2st stage

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -111,9 +111,11 @@ namespace Tensorflow
                             case "Double":
                                 full_values.Add(value.Data<double>()[0]);
                                 break;
-                            case "String":
-                                full_values.Add(value.Data<string>()[0]);
-                                break;
+                            /*case "String":
+                                full_values.Add(value.Data<byte>()[0]);
+                                break;*/
+                            default:
+                                throw new NotImplementedException($"build_results tensor_values[0] {tensor_values[0].dtype.Name}");
                         }
                     }
                     else

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -296,9 +296,9 @@ namespace Tensorflow
                 case "Double":
                     tensor_proto.DoubleVal.AddRange(proto_values.Data<double>());
                     break;
-                case "String":
+                /*case "String":
                     tensor_proto.StringVal.AddRange(proto_values.Data<string>().Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x.ToString())));
-                    break;
+                    break;*/
                 default:
                     throw new Exception("make_tensor_proto Not Implemented");
             }

src/TensorFlowNET.Core/Train/Saving/Saver.cs

@@ -170,7 +170,7 @@ namespace Tensorflow
         {
             if (string.IsNullOrEmpty(latest_filename))
                 latest_filename = "checkpoint";
-            string model_checkpoint_path = "";
+            object model_checkpoint_path = "";
             string checkpoint_file = "";
 
             if (global_step > 0)
@@ -188,10 +188,10 @@ namespace Tensorflow
 
                 if (write_state)
                 {
-                    _RecordLastCheckpoint(model_checkpoint_path);
+                    _RecordLastCheckpoint(model_checkpoint_path.ToString());
                     checkpoint_management.update_checkpoint_state_internal(
                         save_dir: save_path_parent,
-                        model_checkpoint_path: model_checkpoint_path,
+                        model_checkpoint_path: model_checkpoint_path.ToString(),
                         all_model_checkpoint_paths: _last_checkpoints.Keys.Select(x => x).ToList(),
                         latest_filename: latest_filename,
                         save_relative_paths: _save_relative_paths);
@@ -205,7 +205,7 @@ namespace Tensorflow
                 export_meta_graph(meta_graph_filename, strip_default_attrs: strip_default_attrs, save_debug_info: save_debug_info);
             }
 
-            return _is_empty ? string.Empty : model_checkpoint_path;
+            return _is_empty ? string.Empty : model_checkpoint_path.ToString();
         }
 
         public (Saver, object) import_meta_graph(string meta_graph_or_file, 

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -136,7 +136,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         public void Train(Session sess)
         {
             // Number of training iterations in each epoch
-            var num_tr_iter = y_train.len / batch_size;
+            var num_tr_iter = y_train.shape[0] / batch_size;
 
             var init = tf.global_variables_initializer();
             sess.run(init);

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -127,7 +127,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         public void Train(Session sess)
         {
             // Number of training iterations in each epoch
-            var num_tr_iter = mnist.train.labels.len / batch_size;
+            var num_tr_iter = mnist.train.labels.shape[0] / batch_size;
 
             var init = tf.global_variables_initializer();
             sess.run(init);

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -88,7 +88,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         public void Train(Session sess)
         {
             // Number of training iterations in each epoch
-            var num_tr_iter = y_train.len / batch_size;
+            var num_tr_iter = y_train.shape[0] / batch_size;
 
             var init = tf.global_variables_initializer();
             sess.run(init);

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -77,7 +77,7 @@ namespace TensorFlowNET.Examples
 
             var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
 
-            NDArray[] resultArr = results.Data<NDArray>();
+            NDArray[] resultArr = results.GetNDArrays();
 
             buildOutputImage(resultArr);
         }
@@ -119,14 +119,14 @@ namespace TensorFlowNET.Examples
             // get bitmap
             Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg"));
 
-            float[] scores = resultArr[2].Data<float>();
+            float[] scores = resultArr[2].GetData<float>().ToArray();
 
             for (int i=0; i<scores.Length; i++)
             {
                 float score = scores[i];
                 if (score > MIN_SCORE)
                 {
-                    float[] boxes = resultArr[1].Data<float>();
+                    float[] boxes = resultArr[1].GetData<float>().ToArray();
                     float top = boxes[i * 4] * bitmap.Height;
                     float left = boxes[i * 4 + 1] * bitmap.Width;
                     float bottom = boxes[i * 4 + 2] * bitmap.Height;
@@ -140,7 +140,7 @@ namespace TensorFlowNET.Examples
                         Height = (int)(bottom - top)
                     };
 
-                    float[] ids = resultArr[3].Data<float>();
+                    float[] ids = resultArr[3].GetData<float>().ToArray();
 
                     string name = pbTxtItems.items.Where(w => w.id == (int)ids[i]).Select(s=>s.display_name).FirstOrDefault();
 

test/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs

@@ -27,7 +27,7 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
 
-            Console.WriteLine($"Training entries: {train_data.len}, labels: {train_labels.len}");
+            Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}");
 
             // A dictionary mapping words to an integer index
             var word_index = GetWordIndex();

test/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs

@@ -147,8 +147,8 @@ namespace TensorFlowNET.Examples
             Console.WriteLine("\tDONE ");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
-            Console.WriteLine("Training set size: " + train_x.len);
-            Console.WriteLine("Test set size: " + valid_x.len);
+            Console.WriteLine("Training set size: " + train_x.shape[0]);
+            Console.WriteLine("Test set size: " + valid_x.shape[0]);
         }
 
         public Graph ImportGraph()

test/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs

@@ -120,7 +120,7 @@ namespace TensorFlowNET.Examples
         // Generate training batch for the skip-gram model
         private (NDArray, NDArray) next_batch(int batch_size, int num_skips, int skip_window)
         {
-            var batch = np.ndarray((batch_size), dtype: np.int32);
+            var batch = np.ndarray(new Shape(batch_size), dtype: np.int32);
             var labels = np.ndarray((batch_size, 1), dtype: np.int32);
             // get window size (words left and right + current one)
             int span = 2 * skip_window + 1;