Memory leak in FuncGraph #727.

TensorFlow.NET.sln

@@ -26,12 +26,6 @@ Global
 		Debug|Any CPU = Debug|Any CPU
 		Debug|x64 = Debug|x64
 		Debug|x86 = Debug|x86
-		Debug-Minimal|Any CPU = Debug-Minimal|Any CPU
-		Debug-Minimal|x64 = Debug-Minimal|x64
-		Debug-Minimal|x86 = Debug-Minimal|x86
-		Publish|Any CPU = Publish|Any CPU
-		Publish|x64 = Publish|x64
-		Publish|x86 = Publish|x86
 		Release|Any CPU = Release|Any CPU
 		Release|x64 = Release|x64
 		Release|x86 = Release|x86
@@ -43,18 +37,6 @@ Global
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|x64.Build.0 = Debug|x64
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|x86.Build.0 = Debug|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|x64.ActiveCfg = Debug|x64
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|x64.Build.0 = Debug|x64
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|Any CPU.ActiveCfg = Release|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|Any CPU.Build.0 = Release|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|x64.ActiveCfg = Release|x64
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|x64.Build.0 = Release|x64
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|x86.ActiveCfg = Release|Any CPU
-		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Publish|x86.Build.0 = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.Build.0 = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|x64.ActiveCfg = Release|x64
@@ -67,18 +49,6 @@ Global
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug|x64.Build.0 = Debug|x64
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug|x86.Build.0 = Debug|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|x64.ActiveCfg = Debug|x64
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|x64.Build.0 = Debug|x64
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|Any CPU.ActiveCfg = Release|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|Any CPU.Build.0 = Release|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x64.ActiveCfg = Release|x64
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x64.Build.0 = Release|x64
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x86.ActiveCfg = Release|Any CPU
-		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x86.Build.0 = Release|Any CPU
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Release|Any CPU.Build.0 = Release|Any CPU
 		{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Release|x64.ActiveCfg = Release|x64
@@ -91,18 +61,6 @@ Global
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug|x64.Build.0 = Debug|x64
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug|x86.Build.0 = Debug|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|x64.ActiveCfg = Debug|x64
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|x64.Build.0 = Debug|x64
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|Any CPU.ActiveCfg = Release|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|Any CPU.Build.0 = Release|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|x64.ActiveCfg = Release|x64
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|x64.Build.0 = Release|x64
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|x86.ActiveCfg = Release|Any CPU
-		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Publish|x86.Build.0 = Release|Any CPU
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Release|Any CPU.Build.0 = Release|Any CPU
 		{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}.Release|x64.ActiveCfg = Release|x64
@@ -115,18 +73,6 @@ Global
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug|x64.Build.0 = Debug|x64
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug|x86.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|Any CPU.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|Any CPU.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|x64.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|x64.Build.0 = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|x86.ActiveCfg = Debug|Any CPU
-		{03F06299-3F4B-4449-A709-3A647657BC0C}.Publish|x86.Build.0 = Debug|Any CPU
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Release|Any CPU.Build.0 = Release|Any CPU
 		{03F06299-3F4B-4449-A709-3A647657BC0C}.Release|x64.ActiveCfg = Release|Any CPU
@@ -139,18 +85,6 @@ Global
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug|x64.Build.0 = Debug|x64
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug|x86.Build.0 = Debug|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|x64.ActiveCfg = Debug|x64
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|x64.Build.0 = Debug|x64
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|Any CPU.ActiveCfg = Release|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|Any CPU.Build.0 = Release|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|x64.ActiveCfg = Debug|x64
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|x64.Build.0 = Debug|x64
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|x86.ActiveCfg = Release|Any CPU
-		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Publish|x86.Build.0 = Release|Any CPU
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Release|Any CPU.Build.0 = Release|Any CPU
 		{49D71826-C03D-4FA7-9BAC-22C1327E65CF}.Release|x64.ActiveCfg = Release|x64
@@ -163,18 +97,6 @@ Global
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug|x64.Build.0 = Debug|x64
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug|x86.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|Any CPU.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|Any CPU.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|x64.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|x64.Build.0 = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|x86.ActiveCfg = Debug|Any CPU
-		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Publish|x86.Build.0 = Debug|Any CPU
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Release|Any CPU.Build.0 = Release|Any CPU
 		{1AB8108D-4FFE-4A16-88E7-328EAF686370}.Release|x64.ActiveCfg = Release|Any CPU
@@ -187,18 +109,6 @@ Global
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug|x64.Build.0 = Debug|x64
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug|x86.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|Any CPU.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|Any CPU.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|x64.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|x64.Build.0 = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|x86.ActiveCfg = Debug|Any CPU
-		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Publish|x86.Build.0 = Debug|Any CPU
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Release|Any CPU.Build.0 = Release|Any CPU
 		{F17AAECB-960A-4E18-A270-BAD776F0E55B}.Release|x64.ActiveCfg = Release|Any CPU
@@ -211,18 +121,6 @@ Global
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug|x64.Build.0 = Debug|x64
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug|x86.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|Any CPU.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|Any CPU.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|x64.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|x64.Build.0 = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|x86.ActiveCfg = Debug|Any CPU
-		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Publish|x86.Build.0 = Debug|Any CPU
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Release|Any CPU.Build.0 = Release|Any CPU
 		{84CA35F8-99FC-408E-8DF3-5AA175E5EFD3}.Release|x64.ActiveCfg = Release|Any CPU
@@ -235,18 +133,6 @@ Global
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug|x64.Build.0 = Debug|x64
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug|x86.ActiveCfg = Debug|Any CPU
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug|x86.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|Any CPU.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|Any CPU.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|x64.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|x64.Build.0 = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|x86.ActiveCfg = Debug|Any CPU
-		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Publish|x86.Build.0 = Debug|Any CPU
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Release|Any CPU.Build.0 = Release|Any CPU
 		{79EB56DF-E29E-4AE2-A7D9-FE403FD919BA}.Release|x64.ActiveCfg = Release|Any CPU

src/TensorFlowNET.Console/Diagnostician.cs

@@ -0,0 +1,64 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Text;
+using System.Linq;
+using static Tensorflow.Binding;
+using System.Text.RegularExpressions;
+
+namespace Tensorflow
+{
+    public class Diagnostician
+    {
+        public void Diagnose(string log)
+        {
+            var lines = File.ReadAllLines(log);
+
+            foreach(var (i, line) in enumerate(lines))
+            {
+                if(line.StartsWith("New Tensor "))
+                {
+                    var pointers = Regex.Matches(line, "0x[0-9a-f]{16}");
+                    var tensorHandle = pointers[0].Value;
+                    var tensorDataHandle = pointers[1].Value;
+
+                    if (lines.Skip(i).Count(x => x.StartsWith("Delete Tensor ") 
+                        && x.Contains(tensorHandle)
+                        && x.Contains(tensorDataHandle)) == 0)
+                        Console.WriteLine(line);
+                }
+                else if (line.StartsWith("New EagerTensorHandle "))
+                {
+                    var pointers = Regex.Matches(line, "0x[0-9a-f]{16}");
+                    var tensorHandle = pointers[0].Value;
+
+                    var del = $"Delete EagerTensorHandle {tensorHandle}";
+
+                    if (lines.Skip(i).Count(x => x == del) == 0)
+                        Console.WriteLine(line);
+                }
+                else if (line.StartsWith("Take EagerTensorHandle "))
+                {
+                    var pointers = Regex.Matches(line, "0x[0-9a-f]{16}");
+                    var eagerTensorHandle = pointers[0].Value;
+                    var tensorHandle = pointers[1].Value;
+
+                    var delTensor = $"Delete Tensor {tensorHandle}";
+                    var delEagerTensor = $"Delete EagerTensorHandle {eagerTensorHandle}";
+                    if (lines.Skip(i).Count(x => x.StartsWith(delTensor)) == 0
+                        || lines.Skip(i).Count(x => x.StartsWith(delEagerTensor)) == 0)
+                        Console.WriteLine(line);
+                }
+                else if (line.StartsWith("Created Resource "))
+                {
+                    var pointers = Regex.Matches(line, "0x[0-9a-f]{16}");
+                    var eagerTensorHandle = pointers[0].Value;
+
+                    var delTensor = $"Deleted Resource {eagerTensorHandle}";
+                    if (lines.Skip(i).Count(x => x.StartsWith(delTensor)) == 0)
+                        Console.WriteLine(line);
+                }
+            }
+        }
+    }
+}

