diff --git a/graph/vd_cnn.meta b/graph/vd_cnn.meta
index 1c676f52..b857fc6c 100644
Binary files a/graph/vd_cnn.meta and b/graph/vd_cnn.meta differ
diff --git a/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs b/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
index d9877d8c..891ad81f 100644
--- a/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
+++ b/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
@@ -52,38 +52,37 @@ namespace TensorFlowNET.Examples.CnnTextClassification
 
         protected virtual bool RunWithImportedGraph(Session sess, Graph graph)
         {
+            var stopwatch = Stopwatch.StartNew();
             Console.WriteLine("Building dataset...");
             var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit=null);
-            Console.WriteLine("\tDONE");
+            Console.WriteLine("\tDONE ");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
 
             Console.WriteLine("Import graph...");
             var meta_file = model_name + ".meta";
             tf.train.import_meta_graph(Path.Join("graph", meta_file));
-            Console.WriteLine("\tDONE");
-            // definitely necessary, otherwize will get the exception of "use uninitialized variable"
+            Console.WriteLine("\tDONE " + stopwatch.Elapsed);
+
             sess.run(tf.global_variables_initializer());
 
             var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
-            var num_batches_per_epoch = (len(train_x) - 1); // BATCH_SIZE + 1
+            var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1;
             double max_accuracy = 0;
 
             Tensor is_training = graph.get_operation_by_name("is_training");
             Tensor model_x = graph.get_operation_by_name("x");
             Tensor model_y = graph.get_operation_by_name("y");
-            Tensor loss = graph.get_operation_by_name("loss/loss");
+            Tensor loss = graph.get_operation_by_name("loss/value");
             //var optimizer_nodes = graph._nodes_by_name.Keys.Where(key => key.Contains("optimizer")).ToArray();
             Tensor optimizer = graph.get_operation_by_name("loss/optimizer");
             Tensor global_step = graph.get_operation_by_name("global_step");
-            Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy");
-            var stopwatch = Stopwatch.StartNew();
+            Tensor accuracy = graph.get_operation_by_name("accuracy/value");
+            stopwatch = Stopwatch.StartNew();
             int i = 0;
             foreach (var (x_batch, y_batch, total) in train_batches)
             {
                 i++;
-                var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
-                Console.WriteLine($"Training on batch {i}/{total}. Estimated training time: {estimate}");
                 var train_feed_dict = new Hashtable
                 {
                     [model_x] = x_batch,
@@ -94,9 +93,14 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                 //_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
                 var result = sess.run(new ITensorOrOperation[] { optimizer, global_step, loss }, train_feed_dict);
                 //loss_value = result[2];
-                var step = result[1];
+                var step = result[1];
                 if (step % 10 == 0)
+                {
+                    var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
+                    Console.WriteLine($"Training on batch {i}/{total}. Estimated training time: {estimate}");
                     Console.WriteLine($"Step {step} loss: {result[2]}");
+                }
+
                 if (step % 100 == 0)
                 {
                     continue;
@@ -198,6 +202,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             {
                 // download graph meta data
                 var meta_file = model_name + ".meta";
+                if (File.GetLastWriteTime(meta_file) < new DateTime(2019,05,11)) // delete old cached file which contains errors
+                    File.Delete(meta_file);
                 url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
                 Web.Download(url, "graph", meta_file);
             }