add MarShall.Copy for Boolean when creating Tensor.

src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -55,6 +55,9 @@ namespace Tensorflow
             var nd1 = nd.ravel();
             switch (nd.dtype.Name)
             {
+                case "Boolean":
+                    Marshal.Copy(nd1.Data<byte>(), 0, dotHandle, nd.size);
+                    break;
                 case "Int16":
                     Marshal.Copy(nd1.Data<short>(), 0, dotHandle, nd.size);
                     break;

test/TensorFlowNET.Examples/Program.cs

@@ -64,6 +64,7 @@ namespace TensorFlowNET.Examples
             disabled.ForEach(x => Console.WriteLine($"{x} is Disabled!", Color.Tan));
             errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red));
 
+            Console.Write("Please [Enter] to quit.");
             Console.ReadLine();
         }
     }

test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs

@@ -61,7 +61,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             var meta_file = model_name + ".meta";
             tf.train.import_meta_graph(Path.Join("graph", meta_file));
             Console.WriteLine("\tDONE");
-            //sess.run(tf.global_variables_initializer()); // not necessary here, has already been done before meta graph export
+            // definitely necessary, otherwize will get the exception of "use uninitialized variable"
+            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
@@ -89,12 +90,13 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                 };
                 // original python:
                 //_, 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 result = sess.run(new Tensor[] { optimizer, global_step, loss }, train_feed_dict);
+                // exception here, loss value seems like a float[]
+                //loss_value = result[2];
                 var step = result[1];
+                if (step % 10 == 0)
+                    Console.WriteLine($"Step {step} loss: {result[2]}");
                 if (step % 100 == 0)
-                    Console.WriteLine($"Step {step} loss: {loss_value}");
-                if (step % 2000 == 0)
                 {
                     continue;
                     // # Test accuracy with validation data for each epoch.