src/TensorFlowNET.Console/MemoryBasicTest.cs

@@ -0,0 +1,152 @@
+using NumSharp;
+using System;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine.DataAdapters;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow
+{
+    class MemoryBasicTest
+    {
+        public Action<int, int> Placeholder
+            => (epoch, iterate) =>
+            {
+                var ph = array_ops.placeholder(tf.float32, (10, 512, 512, 3));
+            };
+
+        /// <summary>
+        /// 
+        /// </summary>
+        public Action<int, int> Constant
+            => (epoch, iterate) =>
+            {
+                var tensor = tf.constant(3112.0f);
+            };
+
+        public Action<int, int> Constant2x3
+            => (epoch, iterate) =>
+            {
+                var nd = np.arange(1000).reshape(10, 100);
+                var tensor = tf.constant(nd);
+                var data = tensor.numpy();
+            };
+
+        public Action<int, int> ConstantString
+            => (epoch, iterate) =>
+            {
+                var tensor = tf.constant(new string[] 
+                {
+                    "Biden immigration bill would put millions of illegal immigrants on 8-year fast-track to citizenship",
+                    "The Associated Press, which also reported that the eight-year path is in the bill.",
+                    "The bill would also include provisions to stem the flow of migration by addressing root causes of migration from south of the border."
+                });
+                var data = tensor.numpy();
+            };
+
+        public Action<int, int> Variable
+            => (epoch, iterate) =>
+            {
+                var nd = np.arange(1 * 256 * 256 * 3).reshape(1, 256, 256, 3);
+                ResourceVariable variable = tf.Variable(nd);
+            };
+
+        public Action<int, int> VariableRead
+            => (epoch, iterate) =>
+            {
+                var nd = np.zeros(1 * 256 * 256 * 3).astype(np.float32).reshape(1, 256, 256, 3);
+                ResourceVariable variable = tf.Variable(nd);
+                var nd2 = np.arange(1 * 256 * 256 * 3).astype(np.float32).reshape(1, 256, 256, 3);
+                variable.assign(nd2);
+
+                for (int i = 0; i< 100; i++)
+                {
+                    var v = variable.numpy();
+                }
+            };
+
+
+        public Action<int, int> MathAdd
+            => (epoch, iterate) =>
+            {
+                var x = tf.constant(3112.0f);
+                var y = tf.constant(3112.0f);
+                var z = x + y;
+            };
+
+        public Action<int, int> Gradient
+            => (epoch, iterate) =>
+            {
+                var w = tf.constant(3112.0f);
+                using var tape = tf.GradientTape();
+                tape.watch(w);
+                var loss = w * w;
+                var grad = tape.gradient(loss, w);
+            };
+
+        public Action<int, int> Conv2DWithTensor
+            => (epoch, iterate) =>
+            {
+                var input = array_ops.zeros((10, 32, 32, 3), dtypes.float32);
+                var filter = array_ops.zeros((3, 3, 3, 32), dtypes.float32);
+                var strides = new[] { 1, 1, 1, 1 };
+                var dilations = new[] { 1, 1, 1, 1 };
+
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "Conv2D", null,
+                    null,
+                    input, filter,
+                    "strides", strides,
+                    "use_cudnn_on_gpu", true,
+                    "padding", "VALID",
+                    "explicit_paddings", new int[0],
+                    "data_format", "NHWC",
+                    "dilations", dilations);
+            };
+
+        public Action<int, int> Conv2DWithVariable
+            => (epoch, iterate) =>
+            {
+                var input = array_ops.zeros((10, 32, 32, 3), dtypes.float32);
+                var filter = tf.Variable(array_ops.zeros((3, 3, 3, 32), dtypes.float32));
+                var strides = new[] { 1, 1, 1, 1 };
+                var dilations = new[] { 1, 1, 1, 1 };
+
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "Conv2D", null,
+                    null,
+                    input, filter,
+                    "strides", strides,
+                    "use_cudnn_on_gpu", true,
+                    "padding", "VALID",
+                    "explicit_paddings", new int[0],
+                    "data_format", "NHWC",
+                    "dilations", dilations);
+            };
+
+        public Action<int, int> Dataset
+            => (epoch, iterate) =>
+            {
+                TensorShape shape = (16, 32, 32, 3);
+                var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);
+                var data_handler = new DataHandler(new DataHandlerArgs
+                {
+                    X = images,
+                    BatchSize = 2,
+                    StepsPerEpoch = -1,
+                    InitialEpoch = 0,
+                    Epochs = 2,
+                    MaxQueueSize = 10,
+                    Workers = 1,
+                    UseMultiprocessing = false,
+                    StepsPerExecution = tf.Variable(1)
+                });
+
+                /*foreach (var (_epoch, iterator) in data_handler.enumerate_epochs())
+                {
+                    foreach (var step in data_handler.steps())
+                        iterator.next();
+                }*/
+            };
+    }
+}

src/TensorFlowNET.Console/MemoryFuncGraphTest.cs

@@ -0,0 +1,30 @@
+using NumSharp;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Functions;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+    class MemoryFuncGraphTest
+    {
+        public Action<int, int> ConcreteFunction
+            => (epoch, iterate) =>
+            {
+                var func = new ConcreteFunction(Guid.NewGuid().ToString());
+                func.Enter();
+                var input = tf.placeholder(tf.float32);
+                var output = permutation(input);
+                func.ToGraph(input, output);
+                func.Exit();
+            };
+
+        Tensor permutation(Tensor tensor)
+        {
+            TensorShape shape = (8, 64, 64, 3);
+            var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);
+            return tf.constant(images);
+        }
+    }
+}

src/TensorFlowNET.Console/MemoryKerasTest.cs

@@ -0,0 +1,51 @@
+using NumSharp;
+using System;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow
+{
+    class MemoryKerasTest
+    {
+        public Action<int, int> Conv2DLayer
+            => (epoch, iterate) =>
+            {
+                var input_shape = new int[] { 4, 512, 512, 3 };
+                var x = tf.random.normal(input_shape);
+                var conv2d = keras.layers.Conv2D(2, 3, activation: keras.activations.Relu);
+                var output = conv2d.Apply(x);
+            };
+
+        public Action<int, int> InputLayer
+            => (epoch, iterate) =>
+            {
+                TensorShape shape = (32, 256, 256, 3); // 48M
+                var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);
+
+                var inputs = keras.Input((shape.dims[1], shape.dims[2], 3));
+                var conv2d = keras.layers.Conv2D(32, kernel_size: (3, 3),
+                    activation: keras.activations.Linear);
+                var outputs = conv2d.Apply(inputs);
+            };
+
+        public Action<int, int> Prediction
+            => (epoch, iterate) =>
+            {
+                TensorShape shape = (32, 256, 256, 3); // 48M
+                var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);
+
+                var inputs = keras.Input((shape.dims[1], shape.dims[2], 3));
+                var conv2d = keras.layers.Conv2D(32, kernel_size: (3, 3),
+                    activation: keras.activations.Linear).Apply(inputs);
+
+                var flatten = keras.layers.Flatten().Apply(inputs);
+                var outputs = keras.layers.Dense(10).Apply(flatten);
+
+                var model = keras.Model(inputs, outputs, "prediction");
+                for (int i = 0; i < 10; i++)
+                {
+                    model.predict(images, batch_size: 8);
+                }
+            };
+    }
+}

src/TensorFlowNET.Console/MemoryMonitor.cs

@@ -4,6 +4,7 @@ using System.Threading;
 using System.Threading.Tasks;
 using NumSharp;
 using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
 
 namespace Tensorflow
 {
@@ -11,38 +12,44 @@ namespace Tensorflow
     {
         public void WarmUp()
         {
+            TensorShape shape = (1, 32, 32, 3);
+            np.arange(shape.size).astype(np.float32).reshape(shape.dims);
+
             print($"tensorflow native version: v{tf.VERSION}");
+            tf.Context.ensure_initialized();
             var a = tf.constant(np.ones(10, 10));
             var b = tf.Variable(a);
             var c = tf.Variable(b);
             var d = b * c;
             print(d.numpy());
 
-            GC.WaitForPendingFinalizers();
             GC.Collect();
-            Thread.Sleep(1000);
+            GC.WaitForPendingFinalizers();
         }
 
-        public void Execute(int epoch, int iterate, Action<int> process)
+        public void Execute(int epoch, int iterate, Action<int, int> process)
         {
+            GC.Collect();
+            GC.WaitForPendingFinalizers();
+            var initialTotalMemory = Process.GetCurrentProcess().PrivateMemorySize64;
             print($"{process.Method.Name} started...");
 
-            // new thread to run
-            Task.Run(() =>
+            for (int i = 0; i < epoch; i++)
             {
-                for (int i = 0; i < epoch; i++)
-                {
-                    var initialMemory = Process.GetCurrentProcess().PrivateMemorySize64;// GC.GetTotalMemory(true);
-                    process(iterate);
-                    var finalMemory = Process.GetCurrentProcess().PrivateMemorySize64; //GC.GetTotalMemory(true);
-                    print($"Epoch {i}: {Format(finalMemory - initialMemory)}.");
-
-                    GC.Collect();
-                    GC.WaitForPendingFinalizers();
-                }
-            }).Wait();
-
-            print($"Total {process.Method.Name} usage {Format(Process.GetCurrentProcess().PrivateMemorySize64)}");
+                var initialMemory = Process.GetCurrentProcess().PrivateMemorySize64;
+                for (int j = 0; j < iterate; j++)
+                    process(i, j);
+
+                keras.backend.clear_session();
+
+                GC.Collect();
+                GC.WaitForPendingFinalizers();
+                var finalMemory = Process.GetCurrentProcess().PrivateMemorySize64;
+                print($"Epoch {i}: {Format(finalMemory - initialMemory)}.");
+            }
+
+            var finalTotalMemory = Process.GetCurrentProcess().PrivateMemorySize64;
+            print($"Memory usage difference: {Format(finalTotalMemory - initialTotalMemory)} / {Format(Process.GetCurrentProcess().PrivateMemorySize64)}");
         }
 
         private string Format(long usage)

