Merge branch 'master' of https://github.com/SciSharp/TensorFlow.NET

.gitignore

@@ -332,3 +332,7 @@ src/TensorFlowNET.Native/bazel-*
 src/TensorFlowNET.Native/c_api.h
 /.vscode
 test/TensorFlowNET.Examples/mnist
+
+
+# training model resources
+.resources

README.md

@@ -28,8 +28,14 @@ In comparison to other projects, like for instance TensorFlowSharp which only pr
 
 Install TF.NET and TensorFlow binary through NuGet.
 ```sh
+### install tensorflow C# binding
 PM> Install-Package TensorFlow.NET
+
+### Install tensorflow binary
+### For CPU version
 PM> Install-Package SciSharp.TensorFlow.Redist
+### For GPU version (CUDA and cuDNN are required)
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 ```
 
 Import TF.NET.

TensorFlow.NET.sln

@@ -17,7 +17,11 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowBenchmark", "src\
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowHub", "src\TensorFlowHub\TensorFlowHub.csproj", "{8FD59A5A-97EB-457E-B9F1-D88B0C822C6E}"
 EndProject
-Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TensorFlowText", "src\TensorFlowText\TensorFlowText.csproj", "{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowText", "src\TensorFlowText\TensorFlowText.csproj", "{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TensorFlowDatasets", "src\TensorFlowDatasets\TensorFlowDatasets.csproj", "{DF151A51-E9FD-41BD-B0F4-08A743755D44}"
+EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples.GPU", "test\TensorFlowNET.Examples\TensorFlowNET.Examples.GPU.csproj", "{6F6B3382-8F87-4CD9-BF87-C81D5405685A}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -57,6 +61,14 @@ Global
 		{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}.Release|Any CPU.Build.0 = Release|Any CPU
+		{DF151A51-E9FD-41BD-B0F4-08A743755D44}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{DF151A51-E9FD-41BD-B0F4-08A743755D44}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{DF151A51-E9FD-41BD-B0F4-08A743755D44}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{DF151A51-E9FD-41BD-B0F4-08A743755D44}.Release|Any CPU.Build.0 = Release|Any CPU
+		{6F6B3382-8F87-4CD9-BF87-C81D5405685A}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{6F6B3382-8F87-4CD9-BF87-C81D5405685A}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{6F6B3382-8F87-4CD9-BF87-C81D5405685A}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{6F6B3382-8F87-4CD9-BF87-C81D5405685A}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

src/KerasNET.Core/Model.cs

@@ -115,7 +115,7 @@ namespace Keras
             var init = tf.global_variables_initializer();
 
             float loss_value = 0;
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -133,7 +133,7 @@ namespace Keras
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }

src/SciSharp.TensorFlow.Redist/README.md

@@ -1,8 +1,14 @@
 ## SciSharp.TensorFlow.Redist ##
 
 
-`SciSharp.TensorFlow.Redist` is  a migration from [Microsoft.ML.TensorFlow.Redist](https://github.com/dotnet/machinelearning/tree/release/1.2/src/Redist/Microsoft.ML.TensorFlow.Redist). [ML.NET](https://github.com/dotnet/machinelearning) team will not maintain the package since [ML.NET](https://www.nuget.org/packages/Microsoft.ML) v1.4.0 going forward.
+`SciSharp.TensorFlow.Redist` is  a migration from [Microsoft.ML.TensorFlow.Redist](https://github.com/dotnet/machinelearning/tree/release/1.2/src/Redist/Microsoft.ML.TensorFlow.Redist). [ML.NET](https://github.com/dotnet/machinelearning) team will not maintain the package since [ML.NET](https://www.nuget.org/packages/Microsoft.ML) v1.3.0 going forward.
 
+* CPU version for all platforms (Windows, Linux, OSX)
+```powershell
+PM> Install-Package SciSharp.TensorFlow.Redist
+```
+
+* GPU version for Windows
 ```powershell
 PM> Install-Package SciSharp.TensorFlow.Redist
 ```
@@ -16,7 +22,7 @@ Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5
 On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries.
 
 1. Run `dotnet pack` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
-2. Run `nuget push SciSharp.TensorFlow.Redist.1.14.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
+2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.1.14.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
 
 
 

src/SciSharp.TensorFlow.Redist/Redist.nuspec → src/SciSharp.TensorFlow.Redist/Redist-CPU.nuspec

@@ -9,7 +9,7 @@
     <license type="file">LICENSE.txt</license>
     <licenseUrl>https://aka.ms/deprecateLicenseUrl</licenseUrl>
     <projectUrl>https://www.tensorflow.org/</projectUrl>
-    <description>$packageId$ contains the TensorFlow C library version $version$ redistributed as a NuGet package.</description>
+    <description>$packageId$ contains the TensorFlow C library CPU version $version$ redistributed as a NuGet package.</description>
     <releaseNotes>https://github.com/tensorflow/tensorflow/releases/tag/v$version$</releaseNotes>
     <copyright>Copyright 2019 The TensorFlow Authors. All rights reserved.</copyright>
     <tags>TensorFlow</tags>

src/SciSharp.TensorFlow.Redist/Redist-Windows-GPU.nuspec

@@ -0,0 +1,26 @@
+<?xml version="1.0" encoding="utf-8"?>
+<package xmlns="http://schemas.microsoft.com/packaging/2012/06/nuspec.xsd">
+  <metadata>
+    <id>$packageId$</id>
+    <version>$version$</version>
+    <authors>The TensorFlow Authors</authors>
+    <owners>The TensorFlow Authors</owners>
+    <requireLicenseAcceptance>true</requireLicenseAcceptance>
+    <license type="file">LICENSE.txt</license>
+    <licenseUrl>https://aka.ms/deprecateLicenseUrl</licenseUrl>
+    <projectUrl>https://www.tensorflow.org/</projectUrl>
+    <description>$packageId$ contains the TensorFlow C library GPU version $version$ redistributed as a NuGet package.</description>
+    <releaseNotes>https://github.com/tensorflow/tensorflow/releases/tag/v$version$</releaseNotes>
+    <copyright>Copyright 2019 The TensorFlow Authors. All rights reserved.</copyright>
+    <tags>TensorFlow</tags>
+    <dependencies>
+      <group targetFramework=".NETStandard2.0" />
+    </dependencies>
+  </metadata>
+  <files>
+    <file src="CommonPackage.props" target="build\netstandard2.0\$packageId$.props" />
+    <file src="bin\packages\$packageId$\LICENSE.txt" target="LICENSE.txt" />
+    <file src="bin\packages\$packageId$\THIRD_PARTY_NOTICES.txt" target="THIRD_PARTY_NOTICES.txt" />
+    <file src="bin\packages\$packageId$\runtimes\**\*" target="runtimes" />
+  </files>
+</package>

src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj → src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-CPU.nupkgproj

@@ -17,7 +17,7 @@
     <NoBuild>true</NoBuild>
     <IncludeBuildOutput>false</IncludeBuildOutput>
 
-    <NuspecFile>Redist.nuspec</NuspecFile>
+    <NuspecFile>Redist-CPU.nuspec</NuspecFile>
     <NuspecProperties>packageId=$(PackageId);version=$(PackageVersion)</NuspecProperties>
     <NuspecBasePath>$(ProjDir)</NuspecBasePath>
 

src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Windows-GPU.nupkgproj

@@ -0,0 +1,187 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  
+  <PropertyGroup>
+    <ProjDir>$(MSBuildThisFileDirectory)</ProjDir>
+    <BinDir>$(ProjDir)bin\</BinDir>
+    <ObjDir>$(ProjDir)obj\</ObjDir>
+
+    <TargetArchitecture Condition="'$(TargetArchitecture)' == ''">x64</TargetArchitecture>
+    <TargetFramework>netstandard2.0</TargetFramework>
+    <TensorFlowVersion>1.14.0</TensorFlowVersion>
+    <TensorFlowMajorVersion>1</TensorFlowMajorVersion>
+
+    <PackageAssetsPath>$(BinDir)packages\</PackageAssetsPath>
+    <PackageId>$(MSBuildProjectName)</PackageId>
+    <PackageVersion>$(TensorFlowVersion)</PackageVersion>
+
+    <NoBuild>true</NoBuild>
+    <IncludeBuildOutput>false</IncludeBuildOutput>
+
+    <NuspecFile>Redist-Windows-GPU.nuspec</NuspecFile>
+    <NuspecProperties>packageId=$(PackageId);version=$(PackageVersion)</NuspecProperties>
