@@ -43,7 +43,8 @@ public Optimizer AdamOptimizer(Tensor learning_rate, string name = "Adam")
             public ExponentialMovingAverage ExponentialMovingAverage(float decay)
                 => new ExponentialMovingAverage(decay);
 
-            public Saver Saver(VariableV1[] var_list = null) => new Saver(var_list: var_list);
+            public Saver Saver(VariableV1[] var_list = null, int max_to_keep = 5) 
+                => new Saver(var_list: var_list, max_to_keep: max_to_keep);
 
             public string write_graph(Graph graph, string logdir, string name, bool as_text = true) 
                 => graph_io.write_graph(graph, logdir, name, as_text);
 
@@ -108,10 +108,7 @@ public static Tensor[] _GradientsHelper(Tensor[] ys,
                     {
                         // generate gradient subgraph for op.
                         var op = queue.Dequeue();
-                        if(tf.get_default_graph()._nodes_by_name.Count >= 23868)
-                        {
 
-                        }
                         _maybe_colocate_with(op, gradient_uid, colocate_gradients_with_ops);
                         //if (loop_state != null)
                         //loop_state.EnterGradWhileContext(op, before: true);
 
@@ -54,6 +54,8 @@ public class Main : IExample
         List<RefVariable> first_stage_trainable_var_list;
         Operation train_op_with_frozen_variables;
         Operation train_op_with_all_variables;
+        Saver loader;
+        Saver saver;
         #endregion
 
         public bool Run()
@@ -74,7 +76,9 @@ public bool Run()
 
         public void Train(Session sess)
         {
-
+            sess.run(tf.global_variables_initializer());
+            print($"=> Restoring weights from: {cfg.TRAIN.INITIAL_WEIGHT} ... ");
+            loader.restore(sess, cfg.TRAIN.INITIAL_WEIGHT);
         }
 
         public void Test(Session sess)
@@ -184,6 +188,21 @@ public Graph BuildGraph()
                 });
             });
 
+            tf_with(tf.name_scope("loader_and_saver"), delegate
+            {
+                loader = tf.train.Saver(net_var);
+                saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10);
+            });
+
+            tf_with(tf.name_scope("summary"), delegate
+            {
+                tf.summary.scalar("learn_rate", learn_rate);
+                tf.summary.scalar("giou_loss", giou_loss);
+                tf.summary.scalar("conf_loss", conf_loss);
+                tf.summary.scalar("prob_loss", prob_loss);
+                tf.summary.scalar("total_loss", loss);
+            });
+
             return graph;
         }
 
 
@@ -60,7 +60,7 @@ public class TrainConfig
             public TrainConfig(string root)
             {
                 _root = root;
-                INITIAL_WEIGHT = Path.Combine(_root, "data", "checkpoint", "yolov3_coco_demo.ckpt");
+                INITIAL_WEIGHT = Path.Combine(_root, "checkpoint", "yolov3_coco_demo.ckpt");
                 ANNOT_PATH = Path.Combine(_root, "data", "dataset", "voc_train.txt");
             }
         }
Original file line number	Diff line number	Diff line change
@@ -60,7 +60,7 @@ public class TrainConfig
60	60	public TrainConfig(string root)
61	61	{
62	62	_root = root;
63		- INITIAL_WEIGHT = Path.Combine(_root, "data", "checkpoint", "yolov3_coco_demo.ckpt");
	63	+ INITIAL_WEIGHT = Path.Combine(_root, "checkpoint", "yolov3_coco_demo.ckpt");
64	64	ANNOT_PATH = Path.Combine(_root, "data", "dataset", "voc_train.txt");
65	65	}
66	66	}