src/TensorFlowNET.Console/MemoryTestingCases.cs

@@ -1,113 +0,0 @@
-using NumSharp;
-using System;
-using static Tensorflow.Binding;
-
-namespace Tensorflow
-{
-    class MemoryTestingCases
-    {
-        /// <summary>
-        /// 
-        /// </summary>
-        public Action<int> Constant
-            => (iterate) =>
-            {
-                for (int i = 0; i < iterate; i++)
-                {
-                    var tensor = tf.constant(3112.0f);
-                }
-            };
-
-        public Action<int> Constant2x3
-            => (iterate) =>
-            {
-                var nd = np.arange(1000).reshape(10, 100);
-                for (int i = 0; i < iterate; i++)
-                {
-                    var tensor = tf.constant(nd);
-                    var data = tensor.numpy();
-                }
-            };
-
-        public Action<int> Variable
-            => (iterate) =>
-            {
-                for (int i = 0; i < iterate; i++)
-                {
-                    var nd = np.arange(128 * 128 * 3).reshape(128, 128, 3);
-                    var variable = tf.Variable(nd);
-                }
-            };
-
-        public Action<int> MathAdd
-            => (iterate) =>
-            {
-                var x = tf.constant(3112.0f);
-                var y = tf.constant(3112.0f);
-
-                for (int i = 0; i < iterate; i++)
-                {
-                    var z = x + y;
-                }
-            };
-
-        public Action<int> Gradient
-            => (iterate) =>
-            {
-                for (int i = 0; i < iterate; i++)
-                {
-                    var w = tf.constant(3112.0f);
-                    using var tape = tf.GradientTape();
-                    tape.watch(w);
-                    var loss = w * w;
-                    var grad = tape.gradient(loss, w);
-                }
-            };
-
-        public Action<int> Conv2dWithVariable
-            => (iterate) =>
-            {
-                for (int i = 0; i < iterate; i++)
-                {
-                    var input = array_ops.zeros((10, 32, 32, 3), dtypes.float32);
-                    var filter = tf.Variable(array_ops.zeros((3, 3, 3, 32), dtypes.float32));
-                    var strides = new[] { 1, 1, 1, 1 };
-                    var dilations = new[] { 1, 1, 1, 1 };
-
-                    var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
-                        "Conv2D", null,
-                        null,
-                        input, filter,
-                        "strides", strides,
-                        "use_cudnn_on_gpu", true,
-                        "padding", "VALID",
-                        "explicit_paddings", new int[0],
-                        "data_format", "NHWC",
-                        "dilations", dilations);
-                }
-            };
-
-        public Action<int> Conv2dWithTensor
-            => (iterate) =>
-            {
-                for (int i = 0; i < iterate; i++)
-                {
-                    var input = array_ops.zeros((10, 32, 32, 3), dtypes.float32);
-                    var filter = array_ops.zeros((3, 3, 3, 32), dtypes.float32);
-                    var strides = new[] { 1, 1, 1, 1 };
-                    var dilations = new[] { 1, 1, 1, 1 };
-
-                    var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
-                        "Conv2D", null,
-                        null,
-                        input, filter,
-                        "strides", strides,
-                        "use_cudnn_on_gpu", true,
-                        "padding", "VALID",
-                        "explicit_paddings", new int[0],
-                        "data_format", "NHWC",
-                        "dilations", dilations);
-                }
-            };
-    }
-}

src/TensorFlowNET.Console/Program.cs

@@ -1,4 +1,5 @@
 using System;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
@@ -6,6 +7,9 @@ namespace Tensorflow
     {
         static void Main(string[] args)
         {
+            var diag = new Diagnostician();
+            // diag.Diagnose(@"D:\memory.txt");
+
             // this class is used explor new features.
             var exploring = new Exploring();
             // exploring.Run();
@@ -14,34 +18,72 @@ namespace Tensorflow
             var mm = new MemoryMonitor();
             // warm up tensorflow.net 37.3M.
             mm.WarmUp();
-            var cases = new MemoryTestingCases();
 
+            BasicTest(mm);
+
+            KerasTest(mm);
+
+            FuncGraph(mm);
+
+            // 85M
+            Console.WriteLine("Finished.");
+            Console.ReadLine();
+        }
+
+        static void BasicTest(MemoryMonitor mm)
+        {
             int batchSize = 1000;
 
+            var basic = new MemoryBasicTest();
+
+            // 1 million placeholder
+            /*tf.compat.v1.disable_eager_execution();
+            mm.Execute(10, 100 * batchSize, basic.Placeholder);
+            tf.enable_eager_execution();*/
+
             // 1 million tensor
-            mm.Execute(10, 100 * batchSize, cases.Constant);
+            mm.Execute(10, 100 * batchSize, basic.Constant);
 
             // explaination of constant
-            mm.Execute(10, 100 * batchSize, cases.Constant2x3);
+            mm.Execute(10, 100 * batchSize, basic.Constant2x3);
+
+            mm.Execute(10, 100 * batchSize, basic.ConstantString);
 
-            // +0M
-            mm.Execute(10, batchSize, cases.Conv2dWithTensor);
+            // 100K float variable.
+            mm.Execute(10, batchSize, basic.Variable);
 
-            // 100K float variable 84M.
-            mm.Execute(10, batchSize, cases.Variable);
+            // 1 million math.
+            mm.Execute(10, 100 * batchSize, basic.MathAdd);
 
-            // +45M memory leak
-            mm.Execute(10, batchSize, cases.Conv2dWithVariable);
+            // Conv2d in constant tensor
+            mm.Execute(10, batchSize, basic.Conv2DWithTensor);
 
-            // 1 million math add 39M.
-            mm.Execute(10, 100 * batchSize, cases.MathAdd);
+            // Conv2d in variable
+            mm.Execute(10, batchSize, basic.Conv2DWithVariable);
 
             // 100K gradient 44M.
-            mm.Execute(10, 10 * batchSize, cases.Gradient);
+            mm.Execute(10, 10 * batchSize, basic.Gradient);
 
-            // 95M
-            Console.WriteLine("Finished.");
-            Console.ReadLine();
+            // memory leak when increasing the epoch
+            mm.Execute(10, 10, basic.Dataset);
+        }
+
+        static void KerasTest(MemoryMonitor mm)
+        {
+            var keras = new MemoryKerasTest();
+
+            // +1M (10,50)
+            mm.Execute(10, 1, keras.Conv2DLayer);
+
+            mm.Execute(10, 50, keras.InputLayer);
+
+            mm.Execute(10, 10, keras.Prediction);
+        }
+
+        static void FuncGraph(MemoryMonitor mm)
+        {
+            var func = new MemoryFuncGraphTest();
+            mm.Execute(10, 100, func.ConcreteFunction);
         }
     }
 }

src/TensorFlowNET.Console/Tensorflow.Console.csproj

@@ -6,6 +6,7 @@
     <RootNamespace>Tensorflow</RootNamespace>
     <AssemblyName>Tensorflow</AssemblyName>
     <Platforms>AnyCPU;x64</Platforms>
+    <LangVersion>9.0</LangVersion>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">

src/TensorFlowNET.Core/Binding.cs

@@ -4,7 +4,6 @@ namespace Tensorflow
 {
     public static partial class Binding
     {
-        [DebuggerHidden]
         public static tensorflow tf { get; } = New<tensorflow>();
 
         /// <summary>

src/TensorFlowNET.Core/Contexts/Context.cs

@@ -81,7 +81,7 @@ namespace Tensorflow.Contexts
         /// Checks whether the current thread has eager execution enabled.
         /// </summary>
         /// <returns></returns>
-        // [DebuggerStepThrough]
+        [DebuggerStepThrough]
         public bool executing_eagerly()
         {
             if(context_switches.Count() == 0)
@@ -136,8 +136,11 @@ namespace Tensorflow.Contexts
         public void reset_context()
         {
             ops.reset_uid();
+            // tf.defaultSession = null;
             ops.reset_default_graph();
             context_switches.Clear();
+            tf.Context.ensure_initialized();
+
             if (_handle != null)
                 c_api.TFE_ContextClearCaches(_handle);
         }

src/TensorFlowNET.Core/Data/IteratorBase.cs

@@ -1,6 +0,0 @@
-namespace Tensorflow
-{
-    public class IteratorBase
-    {
-    }
-}

src/TensorFlowNET.Core/Data/IteratorResourceDeleter.cs

@@ -1,26 +0,0 @@
-using System;
-
-namespace Tensorflow
-{
-    /// <summary>
-    /// An object which cleans up an iterator resource handle.
-    /// </summary>
-    public class IteratorResourceDeleter : IDisposable
-    {
-        Tensor _handle;
-        Tensor _deleter;
-        dataset_ops ops;
-
-        public IteratorResourceDeleter(Tensor handle, Tensor deleter)
-        {
-            _handle = handle;
-            _deleter = deleter;
-            ops = new dataset_ops();
-        }
-
-        public void Dispose()
-        {
-            ops.delete_iterator(_handle, _deleter);
-        }
-    }
-}

src/TensorFlowNET.Core/Data/MapDataset.cs

@@ -15,7 +15,7 @@ namespace Tensorflow
             bool preserve_cardinality = false,
             bool use_legacy_function = false) : base(input_dataset)
         {
-            using var func = new ConcreteFunction($"{map_func.Method.Name}_{Guid.NewGuid()}");
+            var func = new ConcreteFunction($"{map_func.Method.Name}_{Guid.NewGuid()}");
             func.Enter();
             var input = tf.placeholder(input_dataset.element_spec[0].dtype);
             var output = map_func(input);

src/TensorFlowNET.Core/Data/OptimizeDataset.cs

@@ -7,8 +7,6 @@ namespace Tensorflow
     /// </summary>
     public class OptimizeDataset : UnaryUnchangedStructureDataset
     {
-        Tensor _optimizations;
-
         public OptimizeDataset(IDatasetV2 dataset,
             string[] optimizations = null,
             string[] optimization_configs = null) :
@@ -19,7 +17,7 @@ namespace Tensorflow
             if (optimization_configs == null)
                 optimization_configs = new string[0];
 
-            _optimizations = tf.convert_to_tensor(optimizations, dtype: TF_DataType.TF_STRING, name: "optimizations");
+            var _optimizations = tf.convert_to_tensor(optimizations, dtype: TF_DataType.TF_STRING, name: "optimizations");
             variant_tensor = ops.optimize_dataset(
                 _input_dataset.variant_tensor,
                 _optimizations,

src/TensorFlowNET.Core/Data/OwnedIterator.cs

@@ -8,14 +8,13 @@ namespace Tensorflow
     /// <summary>
     /// An iterator producing tf.Tensor objects from a tf.data.Dataset.
     /// </summary>
-    public class OwnedIterator : IteratorBase, IDisposable
+    public class OwnedIterator : IDisposable
     {
         IDatasetV2 _dataset;
         TensorSpec[] _element_spec;
         dataset_ops ops = new dataset_ops();
-        Tensor _iterator_resource;
         Tensor _deleter;
-        IteratorResourceDeleter _resource_deleter;
+        Tensor _iterator_resource;
 
         public OwnedIterator(IDatasetV2 dataset)
         {
@@ -29,9 +28,6 @@ namespace Tensorflow
             _element_spec = dataset.element_spec;
             (_iterator_resource, _deleter) = ops.anonymous_iterator_v2(_dataset.output_types, _dataset.output_shapes);
             ops.make_iterator(dataset.variant_tensor, _iterator_resource);
-
-            // Delete the resource when this object is deleted
-            _resource_deleter = new IteratorResourceDeleter(_iterator_resource, _deleter);
         }
 