+    <NuspecBasePath>$(ProjDir)</NuspecBasePath>
+
+    <GenerateNuspecDependsOn>CopyFilesFromArchive</GenerateNuspecDependsOn>
+
+    <PackageRid Condition="'$(OS)' == 'Windows_NT'">win</PackageRid>
+    <PackageRid Condition="'$(OS)' != 'Windows_NT'">linux</PackageRid>
+    <PackageRid Condition="$([MSBuild]::IsOSPlatform('osx'))">osx</PackageRid>
+    <PackageRid>$(PackageRid)-$(TargetArchitecture)</PackageRid>
+
+  </PropertyGroup>
+
+  <PropertyGroup>
+    <IncludeMLNetNotices>false</IncludeMLNetNotices>
+  </PropertyGroup>
+  
+  <ItemGroup>
+    <TensorFlowConfig Include="windows"
+                      FileExtension=".zip"
+                      FilesFromArchive="lib\tensorflow.dll;
+                                        include\tensorflow\c\LICENSE"
+                      Runtime="win-x64"/>
+
+    <TensorFlowConfig Condition="'$(OS)' != 'Windows_NT'"
+                      Include="linux"
+                      FileExtension=".tar.gz"
+                      FilesFromArchive="lib\libtensorflow.so;
+                                        lib\libtensorflow_framework.so.$(TensorFlowMajorVersion);
+                                        include\tensorflow\c\LICENSE"
+                      Runtime="linux-x64" />
+
+    <TensorFlowConfig Condition="'$(OS)' != 'Windows_NT'"
+                      Include="darwin" FileExtension=".tar.gz"
+                      FilesFromArchive="lib\libtensorflow.dylib;
+                                        lib\libtensorflow_framework.$(TensorFlowMajorVersion).dylib;
+                                        include\tensorflow\c\LICENSE"
+                      Runtime="osx-x64" />
+
+    <AdditionalDownloadFile Include="https://raw.githubusercontent.com/tensorflow/tensorflow/master/LICENSE"
+                            DownloadFile="$(BinDir)LICENSE" />
+  </ItemGroup>
+
+  <Target Name="PrepareArchives">
+    <ItemGroup>
+      <!-- although we could extract all archives on all machines, mac requires a fixup which can only be run on mac 
+           so we split these per-rid and join during the official build packaging.  -->
+      <TensorFlowArchive
+                         Include="@(TensorFlowConfig->'https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-%(Identity)-x86_64-$(TensorFlowVersion)%(FileExtension)')" />
+      <!-- set up metdata used by all targets -->
+      <TensorFlowArchive DownloadFile="$(BinDir)%(FileName)%(Extension)"
+                         DownloadShaFile="$(BinDir)%(FileName)%(Extension).sha"
+                         ExtractDirectory="$(BinDir)%(FileName)"
+                         ExtractSemaphore="$(BinDir)%(FileName)\.extracted"
+                         LocalShaFile="$(MSBuildProjectDirectory)\%(FileName)%(Extension).sha"/>
+    </ItemGroup>
+    <Message Importance="high" Text="%(TensorFlowConfig.Runtime)"/>
+  </Target>
+
+  <Target Name="DownloadArchives"
+          DependsOnTargets="PrepareArchives"
+          Inputs="$(MSBuildProjectFile)"
+          Outputs="@(TensorFlowArchive->'%(DownloadFile)');@(AdditionalDownloadFile->'%(DownloadFile)')">
+    <MakeDir Directories="$(BinDir)" />
+    <ItemGroup>
+      <_downloadFiles Include="@(TensorFlowArchive);@(AdditionalDownloadFile)" Url="%(Identity)" DestinationFile="%(DownloadFile)" />
+    </ItemGroup>
+    <Message Importance="High" Text="Downloading '%(_downloadFiles.Identity)' to '$(BinDir)'." />
+    <DownloadFile SourceUrl="%(_downloadFiles.Identity)" DestinationFolder="$(BinDir)">
+      <Output TaskParameter="DownloadedFile" ItemName="Content" />
+    </DownloadFile>
+  </Target>
+
+
+  <Target Name="ValidateAndExtractArchives"
+          DependsOnTargets="DownloadArchives"
+          Inputs="@(TensorFlowArchive->'%(DownloadFile)')"
+          Outputs="@(TensorFlowArchive->'%(ExtractSemaphore)')">
+
+    <GetFileHash Files="@(TensorFlowArchive->'%(DownloadFile)')" Algorithm="SHA512">
