron->get_nth_weight(neuron_counter) );
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}
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current_neuron->set_derror( current_neuron->get_derror()*Tools::activation_function_derivative(h_activ,current_neuron->get_weighted_sum()) );
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}
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}
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}
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@ -175,6 +203,26 @@ bool Network::set_errors(const std::vector<float> &target)
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bool Network::backward(float learning_rate)
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{
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int layer_counter = layers.size()+1;
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for(list<forward_list<Neuron>>::reverse_iterator current_layer(layers.rbegin()) ; current_layer!=layers.rend() ; ++current_layer)
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{//inside current layer
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layer_counter--;
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if(layer_counter>1) //all layers except input layer
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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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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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}
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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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@ -221,12 +269,14 @@ void Network::print()
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cout << (">> Output layer\n");
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cout << "size : " << current_layer_size << endl;
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cout << ("neurons' activations : ");
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for(forward_list<Neuron>::iterator it2(it1->begin()) ; it2!=it1->end() ; ++it2){cout << it2->get_activated_output() << " ";}
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//for(forward_list<Neuron>::iterator it2(it1->begin()) ; it2!=it1->end() ; ++it2){cout << it2->get_activated_output() << " ";}
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for(forward_list<Neuron>::iterator it2(it1->begin()) ; it2!=it1->end() ; ++it2){cout << it2->get_activated_output() << " " << it2->get_derror() << endl; for(int i=1;i<=3;i++){cout << it2->get_nth_weight(i) << " ";}cout<<endl;}//to be deleted
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cout << endl;
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}else
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{
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cout << ">> Hidden layer " << layer_counter-1 << endl;
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cout << "size : " << current_layer_size << endl;
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for(forward_list<Neuron>::iterator it2(it1->begin()) ; it2!=it1->end() ; ++it2){cout << it2->get_activated_output() << " " << it2->get_derror() << endl;}//to be deleted
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}
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cout << "------------------------------------------------" << endl;
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}
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@ -21,6 +21,9 @@ public:
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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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