         public Tensor[] next()
@@ -51,7 +47,7 @@ namespace Tensorflow
 
         public void Dispose()
         {
-            _resource_deleter.Dispose();
+            tf.Runner.Execute(tf.Context, "DeleteIterator", 0, new[] { _iterator_resource, _deleter }, null);
         }
     }
 }

src/TensorFlowNET.Core/Data/PrefetchDataset.cs

@@ -7,17 +7,15 @@ namespace Tensorflow
     /// </summary>
     public class PrefetchDataset : UnaryUnchangedStructureDataset
     {
-        Tensor _buffer_size;
-
         public PrefetchDataset(IDatasetV2 input_dataset,
             long buffer_size = -1,
             int? slack_period = null) :
             base(input_dataset)
         {
-            _buffer_size = tf.convert_to_tensor(buffer_size, dtype: TF_DataType.TF_INT64, name: "buffer_size");
+            var buffer_size_tensor = tf.convert_to_tensor(buffer_size, dtype: TF_DataType.TF_INT64, name: "buffer_size");
 
             variant_tensor = ops.prefetch_dataset(input_dataset.variant_tensor,
-                _buffer_size,
+                buffer_size_tensor,
                 input_dataset.output_types,
                 input_dataset.output_shapes,
                 slack_period: slack_period);

src/TensorFlowNET.Core/Data/RangeDataset.cs

@@ -5,21 +5,17 @@ namespace Tensorflow.Data
 {
     public class RangeDataset : DatasetSource
     {
-        Tensor start;
-        Tensor step;
-        Tensor stop;
-
         public RangeDataset(int stop,
             int start = 0,
             int step = 1,
             TF_DataType output_type = TF_DataType.TF_INT64)
         {
-            this.start = tf.convert_to_tensor((long)start);
-            this.step = tf.convert_to_tensor((long)step);
-            this.stop = tf.convert_to_tensor((long)stop);
+            var start_tensor = tf.convert_to_tensor((long)start);
+            var step_tensor = tf.convert_to_tensor((long)step);
+            var stop_tensor = tf.convert_to_tensor((long)stop);
 
             structure = new TensorSpec[] { new TensorSpec(new int[0], dtype: output_type) };
-            variant_tensor = ops.range_dataset(this.start, this.stop, this.step, output_types, output_shapes);
+            variant_tensor = ops.range_dataset(start_tensor, stop_tensor, step_tensor, output_types, output_shapes);
         }
     }
 }

src/TensorFlowNET.Core/Data/RepeatDataset.cs

@@ -5,14 +5,12 @@
     /// </summary>
     public class RepeatDataset : UnaryUnchangedStructureDataset
     {
-        Tensor _count;
-
         public RepeatDataset(IDatasetV2 input_dataset, int count = -1) :
             base(input_dataset)
         {
-            _count = constant_op.constant(count, dtype: TF_DataType.TF_INT64, name: "count");
+            var count_tensor = constant_op.constant(count, dtype: TF_DataType.TF_INT64, name: "count");
             variant_tensor = ops.repeat_dataset(input_dataset.variant_tensor,
-                _count,
+                count_tensor,
                 input_dataset.output_types,
                 input_dataset.output_shapes);
         }

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs

@@ -42,8 +42,7 @@ namespace Tensorflow.Eager
             int num_outputs)
         {
             var status = tf.Status;
-            var op = GetOp(ctx, op_name, status);
-            status.Check(true);
+            using var op = GetOp(ctx, op_name, status);
             c_api.TFE_OpSetDevice(op, device_name, status.Handle);
             if (status.ok())
             {
@@ -67,7 +66,7 @@ namespace Tensorflow.Eager
                 c_api.TFE_Execute(op, outputs, out num_outputs, status.Handle);
                 status.Check(true);
             }
-            return outputs.Select(x => new EagerTensor(x, op)).ToArray();
+            return outputs.Select(x => new EagerTensor(x)).ToArray();
         }
     }
 }

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs

@@ -46,7 +46,7 @@ namespace Tensorflow.Eager
             op_exec_info.run_callbacks = op_exec_info.run_gradient_callback || op_exec_info.run_post_exec_callbacks;
 
             var status = tf.Status;
-            var op = GetOp(ctx, opName, status);
+            using var op = GetOp(ctx, opName, status);
 
             var op_def = tf.get_default_graph().GetOpDef(opName);
 
@@ -158,7 +158,8 @@ namespace Tensorflow.Eager
             c_api.TFE_Execute(op, retVals, out num_retvals, status.Handle);
             status.Check(true);
 
-            var flat_result = retVals.Select(x => new EagerTensor(x, op)).ToArray();
+            var flat_result = retVals.Select(x => new EagerTensor(x)).ToArray();
+
 
             if (op_exec_info.run_callbacks)
             {
@@ -183,9 +184,7 @@ namespace Tensorflow.Eager
             status.Check(true);
             return op;*/
             var op = c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle);
-#if TRACK_TENSOR_LIFE
-            print($"New OpHandle 0x{op.DangerousGetHandle().ToString("x16")}");
-#endif
+            status.Check(true);
             return op;
         }
 

src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs

@@ -1,57 +1,56 @@
 using NumSharp;
 using System;
+using System.Linq;
 using static Tensorflow.Binding;
 
 namespace Tensorflow.Eager
 {
     public partial class EagerTensor
     {
-        SafeOpHandle _opHandle;
-
-        public EagerTensor(SafeTensorHandleHandle handle, SafeOpHandle opHandle) : base(IntPtr.Zero)
+        public EagerTensor(SafeTensorHandleHandle handle) : base(IntPtr.Zero)
         {
-            _opHandle = opHandle;
+            _id = ops.uid();
             EagerTensorHandle = handle;
             Resolve();
         }
 
         public EagerTensor(string value, string device_name) : base(value)
         {
-            SetEagerTensorHandleAndResolve();
+            NewEagerTensorHandle(_handle);
         }
 
         public EagerTensor(byte[] value, string device_name, TF_DataType dtype) : base(value, dType: dtype)
         {
-            SetEagerTensorHandleAndResolve();
+            NewEagerTensorHandle(_handle);
         }
 
         public EagerTensor(string[] value, string device_name) : base(value)
         {
-            SetEagerTensorHandleAndResolve();
+            NewEagerTensorHandle(_handle);
         }
 
         public EagerTensor(NDArray value, string device_name) : base(value)
         {
-            SetEagerTensorHandleAndResolve();
+            NewEagerTensorHandle(_handle);
         }
 
-        void SetEagerTensorHandleAndResolve()
+        void NewEagerTensorHandle(IntPtr h)
         {
-            EagerTensorHandle = c_api.TFE_NewTensorHandle(_handle, tf.Status.Handle);
-            Resolve();
+            _id = ops.uid();
+            EagerTensorHandle = c_api.TFE_NewTensorHandle(h, tf.Status.Handle);
+            tf.Status.Check(true);
+#if TRACK_TENSOR_LIFE
+            print($"New EagerTensorHandle {EagerTensorHandle} {Id} From 0x{h.ToString("x16")}");
+#endif
         }
 
-        public EagerTensor Resolve()
+        private void Resolve()
         {
-            _id = ops.uid();
-
-            if (_handle == IntPtr.Zero)
-                _handle = c_api.TFE_TensorHandleResolve(EagerTensorHandle, tf.Status.Handle);
+            _handle = c_api.TFE_TensorHandleResolve(EagerTensorHandle, tf.Status.Handle);
+            tf.Status.Check(true);
 #if TRACK_TENSOR_LIFE
-            print($"New TensorHandle {Id} 0x{_handle.ToString("x16")}");
-            print($"New EagerTensorHandle {Id} {EagerTensorHandle}");
+            print($"Take EagerTensorHandle {EagerTensorHandle} {Id} Resolving 0x{_handle.ToString("x16")}");
 #endif
-            return this;
         }
 
         /// <summary>
@@ -80,22 +79,10 @@ namespace Tensorflow.Eager
             }
         }
 
-        public override IntPtr ToPointer()
-            => EagerTensorHandle?.DangerousGetHandle() ?? IntPtr.Zero;
-
-        protected override void DisposeManagedResources()
-        {
-            base.DisposeManagedResources();
-        }
-
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
             base.DisposeUnmanagedResources(handle);
-            
             EagerTensorHandle.Dispose();
-
-            if (_opHandle != null)
-                _opHandle.Dispose();
         }
     }
 }

src/TensorFlowNET.Core/Eager/EagerTensor.Implicit.cs

@@ -4,8 +4,7 @@ namespace Tensorflow.Eager
 {
     public partial class EagerTensor
     {
-        [Obsolete("Implicit conversion of EagerTensor to IntPtr is not supported.", error: true)]
         public static implicit operator IntPtr(EagerTensor tensor)
-            => throw new NotSupportedException();
+            => tensor.EagerTensorHandle.DangerousGetHandle();
     }
 }

src/TensorFlowNET.Core/Eager/SafeOpHandle.cs

@@ -30,16 +30,11 @@ namespace Tensorflow.Eager
         public SafeOpHandle(IntPtr handle)
             : base(handle)
         {
-#if TRACK_TENSOR_LIFE
-            print($"Get OpHandle 0x{handle.ToString("x16")}");
-#endif
+
         }
 
         protected override bool ReleaseHandle()
         {
-#if TRACK_TENSOR_LIFE
-            print($"Delete OpHandle 0x{handle.ToString("x16")}");
-#endif
             c_api.TFE_DeleteOp(handle);
             SetHandle(IntPtr.Zero);
             return true;

src/TensorFlowNET.Core/Functions/ConcreteFunction.cs

@@ -10,24 +10,14 @@ namespace Tensorflow.Functions
     /// <summary>
     /// 
     /// </summary>
-    public class ConcreteFunction : IDisposable
+    public class ConcreteFunction
     {
-        IntPtr _handle;
         FuncGraph func_graph;
         public Tensor[] Inputs => func_graph.Inputs;
         public Tensor[] CapturedInputs => func_graph.external_captures;
 
