fix _constant_if_small for zeros.

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -322,6 +322,8 @@ namespace Tensorflow
                         attr_value.Shape = val1.as_proto();
                     else if(value is long[] val2)
                         attr_value.Shape = tensor_util.as_shape(val2);
+                    else if (value is int[] val3)
+                        attr_value.Shape = tensor_util.as_shape(val3);
 
                     break;
                 default:

src/TensorFlowNET.Core/Operations/array_ops.py.cs

@@ -34,10 +34,9 @@ namespace Tensorflow
         private static Tensor _constant_if_small<T>(T value, Shape shape, TF_DataType dtype, string name)
         {
             Tensor tShape = null;
-            var nd = np.zeros<T>(shape);
             if (shape.Size < 1000)
             {
-                return constant_op.constant(nd, name: name);
+                return constant_op.constant(value, shape: shape, dtype: dtype, name: name);
             }
             else
             {

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -43,7 +43,7 @@ TensorFlow 1.13 RC.</PackageReleaseNotes>
 
   <ItemGroup>
     <PackageReference Include="Google.Protobuf" Version="3.6.1" />
-    <PackageReference Include="NumSharp" Version="0.7.0" />
+    <PackageReference Include="NumSharp" Version="0.7.1" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -56,9 +56,9 @@ namespace Tensorflow
 
                 switch (values)
                 {
-                    /*case bool boolVal:
+                    case bool boolVal:
                         nparray = boolVal;
-                        break;*/
+                        break;
                     case int intVal:
                         nparray = intVal;
                         break;
@@ -74,6 +74,9 @@ namespace Tensorflow
                     case string strVal:
                         nparray = strVal;
                         break;
+                    case string[] strVals:
+                        nparray = strVals;
+                        break;
                     default:
                         throw new Exception("make_tensor_proto Not Implemented");
                 }
@@ -100,7 +103,8 @@ namespace Tensorflow
             }
             else
             {
-                throw new NotImplementedException("make_tensor_proto shape not implemented");
+                shape_size = new TensorShape(shape).Size;
+                is_same_size = shape_size == nparray.size;
             }
 
             var tensor_proto = new tensor_pb2.TensorProto
@@ -111,41 +115,17 @@ namespace Tensorflow
 
             if (is_same_size && _TENSOR_CONTENT_TYPES.Contains(numpy_dtype) && shape_size > 1)
             {
-                var bytes = new List<byte>();
-                var nd2 = nparray.ravel();
-                switch (nparray.dtype.Name)
-                {
-                    case "Int32":
-                        nd2.Data<int>().Select(x => 
-                        {
-                            bytes.AddRange(BitConverter.GetBytes(x));
-                            return x;
-                        }).ToArray();
-                        break;
-                    case "Single":
-                        nd2.Data<float>().Select(x =>
-                        {
-                            bytes.AddRange(BitConverter.GetBytes(x));
-                            return x;
-                        }).ToArray();
-                        break;
-                    case "Double":
-                        nd2.Data<double>().Select(x =>
-                        {
-                            bytes.AddRange(BitConverter.GetBytes(x));
-                            return x;
-                        }).ToArray();
-                        break;
-                    default:
-                        throw new Exception("make_tensor_proto Not Implemented");
-                }
+                byte[] bytes = nparray.ToByteArray();
                 tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(bytes.ToArray());
                 return tensor_proto;
             }
 
-            if (numpy_dtype == TF_DataType.TF_STRING && !(values is NDArray) && values is string str)
+            if (numpy_dtype == TF_DataType.TF_STRING && !(values is NDArray))
             {
-                tensor_proto.StringVal.Add(Google.Protobuf.ByteString.CopyFromUtf8(str));
+                if (values is string str)
+                    tensor_proto.StringVal.Add(Google.Protobuf.ByteString.CopyFromUtf8(str));
+                else if (values is string[] str_values)
+                    tensor_proto.StringVal.AddRange(str_values.Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x)));
                 return tensor_proto;
             }
 