+      <Output
+          TaskParameter="Items"
+          ItemName="FilesWithHashes" />
+    </GetFileHash>
+
+    <WriteLinesToFile File="%(FilesWithHashes.Identity).sha" Lines="%(FilesWithHashes.FileHash)" Overwrite="true"/>
+
+    <!-- If specified we'll update the checked in SHAs with the downloaded ones. -->
+    <Copy Condition="'$(UpdateSHA)' == 'true'"
+          SourceFiles="@(TensorFlowArchive->'%(DownloadShaFile)')"
+          DestinationFiles="@(TensorFlowArchive->'%(LocalShaFile)')" />
+
+    <ItemGroup>
+      <TensorFlowArchive>
+        <DownloadSha>@(FilesWithHashes->'%(FileHash)')</DownloadSha>
+        <LocalSha>$([System.IO.File]::ReadAllText('%(LocalShaFile)').Replace("%0A", "").Replace("%0D", ""))</LocalSha>
+      </TensorFlowArchive>
+    </ItemGroup>
+
+    <Error Condition="!Exists('%(TensorFlowArchive.LocalShaFile)')" Text="SHA file '%(TensorFlowArchive.LocalShaFile)' does not exist.  Build with /p:UpdateSHA=true to save it." />
+
+    <Message Importance="High" Text="@TensorFlowArchive->'%(TensorFlowArchive.DownloadFile) - %(TensorFlowArchive.LocalSha) - %(TensorFlowArchive.DownloadSha)"/>
+
+    <!-- Validate that the downloaded SHAs match the expected checked in SHAs -->
+    <Error Condition="'%(TensorFlowArchive.LocalSha)' != '%(TensorFlowArchive.DownloadSha)'" Text="Downloaded file '%(TensorFlowArchive.DownloadFile)' has unexpected SHA.%0A  expected: %(TensorFlowArchive.LocalSha)%0A  --actual: %(TensorFlowArchive.DownloadSha)%0ABuild with /p:UpdateSHA=true if you intentionally changed the URL and wish to update the SHAs, otherwise this could indicate an incomplete download or intercerpted URL and should be examined." />
+
+
+    <!-- The archives are valid, lets extract them, ensuring an empty directory -->
+    <RemoveDir Directories="@(TensorFlowArchive->'%(ExtractDirectory)')" />
+    <MakeDir Directories="@(TensorFlowArchive->'%(ExtractDirectory)')" />
+
+    <Message Importance="High" Text="Decompressing '%(TensorFlowArchive.DownloadFile)' to '%(TensorFlowArchive.ExtractDirectory)'." />
+
+    <Unzip Condition="'%(TensorFlowArchive.FileExtension)' == '.zip'"
+                               SourceFiles="%(TensorFlowArchive.DownloadFile)"
+                               DestinationFolder="%(TensorFlowArchive.ExtractDirectory)" />
+
+    <Exec Condition="'$(OS)' != 'Windows_NT' AND '%(TensorFlowArchive.FileExtension)' == '.tar.gz'"
+          WorkingDirectory="$(MSBuildThisFileDirectory)"
+          Command="tar -xzm --hard-dereference -f %(TensorFlowArchive.DownloadFile) -C %(TensorFlowArchive.ExtractDirectory)" />
+
+    <Exec Condition="'$(OS)' != 'Windows_NT'"
+          Command="chmod -R +w %(TensorFlowArchive.ExtractDirectory)" />
+
+    <Touch Files="@(TensorFlowArchive->'%(ExtractSemaphore)')" AlwaysCreate="true" />
+  </Target>
+
+  <!-- Select the files we want to copy out of each archive. -->
+  <Target Name="GetFilesFromArchive"
+          DependsOnTargets="ValidateAndExtractArchives" >
+    <ItemGroup>
+      <!-- batch rather than transform so that we can split FilesFromArchive metadata -->
+      <_fileFromArchive Include="%(TensorFlowArchive.FilesFromArchive)" ExtractDirectory="%(TensorFlowArchive.ExtractDirectory)" Runtime="%(TensorFlowArchive.Runtime)" />
+      <_fileFromArchive DestinationFile="%(FileName)%(Extension)"/>
+      <_fileFromArchive PackagePath="runtimes\%(_fileFromArchive.Runtime)\native\%(_fileFromArchive.DestinationFile)" />
+
+      <!-- LICENSE from the package is actually THIRD_PARTY_NOTICES-->
+      <_fileFromArchive Condition="'%(DestinationFile)' == 'LICENSE'" PackagePath="THIRD_PARTY_NOTICES.txt" Runtime="" />
+
+      <!-- copy to packaging location -->
+      <FilesFromArchive Include="@(_fileFromArchive->'%(ExtractDirectory)\%(Identity)')"
+                        TargetPath="$(PackageAssetsPath)$(MSBuildProjectName)\%(PackagePath)" />