-        public string Name
-        {
-            get
-            {
-                if (func_graph != null)
-                    return func_graph.FuncName;
+        public string Name => func_graph?.FuncName;
 
-                return _handle == IntPtr.Zero ? string.Empty : c_api.StringPiece(c_api.TF_FunctionName(_handle));
-            }
-        }
-        
         public Tensor[] Outputs;
         public Type ReturnType;
         public TensorSpec[] OutputStructure;
@@ -48,39 +38,37 @@ namespace Tensorflow.Functions
         {
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
 
-            // IntPtr func_handle;
-            using var graph = new FuncGraph(func_name);
-            graph.as_default();
+            func_graph = new FuncGraph(func_name);
+            func_graph.as_default();
             var input = tf.placeholder(dtype);
             var output = func(input);
 
-            var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-            _handle = graph.ToGraph(opers,
+            var opers = func_graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
+            func_graph.ToGraph(opers,
                 new[] { input },
                 new[] { output },
                 null);
-            graph.Exit();
+            func_graph.Exit();
         }
 
         public ConcreteFunction(Func<Tensor, IDatasetV2> func, TF_DataType dtype)
         {
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
 
-            // IntPtr func_handle;
-            using var graph = new FuncGraph(func_name);
-            graph.as_default();
+            func_graph = new FuncGraph(func_name);
+            func_graph.as_default();
 
             var input = tf.placeholder(dtype);
             var output = func(input);
 
             OutputStructure = output.structure;
 
-            var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-            _handle = graph.ToGraph(opers,
+            var opers = func_graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
+            func_graph.ToGraph(opers,
                 new[] { input },
                 new[] { output.variant_tensor },
                 null);
-            graph.Exit();
+            func_graph.Exit();
         }
 
         public ConcreteFunction(Func<Tensors, Tensors> func,
@@ -89,8 +77,8 @@ namespace Tensorflow.Functions
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
 
             // IntPtr func_handle;
-            using var graph = new FuncGraph(func_name);
-            graph.as_default();
+            func_graph = new FuncGraph(func_name);
+            func_graph.as_default();
 
             var inputs = new Tensors();
             foreach(var (i, dtype) in enumerate(dtypes))
@@ -98,15 +86,15 @@ namespace Tensorflow.Functions
             Outputs = func(inputs);
             OutputStructure = Outputs.Select(x => x.ToTensorSpec()).ToArray();
 
-            var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-            _handle = graph.ToGraph(opers, inputs, Outputs, null);
-            graph.Exit();
+            var opers = func_graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
+            func_graph.ToGraph(opers, inputs, Outputs, null);
+            func_graph.Exit();
         }
 
         public void ToGraph(Tensors inputs, Tensors outputs)
         {
             var opers = func_graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-            _handle = func_graph.ToGraph(opers,
+            func_graph.ToGraph(opers,
                 inputs,
                 outputs,
                 null);
@@ -180,11 +168,5 @@ namespace Tensorflow.Functions
 
         public override string ToString()
             => Name;
-
-        public void Dispose()
-        {
-            c_api.TFE_ContextRemoveFunction(tf.Context.Handle, Name, tf.Status.Handle);
-            c_api.TF_DeleteFunction(_handle);
-        }
     }
 }

src/TensorFlowNET.Core/Graphs/AutoGraph.cs

@@ -10,20 +10,18 @@ namespace Tensorflow.Graphs
         {
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
 
-            // IntPtr func_handle;
-            using (var graph = new FuncGraph(func_name))
-            {
-                graph.as_default();
-                var input = tf.placeholder(tf.int32);
-                var output = func(input);
+            var graph = new FuncGraph(func_name);
+            graph.as_default();
 
-                var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-                var func_handle = graph.ToGraph(opers,
-                    new[] { input },
-                    new[] { output },
-                    null);
-                graph.Exit();
-            }
+            var input = tf.placeholder(tf.int32);
+            var output = func(input);
+
+            var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
+            graph.ToGraph(opers,
+                new[] { input },
+                new[] { output },
+                null);
+            graph.Exit();
             
 
             return (Tensor input) =>
@@ -42,21 +40,19 @@ namespace Tensorflow.Graphs
         {
             string func_name = $"{func.Method.Name}_{Guid.NewGuid()}";
 
-            // IntPtr func_handle;
-            using (var graph = new FuncGraph(func_name))
-            {
-                graph.as_default();
-                var input1 = tf.placeholder(tf.int32);
-                var input2 = tf.placeholder(tf.int32);
-                var output = func(input1, input2);
+            var graph = new FuncGraph(func_name);
+            graph.as_default();
+
+            var input1 = tf.placeholder(tf.int32);
+            var input2 = tf.placeholder(tf.int32);
+            var output = func(input1, input2);
 
-                var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
-                var func_handle = graph.ToGraph(opers,
-                    new[] { input1, input2 },
-                    new[] { output },
-                    null);
-                graph.Exit();
-            }
+            var opers = graph._nodes_by_name.Values.Select(x => x as Operation).ToArray();
+            graph.ToGraph(opers,
+                new[] { input1, input2 },
+                new[] { output },
+                null);
+            graph.Exit();
             
             return (Tensor a, Tensor b) =>
             {

src/TensorFlowNET.Core/Graphs/FuncGraph.cs

@@ -13,8 +13,7 @@ namespace Tensorflow.Graphs
     /// </summary>
     public class FuncGraph : Graph
     {
-        // _handle == IntPtr.Zero ? string.Empty : c_api.StringPiece(c_api.TF_FunctionName(_handle));
-        IntPtr func_handle;
+        IntPtr _func_graph_handle;
         public string FuncName => _graph_key;
 
         public Tensors Inputs { get; set; } = new Tensors();
@@ -60,12 +59,12 @@ namespace Tensorflow.Graphs
             _handle = handle;
         }
 
-        public IntPtr ToGraph(Operation[] opers,
+        public void ToGraph(Operation[] opers,
             Tensor[] inputs, Tensor[] outputs,
             string[] output_names)
         {
-            using var status = new Status();
-            func_handle = c_api.TF_GraphToFunction(_handle,
+            var status = new Status();
+            _func_graph_handle = c_api.TF_GraphToFunction(_handle,
                 _graph_key,
                 false,
                 opers.Length,
@@ -82,19 +81,17 @@ namespace Tensorflow.Graphs
 
             SetAttrs();
 
-            c_api.TF_GraphCopyFunction(outer_graph, func_handle, IntPtr.Zero, status.Handle);
-            status.Check(true);
+            // c_api.TF_GraphCopyFunction(outer_graph, _func_graph_handle, IntPtr.Zero, status.Handle);
+            // status.Check(true);
 
-            c_api.TFE_ContextAddFunction(tf.Context.Handle, func_handle, status.Handle);
+            c_api.TFE_ContextAddFunction(tf.Context.Handle, _func_graph_handle, status.Handle);
             status.Check(true);
 
-            _graph_key = c_api.StringPiece(c_api.TF_FunctionName(func_handle));
+            _graph_key = c_api.StringPiece(c_api.TF_FunctionName(_func_graph_handle));
 
             Inputs = inputs;
             // mark_as_return
             Outputs = outputs;// .Select(x => array_ops.identity(x)).ToArray();
-
-            return func_handle;
         }
 
         public override Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes, TF_DataType[] input_types = null, string name = null, Dictionary<string, AttrValue> attrs = null, OpDef op_def = null, bool compute_device = true)
@@ -233,7 +230,7 @@ namespace Tensorflow.Graphs
                 {
                     S = ByteString.CopyFromUtf8(attr_value)
                 }.ToByteArray();
-                c_api.TF_FunctionSetAttrValueProto(func_handle, _name, serialized, serialized.Length, tf.Status.Handle);
+                c_api.TF_FunctionSetAttrValueProto(_func_graph_handle, _name, serialized, serialized.Length, tf.Status.Handle);
                 tf.Status.Check(true);
             }
         }
@@ -250,5 +247,12 @@ namespace Tensorflow.Graphs
             tf.Context.restore_mode();
             ops.pop_graph();
         }
+
+        protected override void DisposeUnmanagedResources(IntPtr handle)
+        {
+            c_api.TFE_ContextRemoveFunction(tf.Context.Handle, _graph_key, tf.Status.Handle);
+            c_api.TF_DeleteFunction(_func_graph_handle);
+            base.DisposeUnmanagedResources(handle);
+        }
     }
 }

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -302,7 +302,7 @@ namespace Tensorflow
 
         public void device(string device_name)
         {
-            throw new NotImplementedException("");
+            
         }
 
         private void _create_op_helper(Operation op, bool compute_device = true)

src/TensorFlowNET.Core/Keras/Engine/INode.cs

@@ -8,7 +8,7 @@ namespace Tensorflow.Keras.Engine
     {
         Tensors input_tensors { get; }
         Tensors Outputs { get; }
-        ILayer Layer { get; set; }
+        ILayer Layer { get; }
         List<Tensor> KerasInputs { get; set; }
         INode[] ParentNodes { get; }
         IEnumerable<(ILayer, int, int, Tensor)> iterate_inbound();

src/TensorFlowNET.Core/Keras/Engine/KerasHistory.cs

@@ -11,14 +11,12 @@
         public int NodeIndex => node_index;
         int tensor_index;
         public int TensorIndex => tensor_index;
-        Tensor tensor;
 
-        public KerasHistory(ILayer layer, int node_index, int tensor_index, Tensor tensor)
+        public KerasHistory(ILayer layer, int node_index, int tensor_index)
         {
             this.layer = layer;
             this.node_index = node_index;
             this.tensor_index = tensor_index;
-            this.tensor = tensor;
         }
 
         public void Deconstruct(out ILayer layer, out int node_index, out int tensor_index)
@@ -29,6 +27,6 @@
         }
 
         public override string ToString()
-            => $"{layer.GetType().Name} {layer.Name} {tensor.name}";
+            => $"{layer.GetType().Name} {layer.Name}";
     }
 }

src/TensorFlowNET.Core/Keras/Layers/ILayer.cs

@@ -16,6 +16,8 @@ namespace Tensorflow.Keras
         List<IVariableV1> trainable_weights { get; }
         List<IVariableV1> non_trainable_weights { get; }
         TensorShape output_shape { get; }
+        TensorShape BatchInputShape { get; }
+        TF_DataType DType { get; }
         int count_params();
         LayerArgs get_config();
     }

src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs

@@ -72,6 +72,10 @@ namespace Tensorflow
 
         public TensorShape output_shape => throw new NotImplementedException();
 
