Updating functions

main.cpp

@@ -14,11 +14,8 @@ int main(int argc, char *argv[])
 
 
     cout << "Bonjour et bienvenu" << endl;
-    vector<int> v = {1,2,3,4};
-    cout << "size = " << v.size() << endl;
-    cout << "size of bool = " << sizeof(bool) << endl;
 
-    Neuron n0(3,SIGMOID);
+    /*Neuron n0(3,SIGMOID);
 
     Neuron n1(3,RELU);n1.set_output(1.0);
     Neuron n2(3,RELU);n2.set_output(2.0);
@@ -28,6 +25,7 @@ int main(int argc, char *argv[])
     forward_list<Neuron>::iterator it(fl.begin());
 
     n0.activate(it);
-    cout << "is = " << n0.get_output() << endl;
+    cout << "is = " << n0.get_output() << endl;*/
+    Network(4, 5);
     return 0;
 }

myclasses.cpp

@@ -57,6 +57,108 @@ float Neuron::get_output()//to be deleted later
     return output;
 }
 
+Network::Network(int n_layers, int n_neurons)
+{
+    for(int i(1) ; i<=n_layers ; i++)
+    {
+        forward_list<Neuron> current_layer;
+        for(int j(1) ; j<=n_neurons ; j++)
+        {
+            if(i==1)
+            {
+                current_layer.push_front( Neuron(0, LINEAR) );
+            }else if(i==n_layers)
+            {
+                current_layer.push_front( Neuron(n_neurons, SIGMOID) );
+            }else
+            {
+                current_layer.push_front( Neuron(n_neurons, RELU) );
+            }
+        }
+        layers.push_back(current_layer);
+    }
+    h_activ = RELU;
+    o_activ = SIGMOID;
+}
+
+Network::Network(const std::vector<int> &n_neurons, Activ h_activ, Activ o_activ)
+{
+    for(int i(0) ; i<n_neurons.size() ; i++)
+    {
+        forward_list<Neuron> current_layer;
+        for(int j(1) ; j<=n_neurons[i] ; j++)
+        {
+            if(i==0)
+            {
+                current_layer.push_front( Neuron(0, LINEAR) );
+            }else if(i==n_neurons.size()-1)
+            {
+                current_layer.push_front( Neuron(n_neurons[i-1], o_activ) );
+            }else
+            {
+                current_layer.push_front( Neuron(n_neurons[i-1], h_activ) );
+            }
+        }
+        layers.push_back(current_layer);
+    }
+    h_activ = h_activ;
+    o_activ = o_activ;
+}
+
+void Network::print()
+{
+    cout << endl << "#>>==========================================<<#" << endl;
+    cout <<         "#                 NEURAL NETWORK               #" << endl;
+    cout <<         "#>>==========================================<<#" << endl;
+    cout << ">> Number of layers : " << layers.size() << endl;
+    cout << "------------------------------------------------" << endl;
+    for(list<forward_list<Neuron>>::iterator it1(layers.begin()) ; it1!=layers.end() ; ++it1)
+    {
+        int current_layer_size = 0;
+        for(forward_list<Neuron>::iterator it2(it1) ; it2!=it1.end() ; ++it2)
+        {
+            current_layer_size++;
+        }
+        if(i==0)
+        {
+            cout << ">> Input layer" << endl;
+            cout << "size : " << layers << endl;
+            cout << "neurons' outputs : ";
+            temp = network->layers_first_neurons[i];
+            while(temp != NULL)
+            {
+                cout << ("%f ", temp->output);
+                temp = temp->same_layer_next_neuron;
+            }
+            cout << ("\n");
+        }else if(i==layers.size()-1)
+        {
+            cout << (">> Output layer\n");
+            cout << ("size : %d\n", network->neurons_per_layer[i]);
+            cout << ("neurons' outputs : ");
+            temp = network->layers_first_neurons[i];
+            while(temp != NULL)
+            {
+                cout << ("%f ", temp->output);
+                temp = temp->same_layer_next_neuron;
+            }
+            cout << ("\n");
+        }else
+        {
+            cout << (">> Hidden layer %d\n", i);
+            cout << ("size : %d\n", network->neurons_per_layer[i]);
+        }
+        cout << ("------------------------------------------------\n");
+    }
+    cout << ("Number of parameters : ");
+    for(i=1 ; i<network->n_layers ; i++)
+    {
+        n_params += network->neurons_per_layer[i] * (network->neurons_per_layer[i-1] + 1);
+    }
+    cout << ("%d\n", n_params);
+    cout << "#>>==========================================<<#" << endl << endl;
+}
+
 void Tools::activate_randomness()
 {
     srand(time(NULL));

myclasses.h

@@ -30,8 +30,9 @@ private:
 class Network
 {
 public:
-    Network(int n_neurons);
+    Network(int n_layers, int n_neurons);
     Network(const std::vector<int> &n_neurons, Activ h_activ=RELU, Activ o_activ=SIGMOID);
+    void print() const;
     bool forward(const std::vector<float> &input, const std::vector<float> &target);
     bool backward();
 private: