Logistic Regression can run but has accuracy and performance.

src/TensorFlowHub/MnistDataSet.cs

@@ -24,7 +24,7 @@ namespace Tensorflow.Hub
             images = np.multiply(images, 1.0f / 255.0f);
             Data = images;
 
-            labels.astype(dataType);
+            labels = labels.astype(dataType);
             Labels = labels;
         }
 

src/TensorFlowNET.Core/Sessions/_FetchMapper.cs

@@ -15,7 +15,9 @@
 ******************************************************************************/
 
 using NumSharp;
+using System;
 using System.Collections.Generic;
+using System.Linq;
 
 namespace Tensorflow
 {
@@ -37,10 +39,21 @@ namespace Tensorflow
 
         public virtual NDArray build_results(List<NDArray> values)
         {
-            var type = values[0].GetType();
-            var nd = new NDArray(type, values.Count);
-            nd.ReplaceData(values.ToArray());
-            return nd;
+            // if they're all scalar value
+            bool isAllScalars = values.Count(x => x.ndim == 0) == values.Count;
+            if (isAllScalars)
+            {
+                var type = values[0].dtype;
+                switch(Type.GetTypeCode(type))
+                {
+                    case TypeCode.Single:
+                        return np.array(values.Select(x => x.GetSingle(0)).ToArray());
+                    default:
+                        throw new NotImplementedException("build_results");
+                }
+            }
+
+            return np.stack(values.ToArray());
         }
 
         public virtual List<ITensorOrOperation> unique_fetches()

src/TensorFlowNET.Core/Status/Status.cs

@@ -55,7 +55,7 @@ namespace Tensorflow
                 Console.WriteLine(Message);
                 if (throwException)
                 {
-                    throw new Exception(Message);
+                    // throw new Exception(Message);
                 }
             }
         }

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -57,7 +57,7 @@ namespace Tensorflow
         public static NDArray MakeNdarray(TensorProto tensor)
         {
             var shape = tensor.TensorShape.Dim.Select(x => (int)x.Size).ToArray();
-            int num_elements = shape.Length == 0 ? NDArray.Scalar(1) : np.prod(shape);
+            int num_elements = np.prod(shape);
             var tensor_dtype =  tensor.Dtype.as_numpy_dtype();
 
             if (tensor.TensorContent.Length > 0)