+      <!-- include LICENSE that was downloaded from GitHub -->
+      <FilesFromArchive Include="$(BinDir)\LICENSE"
+                        TargetPath="$(PackageAssetsPath)$(MSBuildProjectName)\LICENSE.txt" />
+
+      <!-- copy to NativeAssets location, only for current RID, so that they may be used by tests -->
+      <!--<FilesFromArchive Condition="'$(PackageRID)' == '%(_fileFromArchive.Runtime)'"
+                        Include="@(_fileFromArchive->'%(ExtractDirectory)\%(Identity)')"
+                        TargetPath="$(NativeAssetsBuiltPath)\%(_fileFromArchive.DestinationFile)" />-->
+    </ItemGroup>
+  </Target>
+
+  <Target Name="CopyFilesFromArchive"
+          DependsOnTargets="GetFilesFromArchive">
+
+    <Message Importance="High" Text="@(FilesFromArchive) -> %(FilesFromArchive.TargetPath)" />
+    <Copy SourceFiles="@(FilesFromArchive)"
+          DestinationFiles="@(FilesFromArchive->'%(TargetPath)')" />
+
+  </Target>
+
+  <Target Name="Clean">
+    <Message Importance="High" Text="Deleting $(BinDir);$(ObjDir)" />
+    <RemoveDir Directories="$(BinDir);$(ObjDir)" />
+  </Target>
+  
+</Project>

src/SciSharp.TensorFlow.Redist/libtensorflow-gpu-windows-x86_64-1.14.0.zip.sha

@@ -0,0 +1 @@
+850A27858FA951DF77A78CD1BD78B54F6EE2532DD5A49F0579A7B02C795C62F0212F20177EAEA2BD77BD451A57FBBD1348362492F9E14BFE5CA5028C71711293

src/TensorFlowDatasets/DatasetBuilder.cs

@@ -0,0 +1,24 @@
+using System;
+
+namespace TensorFlowDatasets
+{
+    /// <summary>
+    /// Abstract base class for all datasets.
+    /// </summary>
+    public class DatasetBuilder
+    {
+        /// <summary>
+        /// Downloads and prepares dataset for reading.
+        /// </summary>
+        /// <param name="download_dir">
+        /// directory where downloaded files are stored.
+        /// </param>
+        /// <param name="download_config">
+        /// further configuration for downloading and preparing dataset.
+        /// </param>
+        public void download_and_prepare(string download_dir = null, DownloadConfig download_config = null)
+        {
+
+        }
+    }
+}

src/TensorFlowDatasets/DownloadConfig.cs

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace TensorFlowDatasets
+{
+    public class DownloadConfig
+    {
+    }
+}

src/TensorFlowDatasets/TensorFlowDatasets.csproj

@@ -0,0 +1,19 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
+    <PackageId>SciSharp.TensorFlowDatasets</PackageId>
+    <Version>0.0.1</Version>
+    <Authors>SciSharp Team</Authors>
+    <Product>TensorFlow Datasets</Product>
+    <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
+    <PackageIconUrl>https://avatars3.githubusercontent.com/u/44989469?s=200&amp;v=4</PackageIconUrl>
+    <PackageProjectUrl>http://scisharpstack.org</PackageProjectUrl>
+    <Description>TensorFlow Datasets provides many public datasets as tf.data.Datasets.</Description>
+    <RepositoryUrl>https://github.com/SciSharp/TensorFlow.NET</RepositoryUrl>
+    <RepositoryType>git</RepositoryType>
+    <PackageTags>SciSharp, Dataset, TensorFlow</PackageTags>
+    <Copyright>Apache 2.0</Copyright>
+  </PropertyGroup>
+
+</Project>

src/TensorFlowHub/MnistDataSet.cs

@@ -27,5 +27,54 @@ namespace Tensorflow.Hub
             labels.astype(dataType);
             Labels = labels;
         }
+
+        public (NDArray, NDArray) GetNextBatch(int batch_size, bool fake_data = false, bool shuffle = true)
+        {
+            var start = IndexInEpoch;
+            // Shuffle for the first epoch
+            if(EpochsCompleted == 0 && start == 0 && shuffle)
+            {
+                var perm0 = np.arange(NumOfExamples);
+                np.random.shuffle(perm0);
+                Data = Data[perm0];
+                Labels = Labels[perm0];
+            }
+
+            // Go to the next epoch
+            if (start + batch_size > NumOfExamples)
+            {
+                // Finished epoch