+        public TensorShape BatchInputShape => throw new NotImplementedException();
+
+        public TF_DataType DType => throw new NotImplementedException();
+
         public RnnCell(bool trainable = true,
             string name = null,
             TF_DataType dtype = TF_DataType.DtInvalid,

src/TensorFlowNET.Core/Operations/control_flow_ops.cs

@@ -65,7 +65,7 @@ namespace Tensorflow
                 return gen_control_flow_ops.next_iteration(data, name: name);
         }
 
-        public static Operation Assert(Tensor condition, object[] data, long? summarize = null, string name = null)
+        public static Operation Assert(Tensor condition, object[] data, long summarize = 3, string name = null)
         {
             if (tf.executing_eagerly())
             {
@@ -82,7 +82,7 @@ namespace Tensorflow
                 condition = ops.convert_to_tensor(condition, name: "Condition");
                 Func<Operation[]> true_assert = () =>
                 {
-                    var assert = gen_logging_ops._assert(condition, data, summarize, name: "Assert");
+                    var assert = gen_logging_ops.assert(condition, data, summarize, name: "Assert");
                     return new Operation[] { assert };
                 };
 

src/TensorFlowNET.Core/Operations/gen_logging_ops.cs

@@ -21,10 +21,17 @@ namespace Tensorflow
 {
     public class gen_logging_ops
     {
-        public static Operation _assert(Tensor condition, object[] data, long? summarize = 3, string name = null)
+        public static Operation assert(Tensor condition, object[] data, long summarize = 3, string name = null)
         {
-            if (!summarize.HasValue)
-                summarize = 3;
+            if (tf.Context.executing_eagerly())
+            {
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "Assert", name,
+                    null,
+                    new object[] { condition, data, summarize });
+
+                return results[0];
+            }
 
             var _op = tf.OpDefLib._apply_op_helper("Assert", name, args: new { condition, data, summarize });
 

src/TensorFlowNET.Core/Operations/gen_resource_variable_ops.cs

@@ -68,7 +68,7 @@ namespace Tensorflow
                     null,
                     resource, value);
 
-                return results.Length == 0 ? null : results[0];
+                return null;
             }
 
             var _op = tf.OpDefLib._apply_op_helper("AssignVariableOp", name, new { resource, value });
@@ -113,7 +113,8 @@ namespace Tensorflow
                     "container", container,
                     "shared_name", shared_name,
                     "dtype", dtype,
-                    "shape", shape.dims);
+                    "shape", shape.dims,
+                    "allowed_devices", new string[0]);
 
                 return results[0];
             }
@@ -129,6 +130,28 @@ namespace Tensorflow
             return _op.output;
         }
 
+        public static Tensor destroy_resource_op(Tensor resource, bool ignore_lookup_error = true, string name = null)
+        {
+            if (tf.Context.executing_eagerly())
+            {
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "DestroyResourceOp", name,
+                    null,
+                    resource,
+                    "ignore_lookup_error", ignore_lookup_error);
+
+                return results.Length == 0 ? null : results[0];
+            }
+
+            var _op = tf.OpDefLib._apply_op_helper("DestroyResourceOp", name, new
+            {
+                resource,
+                ignore_lookup_error
+            });
+
+            return _op.output;
+        }
+
         /// <summary>
         /// Reads the value of a variable.
         /// </summary>

src/TensorFlowNET.Core/Operations/resource_variable_ops.cs

@@ -26,7 +26,7 @@ namespace Tensorflow
     /// </summary>
     public static class resource_variable_ops
     {
-        public static ITensorOrOperation shape_safe_assign_variable_handle(Tensor handle, int[] shape, Tensor value, string name = null)
+        public static Operation shape_safe_assign_variable_handle(Tensor handle, int[] shape, Tensor value, string name = null)
         {
             var value_tensor = ops.convert_to_tensor(value);
             return gen_resource_variable_ops.assign_variable_op(handle,
@@ -96,8 +96,10 @@ namespace Tensorflow
                 // We create an assert Op instead of checking right away in order to be
                 // compatible with ASYNC execution mode. Further, since not all devices
                 // support string tensors, we encode the assertion string in the Op name
-                /*gen_logging_ops._assert(
-                    math_ops.logical_not(exists), [exists], name = "EagerVariableNameReuse");*/
+                /*gen_logging_ops.assert(gen_math_ops.logical_not(exists),
+                    new[] { exists },
+                    name: "EagerVariableNameReuse");*/
+
                 var handle_data = new HandleData();
                 handle_data.IsSet = true;
                 handle_data.ShapeAndType.Add(new HandleShapeAndType

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -456,11 +456,6 @@ namespace Tensorflow
         private void _extend_graph()
         { }
 
-        public void close()
-        {
-            Dispose();
-        }
-
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
             lock (Locks.ProcessWide)

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

@@ -475,11 +475,11 @@ namespace Tensorflow
                 size += TF_StringEncodedSize((ulong)b.Length);
 
             ulong src_size = size + (ulong)buffer.Length * sizeof(ulong);
-            IntPtr handle = TF_AllocateTensor(TF_DataType.TF_STRING, shape, shape.Length, src_size);
+            _handle = TF_AllocateTensor(TF_DataType.TF_STRING, shape, shape.Length, src_size);
             AllocationType = AllocationType.Tensorflow;
 
             // Clear offset table
-            IntPtr input = TF_TensorData(handle);
+            IntPtr input = TensorDataPointer;
             IntPtr data_start = input + buffer.Length * sizeof(ulong);
             IntPtr limit = input + (int)src_size;
             ulong offset = 0;
@@ -496,7 +496,9 @@ namespace Tensorflow
                 }
             }
 
-            _handle = handle;
+#if TRACK_TENSOR_LIFE
+            print($"New Tensor 0x{_handle.ToString("x16")} {AllocationType} String Data: 0x{TensorDataPointer.ToString("x16")}");
+#endif
         }
 
         public unsafe Tensor(NDArray nd, TF_DataType? tensorDType = null)
@@ -563,12 +565,13 @@ namespace Tensorflow
             {
                 AllocationType = AllocationType.FromPointer;
                 AllocationHandle = arraySlice;
-#if TRACK_TENSOR_LIFE
-                print($"New Tensor {Id} {AllocationType} 0x{TensorDataPointer.ToString("x16")}");
-#endif
             }
             else
                 AllocationType = AllocationType.Tensorflow;
+
+#if TRACK_TENSOR_LIFE
+            print($"New Tensor 0x{_handle.ToString("x16")} {AllocationType} Data: 0x{TensorDataPointer.ToString("x16")}");
+#endif
         }
 
         public Tensor(Operation op, int value_index, TF_DataType dtype)

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

@@ -181,7 +181,6 @@ namespace Tensorflow
 
             storage.Allocate(new Shape(shape));
 
-            var bytesize = (long)this.bytesize;
             System.Buffer.MemoryCopy(buffer.ToPointer(), storage.Address, bytesize, bytesize);
 
             return new NDArray(storage);

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -24,6 +24,7 @@ using System.Runtime.InteropServices;
 using Tensorflow.Eager;
 using Tensorflow.Framework;
 using Tensorflow.Keras.Engine;
+using Tensorflow.Variables;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -260,12 +261,11 @@ namespace Tensorflow
         [SuppressMessage("ReSharper", "ConvertIfStatementToSwitchStatement")]
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
-            if (AllocationHandle != null)
-            {
-
 #if TRACK_TENSOR_LIFE
-                print($"Delete AllocationHandle.{AllocationType} 0x{TensorDataPointer.ToString("x16")}");
+            print($"Delete Tensor 0x{handle.ToString("x16")} {AllocationType} Data: 0x{TensorDataPointer.ToString("x16")}");
 #endif
+            if (AllocationHandle != null)
+            {
                 if (AllocationType == AllocationType.GCHandle)
                 {
                     ((GCHandle)AllocationHandle).Free();
@@ -287,17 +287,9 @@ namespace Tensorflow
                     throw new InvalidOperationException($"Tensor.AllocationHandle is not null ({AllocationHandle}) but AllocationType is not matched to a C# allocation type ({AllocationType}).");
             }
 
-#if TRACK_TENSOR_LIFE
-            print($"Delete TensorHandle 0x{handle.ToString("x16")}");
-#endif
             c_api.TF_DeleteTensor(handle);
         }
 
-        public virtual IntPtr ToPointer()
-            => _handle;
-
         public bool IsDisposed => _disposed;
-
-        // public int tensor_int_val { get; set; }
     }
 }

src/TensorFlowNET.Core/Tensors/Tensors.cs

@@ -43,6 +43,11 @@ namespace Tensorflow
             items.AddRange(tensors);
         }
 
+        public Tensors(IEnumerable<Tensor> tensors)
+        {
+            items.AddRange(tensors);
+        }
+
         public Tensors(NDArray nd)
         {
             items.Add(ops.convert_to_tensor(nd));

src/TensorFlowNET.Core/Training/Saving/ResourceVariableSaveable.cs

@@ -35,7 +35,7 @@ namespace Tensorflow
             this.name = name;
         }
 
-        public override ITensorOrOperation restore(Tensor[] restored_tensors, TensorShape[] restored_shapes = null)
+        public override Operation restore(Tensor[] restored_tensors, TensorShape[] restored_shapes = null)
         {
             var restored_tensor = restored_tensors[0];
             restored_tensor = array_ops.identity(restored_tensor);

src/TensorFlowNET.Core/Training/Saving/SaveableObject.cs

@@ -40,7 +40,7 @@ namespace Tensorflow
             this.name = name;
         }
 
-        public virtual ITensorOrOperation restore(Tensor[] restored_tensors, TensorShape[] restored_shapes = null)
+        public virtual Operation restore(Tensor[] restored_tensors, TensorShape[] restored_shapes = null)
         {
             var restored_tensor = restored_tensors[0];
             return gen_state_ops.assign(op,

