Made network smaller.

examples/dnn_metric_learning_on_images_ex.cpp

@@ -131,9 +131,9 @@ void load_mini_batch (
 
 // ----------------------------------------------------------------------------------------
 
-// The next page of code defines the ResNet-34 network.  It's basically copied
+// The next page of code defines a ResNet network.  It's basically copied
 // and pasted from the dnn_imagenet_ex.cpp example, except we replaced the loss
-// layer with loss_metric.
+// layer with loss_metric and make the network somewhat smaller.
 
 template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
 using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
@@ -152,36 +152,40 @@ template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,a
 
 // ----------------------------------------------------------------------------------------
 
-template <typename SUBNET> using level1 = res<512,res<512,res_down<512,SUBNET>>>;
-template <typename SUBNET> using level2 = res<256,res<256,res<256,res<256,res<256,res_down<256,SUBNET>>>>>>;
-template <typename SUBNET> using level3 = res<128,res<128,res<128,res_down<128,SUBNET>>>>;
-template <typename SUBNET> using level4 = res<64,res<64,res<64,SUBNET>>>;
+template <typename SUBNET> using level0 = res_down<256,SUBNET>;
+template <typename SUBNET> using level1 = res<256,res<256,res_down<256,SUBNET>>>;
+template <typename SUBNET> using level2 = res<128,res<128,res_down<128,SUBNET>>>;
+template <typename SUBNET> using level3 = res<64,res<64,res<64,res_down<64,SUBNET>>>>;
+template <typename SUBNET> using level4 = res<32,res<32,res<32,SUBNET>>>;
 
-template <typename SUBNET> using alevel1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
-template <typename SUBNET> using alevel2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>;
-template <typename SUBNET> using alevel3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>;
-template <typename SUBNET> using alevel4 = ares<64,ares<64,ares<64,SUBNET>>>;
+template <typename SUBNET> using alevel0 = ares_down<256,SUBNET>;
+template <typename SUBNET> using alevel1 = ares<256,ares<256,ares_down<256,SUBNET>>>;
+template <typename SUBNET> using alevel2 = ares<128,ares<128,ares_down<128,SUBNET>>>;
+template <typename SUBNET> using alevel3 = ares<64,ares<64,ares<64,ares_down<64,SUBNET>>>>;
+template <typename SUBNET> using alevel4 = ares<32,ares<32,ares<32,SUBNET>>>;
 
 
 // training network type
 using net_type = loss_metric<fc_no_bias<128,avg_pool_everything<
+                            level0<
                             level1<
                             level2<
                             level3<
                             level4<
-                            max_pool<3,3,2,2,relu<bn_con<con<64,7,7,2,2,
-                            input_rgb_image
-                            >>>>>>>>>>>;
+                            max_pool<3,3,2,2,relu<bn_con<con<32,7,7,2,2,
+                            input_rgb_image_sized<150>
+                            >>>>>>>>>>>>;
 
 // testing network type (replaced batch normalization with fixed affine transforms)
 using anet_type = loss_metric<fc_no_bias<128,avg_pool_everything<
+                            alevel0<
                             alevel1<
                             alevel2<
                             alevel3<
                             alevel4<
-                            max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
-                            input_rgb_image
-                            >>>>>>>>>>>;
+                            max_pool<3,3,2,2,relu<affine<con<32,7,7,2,2,
+                            input_rgb_image_sized<150>
+                            >>>>>>>>>>>>;
 
 // ----------------------------------------------------------------------------------------