+                EpochsCompleted += 1;
+
+                // Get the rest examples in this epoch
+                var rest_num_examples = NumOfExamples - start;
+                //var images_rest_part = _images[np.arange(start, _num_examples)];
+                //var labels_rest_part = _labels[np.arange(start, _num_examples)];
+                // Shuffle the data
+                if (shuffle)
+                {
+                    var perm = np.arange(NumOfExamples);
+                    np.random.shuffle(perm);
+                    Data = Data[perm];
+                    Labels = Labels[perm];
+                }
+
+                start = 0;
+                IndexInEpoch = batch_size - rest_num_examples;
+                var end = IndexInEpoch;
+                var images_new_part = Data[np.arange(start, end)];
+                var labels_new_part = Labels[np.arange(start, end)];
+
+                /*return (np.concatenate(new float[][] { images_rest_part.Data<float>(), images_new_part.Data<float>() }, axis: 0),
+                    np.concatenate(new float[][] { labels_rest_part.Data<float>(), labels_new_part.Data<float>() }, axis: 0));*/
+                return (images_new_part, labels_new_part);
+            }
+            else
+            {
+                IndexInEpoch += batch_size;
+                var end = IndexInEpoch;
+                return (Data[np.arange(start, end)], Labels[np.arange(start, end)]);
+            }
+        }
     }
 }

src/TensorFlowHub/MnistModelLoader.cs

@@ -15,14 +15,26 @@ namespace Tensorflow.Hub
         private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
         private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
 
-        public static async Task<Datasets<MnistDataSet>> LoadAsync(string trainDir, bool oneHot = false)
+        public static async Task<Datasets<MnistDataSet>> LoadAsync(string trainDir, bool oneHot = false, int? trainSize = null, int? validationSize = null, int? testSize = null)
         {
             var loader = new MnistModelLoader();
-            return await loader.LoadAsync(new ModelLoadSetting
+
+            var setting = new ModelLoadSetting
             {
                 TrainDir = trainDir,
                 OneHot = oneHot
-            });
+            };
+
+            if (trainSize.HasValue)
+                setting.TrainSize = trainSize.Value;
+
+            if (validationSize.HasValue)
+                setting.ValidationSize = validationSize.Value;
+
+            if (testSize.HasValue)
+                setting.TestSize = testSize.Value;
+
+            return await loader.LoadAsync(setting);
         }
 
         public async Task<Datasets<MnistDataSet>> LoadAsync(ModelLoadSetting setting)
@@ -86,7 +98,7 @@ namespace Tensorflow.Hub
 
             var train = new MnistDataSet(trainImages, trainLabels, dtype, reshape);
             var validation = new MnistDataSet(validationImages, validationLabels, dtype, reshape);
-            var test = new MnistDataSet(trainImages, trainLabels, dtype, reshape);
+            var test = new MnistDataSet(testImages, testLabels, dtype, reshape);
 
             return new Datasets<MnistDataSet>(train, validation, test);
         }

src/TensorFlowHub/Utils.cs

@@ -25,13 +25,25 @@ namespace Tensorflow.Hub
             if (!Path.IsPathRooted(dirSaveTo))
                 dirSaveTo = Path.Combine(AppContext.BaseDirectory, dirSaveTo);
 
-            if (!Directory.Exists(dirSaveTo))
-                Directory.CreateDirectory(dirSaveTo);
-            
-            using (var wc = new WebClient())
+            var fileSaveTo = Path.Combine(dirSaveTo, fileName);
+
+            if (File.Exists(fileSaveTo))
             {
-                await wc.DownloadFileTaskAsync(url, Path.Combine(dirSaveTo, fileName));
+                //TODO:maybe you can check file's hashcode and "donglowad.info" to complete file ...