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -1,5 +1,7 @@
 using NumSharp;
 using System;
+using Tensorflow.Eager;
+using Tensorflow.Variables;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -22,7 +24,6 @@ namespace Tensorflow
         public bool trainable => _trainable;
 
         protected Tensor _initial_value;
-        public Tensor initial_value => _initial_value;
 
         public Operation initializer => initializer_op;
 
@@ -44,16 +45,12 @@ namespace Tensorflow
         public Operation Op => handle.op;
         public Graph Graph => handle.graph;
         public string Device => handle.Device;
+        EagerResourceDeleter eager_resource_deleter;
 
         public BaseResourceVariable()
         {
         }
 
-        public BaseResourceVariable(IntPtr handle, IntPtr tensor)
-        {
-            _handle = handle;
-        }
-
         public void __init__(bool trainable = true,
             Tensor handle = null,
             string name = null,
@@ -66,7 +63,24 @@ namespace Tensorflow
             this.handle = handle;
             _name = name;
 
-            // handle_deleter
+            // After the handle has been created, set up a way to clean it up when
+            // executing eagerly. We'll hold the only reference to the deleter, so that
+            // when this object is garbage collected the deleter will be too. This
+            // means ResourceVariables can be part of reference cycles without those
+            // cycles being uncollectable.
+            if (handle.IsEagerTensor)
+            {
+                _handle = handle.EagerTensorHandle.DangerousGetHandle();
+                eager_resource_deleter = new EagerResourceDeleter(handle, handle.Device);
+            }
+            else
+            {
+                _handle = handle;
+            }
+
+#if TRACK_TENSOR_LIFE
+            print($"Created Resource 0x{_handle.ToString("x16")} {_name}");
+#endif
         }
 
         public Tensor assign<T>(T value, bool use_locking = false, string name = null, bool read_value = true)
@@ -85,7 +99,7 @@ namespace Tensorflow
 
             if (read_value)
                 return gen_resource_variable_ops.read_variable_op(handle, dtype);
-            
+
             return assign_op;
         }
 
@@ -214,6 +228,9 @@ namespace Tensorflow
 
         protected override void DisposeUnmanagedResources(IntPtr handle)
         {
+#if TRACK_TENSOR_LIFE
+            print($"Deleted Resource 0x{handle.ToString("x16")} {_name}");
+#endif
         }
 
         public Tensor AsTensor(TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)

src/TensorFlowNET.Core/Variables/EagerResourceDeleter.cs

@@ -0,0 +1,34 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Variables
+{
+    public class EagerResourceDeleter : DisposableObject
+    {
+        Tensor _tensor;
+        string _handle_device;
+        public EagerResourceDeleter(Tensor handle, string handle_device)
+        {
+            _tensor = handle;
+            _handle = handle.EagerTensorHandle.DangerousGetHandle();
+            _handle_device = handle_device;
+            
+            bool success = false;
+            handle.EagerTensorHandle.DangerousAddRef(ref success);
+        }
+
+        protected override void DisposeUnmanagedResources(IntPtr handle)
+        {
+            // gen_resource_variable_ops.destroy_resource_op(_tensor, ignore_lookup_error: true);
+
+            tf.device(_handle_device);
+            tf.Runner.TFE_Execute(tf.Context, _handle_device, "DestroyResourceOp",
+                new[] { _tensor },
+                new object[] { "ignore_lookup_error", true }, 0);
+            
+            _tensor.EagerTensorHandle.DangerousRelease();
+        }
+    }
+}

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -344,7 +344,7 @@ namespace Tensorflow
                 return assign;
             return assign.op;
         }
-
+        
         public override string ToString()
         {
             return $"tf.RefVariable '{Name}' shape={shape} dtype={dtype}";

src/TensorFlowNET.Core/Variables/ResourceVariable.cs

@@ -78,8 +78,7 @@ namespace Tensorflow
             tf_with(ops.init_scope(), init_scope =>
             {
                 _in_graph_mode = !tf.Context.executing_eagerly();
-                var values = init_from_fn ? new object[0] : new object[] { initial_value };
-                tf_with(ops.name_scope(name, "Variable", values, skip_on_eager: false), scope =>
+                tf_with(ops.name_scope(name, "Variable", initial_value, skip_on_eager: false), scope =>
                 {
                     name = scope;
                     var handle_name = ops.name_from_scope_name(name);
@@ -103,19 +102,17 @@ namespace Tensorflow
                     tf_with(ops.name_scope("Initializer"), delegate
                     {
                         if (initial_value.GetType().GetInterface("IInitializer") != null)
-                            initial_value = ops.convert_to_tensor((initial_value as IInitializer).Apply(new InitializerArgs(shape, dtype: dtype)));
+                            _initial_value = ops.convert_to_tensor((initial_value as IInitializer).Apply(new InitializerArgs(shape, dtype: dtype)));
                         else
                         {
                             var value = init_from_fn ? (initial_value as Func<Tensor>)() : initial_value;
-                            initial_value = ops.convert_to_tensor(value,
+                            _initial_value = ops.convert_to_tensor(value,
                                 name: "initial_value",
                                 dtype: dtype);
                         }
                     });
-                    _shape = shape ?? (initial_value as Tensor).TensorShape;
-                    _initial_value = initial_value as Tensor;
-
 
+                    _shape = shape ?? _initial_value.TensorShape;
 
                     if (_in_graph_mode)
                     {
@@ -141,7 +138,7 @@ namespace Tensorflow
                         initializer_op = null;
                         _graph_element = null;
                         _dtype = _initial_value.dtype.as_base_dtype();
-                        initial_value = _in_graph_mode ? initial_value : null;
+                        // initial_value = _in_graph_mode ? initial_value : null;
                     }
 
                     base.__init__(trainable: trainable,

src/TensorFlowNET.Core/Variables/gen_state_ops.py.cs

@@ -60,6 +60,18 @@ namespace Tensorflow
             bool use_locking = true,
             string name = null)
         {
+            if (tf.executing_eagerly())
+            {
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "Assign", name,
+                    null,
+                    @ref, value,
+                    "validate_shape", validate_shape,
+                    "use_locking", use_locking);
+
+                return results[0];
+            }
+
             var _op = tf.OpDefLib._apply_op_helper("Assign", name: name, args: new { @ref, value, validate_shape, use_locking });
 
             var _result = _op.outputs;

src/TensorFlowNET.Core/ops.cs

@@ -101,7 +101,7 @@ namespace Tensorflow
             foreach (var op_input in op_input_list)
             {
                 // Determine if this is a valid graph_element.
-                var graph_element = op_input;
+                // var graph_element = op_input;
             }
 
             return get_default_graph();

src/TensorFlowNET.Core/ops.name_scope.cs

@@ -79,7 +79,7 @@ namespace Tensorflow
                     return (null, null);
 
                 if (name == null)
-                    name = "";
+                    name = _default_name;
 
                 var scope_name = name;
                 var old_name = ctx.ScopeName;

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -118,10 +118,20 @@ namespace Tensorflow.Keras
         {
             tf.Context.reset_context();
             reset_uids();
-            ops.set_default_session(tf.Session(ops.get_default_graph()));
             // var phase = tf.placeholder_with_default(false, new int[] { }, name: "keras_learning_phase");
-            _GRAPH_LEARNING_PHASES = new Dictionary<Graph, GraphLearningPhase>();
-            _GRAPH_LEARNING_PHASES[tf.get_default_graph()] = 0;
+            if (_GRAPH_LEARNING_PHASES != null)
+                _GRAPH_LEARNING_PHASES.Clear();
+            if (_GRAPH_LEARNING_PHASES != null)
+                _GRAPH_LEARNING_PHASES.Clear();
+            PER_GRAPH_LAYER_NAME_UIDS.Clear();
+            _CURRENT_SCRATCH_GRAPH = null;
+            _GRAPH = null;
+            
+            ops.set_default_session(tf.Session(ops.get_default_graph()));
+            tf.enable_eager_execution();
+
+            GC.Collect();
+            GC.WaitForPendingFinalizers();
         }
         public void manual_variable_initialization(bool value)
         {
@@ -147,7 +157,7 @@ namespace Tensorflow.Keras
             if (ops.executing_eagerly_outside_functions())
             {
                 foreach (var (x, value) in tuples)
-                    x.assign(value);
+                    x.assign(value, read_value: false);
             }
             else
             {

src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs

@@ -93,13 +93,11 @@ namespace Tensorflow.Keras.Engine.DataAdapters
 
         public IEnumerable<(int, OwnedIterator)> enumerate_epochs()
         {
-            var data_iterator = new OwnedIterator(_dataset);
             foreach (var epoch in range(_initial_epoch, _epochs))
             {
                 if (_insufficient_data)
                     break;
-                if (_adapter.ShouldRecreateIterator())
-                    data_iterator = new OwnedIterator(_dataset);
+                using var data_iterator = new OwnedIterator(_dataset);
                 yield return (epoch, data_iterator);
             }
         }

src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs

@@ -72,8 +72,8 @@ namespace Tensorflow.Keras.Engine.DataAdapters
 
         IDatasetV2 slice_inputs(IDatasetV2 indices_dataset, Tensors elements)
         {
-            var dataset2 = tf.data.Dataset.from_tensor(elements).repeat();
-            var dataset = tf.data.Dataset.zip(indices_dataset, dataset2);
+            var dataset = tf.data.Dataset.from_tensor(elements).repeat();
+            dataset = tf.data.Dataset.zip(indices_dataset, dataset);
 
             dataset = dataset.map(inputs =>
             {
@@ -84,9 +84,7 @@ namespace Tensorflow.Keras.Engine.DataAdapters
                 return new Tensors(results);
             });
 
-            dataset = dataset.with_options(new DatasetOptions { });
-
-            return dataset;
+            return dataset.with_options(new DatasetOptions { });
         }
 
         public override int GetSize()