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

@@ -123,8 +123,6 @@ namespace Tensorflow
                     Version = _write_version
                 };
             });
-
-            
         }
 
         public Tensor _AddSaveOps(Tensor filename_tensor, SaveableObject[] saveables)

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -6,7 +6,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="NumSharp" Version="0.7.0" />
+    <PackageReference Include="NumSharp" Version="0.7.1" />
     <PackageReference Include="TensorFlow.NET" Version="0.1.0" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/ConstantTest.cs

@@ -58,6 +58,7 @@ namespace TensorFlowNET.UnitTest
 
                 var data = result.Data<int>();
                 Assert.AreEqual(0, data[0]);
+                Assert.AreEqual(0, data[500]);
                 Assert.AreEqual(0, data[result.size - 1]);
             });
         }
@@ -109,5 +110,15 @@ namespace TensorFlowNET.UnitTest
 
             //c_api.TF_StringDecode(str, (ulong)str.Length, IntPtr.Zero, ref dst_len, status);
         }
+
+        /// <summary>
+        /// tensorflow\c\c_api_test.cc
+        /// TestEncodeDecode
+        /// </summary>
+        [TestMethod]
+        public void EncodeDecode()
+        {
+
+        }
     }
 }

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -19,7 +19,7 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="15.9.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
     <PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
-    <PackageReference Include="NumSharp" Version="0.7.0" />
+    <PackageReference Include="NumSharp" Version="0.7.1" />
     <PackageReference Include="TensorFlow.NET" Version="0.1.0" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/TrainSaverTest.cs

@@ -10,11 +10,11 @@ namespace TensorFlowNET.UnitTest
     public class TrainSaverTest : Python
     {
         [TestMethod]
-        public void WriteGraph()
+        public void ExportGraph()
         {
             var v = tf.Variable(0, name: "my_variable");
             var sess = tf.Session();
-            tf.train.write_graph(sess.graph, "/tmp/my-model", "train.pbtxt");
+            tf.train.write_graph(sess.graph, "/tmp/my-model", "train1.pbtxt");
         }
 
         [TestMethod]
@@ -22,14 +22,13 @@ namespace TensorFlowNET.UnitTest
         {
             var v = tf.Variable(0, name: "my_variable");
             var sess = tf.Session();
-            tf.train.write_graph(sess.graph, "/tmp/my-model", "train.pbtxt");
+            tf.train.write_graph(sess.graph, "/tmp/my-model", "train2.pbtxt");
         }
 
         [TestMethod]
-        public void SaveSimple()
+        public void Save1()
         {
-            var w1 = tf.Variable(tf.random_normal(new int[] { 2 }), name: "w1");
-            var w2 = tf.Variable(tf.random_normal(new int[] { 5 }), name: "w2");
+            var w1 = tf.Variable(0, name: "save1");
 
             var init_op = tf.global_variables_initializer();
 
@@ -41,13 +40,13 @@ namespace TensorFlowNET.UnitTest
                 sess.run(init_op);
 
                 // Save the variables to disk.
-                var save_path = saver.save(sess, "/tmp/model.ckpt");
+                var save_path = saver.save(sess, "/tmp/model1.ckpt");
                 Console.WriteLine($"Model saved in path: {save_path}");
             });
         }
 
         [TestMethod]
-        public void Save()
+        public void Save2()
         {
             var v1 = tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
             var v2 = tf.get_variable("v2", shape: new TensorShape(5), initializer: tf.zeros_initializer);
@@ -69,7 +68,7 @@ namespace TensorFlowNET.UnitTest
                 dec_v2.op.run();
 
                 // Save the variables to disk.
-                var save_path = saver.save(sess, "/tmp/model.ckpt");
+                var save_path = saver.save(sess, "/tmp/model2.ckpt");
                 Console.WriteLine($"Model saved in path: {save_path}");
             });
         }