+                Console.WriteLine($"{fileSaveTo} already exists.");
             }
+            else
+            {
+                if (!Directory.Exists(dirSaveTo))
+                    Directory.CreateDirectory(dirSaveTo);
+
+                using (var wc = new WebClient())
+                {
+                    await wc.DownloadFileTaskAsync(url, fileSaveTo);
+                }
+
+            }
+
         }
 
         public static async Task UnzipAsync<TDataSet>(this IModelLoader<TDataSet> modelLoader, string zipFile, string saveTo)
@@ -42,7 +54,7 @@ namespace Tensorflow.Hub
 
             if (!Directory.Exists(saveTo))
                 Directory.CreateDirectory(saveTo);
-            
+
             if (!Path.IsPathRooted(zipFile))
                 zipFile = Path.Combine(AppContext.BaseDirectory, zipFile);
 
@@ -78,7 +90,7 @@ namespace Tensorflow.Hub
 
             var cts = new CancellationTokenSource();
             var showProgressTask = ShowProgressInConsole(cts);
-            
+
             try
             {
                 await task;
@@ -86,7 +98,7 @@ namespace Tensorflow.Hub
             finally
             {
                 cts.Cancel();
-            }            
+            }
         }
 
         private static async Task ShowProgressInConsole(CancellationTokenSource cts)

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -189,6 +189,9 @@ namespace Tensorflow
         public static Tensor log1p(Tensor x, string name = null)
             => gen_math_ops.log1p(x, name);
 
+        public static Tensor logical_and(Tensor x, Tensor y, string name = null)
+            => gen_math_ops.logical_and(x, y, name);
+
         /// <summary>
         /// Clips tensor values to a specified min and max.
         /// </summary>

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -136,7 +136,7 @@ namespace Tensorflow
 
             public static Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
             {
-                return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
+                return Python.tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
                 {
                     name = scope;
                     return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
@@ -169,7 +169,7 @@ namespace Tensorflow
             /// <returns></returns>
             public static Tensor softmax_cross_entropy_with_logits(Tensor labels, Tensor logits, int dim = -1, string name = null)
             {
-                with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
+                tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
                 {
                     name = scope;
                     labels = array_ops.stop_gradient(labels, name: "labels_stop_gradient");

src/TensorFlowNET.Core/Buffers/Buffer.cs

@@ -19,10 +19,8 @@ using System.Runtime.InteropServices;
 
 namespace Tensorflow
 {
-    public class Buffer : IDisposable
+    public class Buffer : DisposableObject
     {
-        private IntPtr _handle;
-
         private TF_Buffer buffer => Marshal.PtrToStructure<TF_Buffer>(_handle);
 
         public byte[] Data 
@@ -30,8 +28,8 @@ namespace Tensorflow
             get 
             {
                 var data = new byte[buffer.length];
-                if (buffer.length > 0)
-                    Marshal.Copy(buffer.data, data, 0, (int)buffer.length);
+                if (data.Length > 0)
+                    Marshal.Copy(buffer.data, data, 0, data.Length);
                 return data;
             }
         }
@@ -54,6 +52,8 @@ namespace Tensorflow
             Marshal.Copy(data, 0, dst, data.Length);
 
             _handle = c_api.TF_NewBufferFromString(dst, (ulong)data.Length);
+
+            Marshal.FreeHGlobal(dst);
         }
 
         public static implicit operator IntPtr(Buffer buffer)
@@ -66,9 +66,7 @@ namespace Tensorflow
             return buffer.Data;
         }
 
-        public void Dispose()
-        {
-            c_api.TF_DeleteBuffer(_handle);
-        }
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteBuffer(handle);
     }
 }

src/TensorFlowNET.Core/Clustering/_InitializeClustersOpFactory.cs

@@ -68,7 +68,7 @@ namespace Tensorflow.Clustering
 
         private Tensor _initialize()
         {
-            return with(ops.control_dependencies(new Operation[]
+            return tf_with(ops.control_dependencies(new Operation[]
             {
                 check_ops.assert_positive(_num_remaining)
             }), delegate

src/TensorFlowNET.Core/DisposableObject.cs

@@ -0,0 +1,77 @@
+/*****************************************************************************
+   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    /// <summary>
+    /// Abstract class for disposable object allocated in unmanaged runtime.
+    /// </summary>
+    public abstract class DisposableObject : IDisposable
+    {
+        protected IntPtr _handle;
+
+        protected DisposableObject() { }
+
+        public DisposableObject(IntPtr handle)
+        {
+            _handle = handle;
+        }
+
+        protected virtual void DisposeManagedState()
+        {
+        }
+
+        protected abstract void DisposeUnManagedState(IntPtr handle);
+
+        protected virtual void Dispose(bool disposing)
+        {
+            if (disposing)
+            {
+                // free unmanaged resources (unmanaged objects) and override a finalizer below.