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -64,7 +64,7 @@ namespace Tensorflow.Keras.Engine
             {
                 var (layer, node_index, tensor_index) = x.KerasHistory;
                 _output_layers.append(layer);
-                _output_coordinates.append(new KerasHistory(layer, node_index, tensor_index, x));
+                _output_coordinates.append(new KerasHistory(layer, node_index, tensor_index));
             }
 
             // Build self._input_layers:
@@ -72,7 +72,7 @@ namespace Tensorflow.Keras.Engine
             {
                 var (layer, node_index, tensor_index) = x.KerasHistory;
                 _input_layers.append(layer);
-                _input_coordinates.append(new KerasHistory(layer, node_index, tensor_index, x));
+                _input_coordinates.append(new KerasHistory(layer, node_index, tensor_index));
             }
 
             // Keep track of the network's nodes and layers.

src/TensorFlowNET.Keras/Engine/Layer.Apply.cs

@@ -40,7 +40,8 @@ namespace Tensorflow.Keras.Engine
 
                 outputs = Call(inputs, state: state, is_training: is_training);
 
-                outputs = _set_connectivity_metadata_(inputs, outputs);
+                // memory leak
+                // _set_connectivity_metadata_(inputs, outputs);
                 _handle_activity_regularization(inputs, outputs);
                 _set_mask_metadata(inputs, outputs, null);
             });

src/TensorFlowNET.Keras/Engine/Layer.FunctionalConstructionCall.cs

@@ -40,8 +40,8 @@ namespace Tensorflow.Keras.Engine
                     throw new NotImplementedException("");
 
                 outputs = Call(inputs);
-
-                outputs = _set_connectivity_metadata_(inputs, outputs);
+                
+                _set_connectivity_metadata_(inputs, outputs);
                 _handle_activity_regularization(inputs, outputs);
                 _set_mask_metadata(inputs, outputs, null);
             });

src/TensorFlowNET.Keras/Engine/Layer.cs

@@ -120,15 +120,14 @@ namespace Tensorflow.Keras.Engine
         public void SetConnectivityMetadata(Tensors inputs, Tensors outputs)
             => _set_connectivity_metadata_(inputs, outputs);
 
-        private Tensors _set_connectivity_metadata_(Tensors inputs, Tensors outputs)
+        private void _set_connectivity_metadata_(Tensors inputs, Tensors outputs)
         {
-            new Node(this, new NodeArgs
+            var node = new Node(new NodeArgs
             {
                 InputTensors = inputs,
                 Outputs = outputs
             });
-
-            return outputs;
+            node.Connect(this);
         }
 
         private void _handle_activity_regularization(Tensors inputs, Tensors outputs)

src/TensorFlowNET.Keras/Engine/Model.Fit.cs

@@ -99,6 +99,9 @@ namespace Tensorflow.Keras.Engine
                     var result_pairs = string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2:F6}"));
                     Console.WriteLine($"Epoch: {epoch + 1:D3}/{epochs:D3}, Step: {step + 1:D4}/{data_handler.Inferredsteps:D4}, {result_pairs}");
                 }
+
+                GC.Collect();
+                GC.WaitForPendingFinalizers();
             }
         }
     }

src/TensorFlowNET.Keras/Engine/Model.Predict.cs

@@ -59,6 +59,8 @@ namespace Tensorflow.Keras.Engine
                     var end_step = step + data_handler.StepIncrement;
                     // callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
                 }
+                GC.Collect();
+                GC.WaitForPendingFinalizers();
             }
             // callbacks.on_predict_end()
             return outputs;

src/TensorFlowNET.Keras/Engine/Node.cs

@@ -40,7 +40,8 @@ namespace Tensorflow.Keras.Engine
         public TensorShape[] input_shapes;
         public TensorShape[] output_shapes;
         public List<Tensor> KerasInputs { get; set; } = new List<Tensor>();
-        public ILayer Layer { get; set; }
+        ILayer _layer;
+        public ILayer Layer => _layer;
         public bool is_input => args.InputTensors == null;
         public long[] FlatInputIds { get; set; }
         public long[] FlatOutputIds { get; set; }
@@ -61,19 +62,24 @@ namespace Tensorflow.Keras.Engine
             }
         }
 
-        public Node(Layer layer, NodeArgs args)
+        public Node(NodeArgs args)
         {
             this.args = args;
-            this.Layer = layer;
+        }
+
+        public void Connect(Layer layer)
+        {
+            _layer = layer;
 
             if (args.InputTensors != null)
                 KerasInputs.AddRange(args.InputTensors);
-
+            
             foreach (var (i, ele) in enumerate(KerasInputs))
                 _keras_inputs_ids_and_indices[i] = ele.Id;
 
             // Wire up Node to Layers.
             layer.InboundNodes.Add(this);
+            
             foreach (var kt in KerasInputs)
             {
                 if (kt.KerasHistory == null)
@@ -86,7 +92,7 @@ namespace Tensorflow.Keras.Engine
             // Set metadata on outputs.
             var node_index = layer.InboundNodes.Count - 1;
             foreach (var (i, tensor) in enumerate(Outputs))
-                tensor.KerasHistory = new KerasHistory(layer, node_index, i, tensor);
+                tensor.KerasHistory = new KerasHistory(layer, node_index, i);
 
             // Cached for performance.
             FlatInputIds = KerasInputs.Select(x => x.Id).ToArray();

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -64,7 +64,7 @@ namespace Tensorflow.Keras.Engine
 
         public void add(Tensor tensor)
         {
-            var layer = tensor.KerasHistory.Layer as Layer;
+            var layer = tensor.KerasHistory.Layer;
             add(layer);
         }
 
@@ -72,7 +72,7 @@ namespace Tensorflow.Keras.Engine
         /// Adds a layer instance on top of the layer stack.
         /// </summary>
         /// <param name="layer"></param>
-        public void add(Layer layer)
+        public void add(ILayer layer)
         {
             built = false;
             var set_inputs = false;

src/TensorFlowNET.Keras/Layers/Core/InputLayer.cs

@@ -90,10 +90,11 @@ namespace Tensorflow.Keras.Layers
             // and set output_tensors' _keras_history.
             // input_tensor._keras_history = base_layer.KerasHistory(self, 0, 0)
             // input_tensor._keras_mask = None
-            new Node(this, new NodeArgs
+            var node = new Node(new NodeArgs
             {
                 Outputs = args.InputTensor
             });
+            node.Connect(this);
 
             typeSpec = new TensorSpec(args.InputTensor.TensorShape,
                 dtype: args.InputTensor.dtype,

src/TensorFlowNET.Keras/Utils/base_layer_utils.cs

@@ -42,15 +42,13 @@ namespace Tensorflow.Keras.Utils
             Func<Tensor> init_val = () => args.Initializer.Apply(new InitializerArgs(args.Shape, dtype: args.DType));
 
             var variable_dtype = args.DType.as_base_dtype();
-            var v = tf.Variable(init_val,
+            return tf.Variable(init_val,
                 dtype: variable_dtype,
                 shape: args.Shape,
                 name: args.Name,
                 trainable: args.Trainable,
                 validate_shape: args.ValidateShape,
                 use_resource: args.UseResource);
-
-            return v;
         }
 
         /// <summary>

src/TensorFlowNet.Benchmarks/Leak/GpuLeakByCNN.cs

@@ -51,6 +51,8 @@ namespace Tensorflow.Benchmark.Leak
              metrics: new[] { "accuracy" });
 
             model.fit(inputImages, outLables, batch_size: 32, epochs: 200);
+
+            keras.backend.clear_session();
         }
     }
 }

test/TensorFlowNET.Native.UnitTest/CApiAttributesTestcs.cs → test/TensorFlowNET.Native.UnitTest/Attributes/AttributesTestcs.cs

@@ -8,13 +8,13 @@ namespace Tensorflow.Native.UnitTest
     /// `class CApiAttributesTest`
     /// </summary>
     [TestClass]
-    public class CApiAttributesTestcs : CApiTest, IDisposable
+    public class AttributesTestcs : CApiTest, IDisposable
     {
         private Graph graph_;
         private int counter_;
         private Status s_;
 
-        public CApiAttributesTestcs()
+        public AttributesTestcs()
         {
             s_ = new Status();
             graph_ = new Graph();

test/TensorFlowNET.Native.UnitTest/CApiFunctionTest.cs → test/TensorFlowNET.Native.UnitTest/Functions/FunctionTest.cs

@@ -11,7 +11,7 @@ namespace Tensorflow.Native.UnitTest
     /// `class CApiColocationTest`
     /// </summary>
     [TestClass]
-    public class CApiFunctionTest : CApiTest, IDisposable
+    public class FunctionTest : CApiTest, IDisposable
     {
         Graph func_graph_;
         Graph host_graph_;

test/TensorFlowNET.Native.UnitTest/CApiGradientsTest.cs → test/TensorFlowNET.Native.UnitTest/Gradients/GradientsTest.cs

@@ -10,7 +10,7 @@ namespace Tensorflow.Native.UnitTest
     /// `class CApiGradientsTest`
     /// </summary>
     [TestClass]
-    public class CApiGradientsTest : CApiTest, IDisposable
+    public class GradientsTest : CApiTest, IDisposable
     {
         private Graph graph_ = new Graph();
         private Graph expected_graph_ = new Graph();

test/TensorFlowNET.UnitTest/Basics/VariableTest.cs

@@ -48,6 +48,16 @@ namespace TensorFlowNET.UnitTest.Basics
             Assert.AreEqual(11f, (float)v1.numpy());
         }
 
+        [TestMethod]
+        public void Assign3()
+        {
+            var v1 = tf.Variable(10.0f, name: "v1");
+            var v2 = tf.Variable(v1, name: "v2");
+            Assert.AreEqual(v1.numpy(), v2.numpy());
+            v1.assign(30.0f);
+            Assert.AreNotEqual(v1.numpy(), v2.numpy());
+        }
+
         /// <summary>
         /// Assign tensor to slice of other tensor.
         /// https://www.tensorflow.org/api_docs/python/tf/Variable#__getitem__