Preparation fo optimization of convolution layer

.gitignore

@@ -1,2 +1,3 @@
 deep-network
 obj/*.o
+doc/*

src/main.cpp

@@ -8,27 +8,21 @@
 using namespace Layer;
 
 int main(int argc,char** argv){
-  Network N;
+  //Test of convolution layer
   size_t nf=4;
-  ConvolutionLayer L1(1,28,28,5,5,nf);
-  L1.init(0,1);
-  ActivationLayer<Sigmoid> L2(nf*24*24);
-  //Layer::Pooling L3(nf,24,24,2,2);
-  FullConnectedLayer L4(nf*24*24,10);
-  L4.init_standard();
-  ActivationLayer<Sigmoid> L5(10);
-  L1.name="[Convolutionnal]";
-  L2.name="[Sigmoid of convolutionnal]";
-  //L3.name="[Pooling]";
-  L4.name="[Full connected]";
-  L5.name="[Sigmoid of full]";
-
-  N.push_layer(L1);
-  N.push_layer(L2);
-  //    N.push_layer(&L3);
-  N.push_layer(L4);
-  N.push_layer(L5);
-  N.is_done();
+  ConvolutionLayer L(1,28,28,5,5,nf);
   Mnist dataset;
-  N.train(&dataset,1,10,0.1);
+  size_t N=dataset.get_train_size();
+  clock_t c_start = clock();
+  auto t_start = chrono::high_resolution_clock::now();
+  for(int j=0;j<10;++j){
+    for(size_t i=0;i<N;++i){
+      pair<Vector,Vector> data=dataset.get_train(i);
+      Vector x=data.first;
+      L.feed_forward(x);
+    }
+  }
+  auto t_end = chrono::high_resolution_clock::now();
+  cout<< "Duration: "<< chrono::duration<double, std::milli>(t_end-t_start).count()<<" ms\n";
+  
 }