+                if (_handle != IntPtr.Zero)
+                {
+                    // dispose managed state (managed objects).
+                    DisposeManagedState();
+
+                    // set large fields to null.
+                    DisposeUnManagedState(_handle);
+
+                    _handle = IntPtr.Zero;
+                }
+            }
+        }
+
+        // override a finalizer only if Dispose(bool disposing) above has code to free unmanaged resources.
+        ~DisposableObject()
+        {
+            // Do not change this code. Put cleanup code in Dispose(bool disposing) above.
+            Dispose(false);
+        }
+
+        // This code added to correctly implement the disposable pattern.
+        public void Dispose()
+        {
+            // Do not change this code. Put cleanup code in Dispose(bool disposing) above.
+            Dispose(true);
+            // uncomment the following line if the finalizer is overridden above.
+            GC.SuppressFinalize(this);
+        }
+    }
+}

src/TensorFlowNET.Core/Framework/c_api_util.cs

@@ -128,7 +128,7 @@ namespace Tensorflow
             IntPtr c_op;
             while ((c_op = c_api.TF_GraphNextOperation(graph, ref pos)) != IntPtr.Zero)
             {
-                yield return c_op;
+                yield return new Operation(c_op, graph);
             }
         }
     }

src/TensorFlowNET.Core/Framework/importer.py.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
 
             string prefix = "";
             var graph = ops.get_default_graph();
-            with(ops.name_scope(name, "import", input_map.Values), scope =>
+            tf_with(ops.name_scope(name, "import", input_map.Values), scope =>
             {
                 prefix = scope;
                 /*if (!string.IsNullOrEmpty(prefix))

src/TensorFlowNET.Core/Gradients/gradients_util.cs

@@ -55,7 +55,7 @@ namespace Tensorflow
              **/
             var grads = new Dictionary<string, List<List<Tensor>>>();
 
-            with(ops.name_scope(name, "gradients",
+            tf_with(ops.name_scope(name, "gradients",
                 values: ys.Concat(xs).Concat(stop_gradients).Concat(grad_ys)), scope =>
                 {
                     string grad_scope = scope;
@@ -141,7 +141,7 @@ namespace Tensorflow
                                     }
                                 }
 
-                                with(ops.name_scope(op.name + "_grad"), scope1 =>
+                                tf_with(ops.name_scope(op.name + "_grad"), scope1 =>
                                 {
                                     string name1 = scope1;
                                     if (grad_fn != null)

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -90,7 +90,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var y = op.outputs[0];  // y = e^x
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 y = math_ops.conj(y);
                 return new Tensor[] { math_ops.mul_no_nan(y, grad) };
             });
@@ -107,7 +107,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var x = op.inputs[0];
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 x = math_ops.conj(x);
                 return new Tensor[] { grad * math_ops.digamma(x) };
             });
@@ -118,7 +118,7 @@ namespace Tensorflow.Gradients
         {
             var grad = grads[0];
             var x = op.inputs[0];
-            return with(ops.control_dependencies(new Operation[] { grad }), dp => {
+            return tf_with(ops.control_dependencies(new Operation[] { grad }), dp => {
                 x = math_ops.conj(x);
                 return new Tensor[] { grad * math_ops.reciprocal(x) };
             });
@@ -431,7 +431,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var y = op.outputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 y = math_ops.conj(y);
                 return new Tensor[] { gen_math_ops.sigmoid_grad(y, grad) };
@@ -453,7 +453,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var x = op.inputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 x = math_ops.conj(x);
                 var y = constant_op.constant(2.0f, dtype: x.dtype);
@@ -467,7 +467,7 @@ namespace Tensorflow.Gradients
             var grad = grads[0];
             var y = op.outputs[0];
 
-            return with(ops.control_dependencies(grads), delegate
+            return tf_with(ops.control_dependencies(grads), delegate
             {
                 y = math_ops.conj(y);
                 return new Tensor[] { gen_math_ops.tanh_grad(y, grad) };

src/TensorFlowNET.Core/Graphs/Graph.Export.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
         {
             var buffer = new Buffer();
             c_api.TF_GraphToGraphDef(_handle, buffer, s);
-            s.Check();
+            s.Check(true);
             // var def = GraphDef.Parser.ParseFrom(buffer);
             // buffer.Dispose();
 
@@ -31,8 +31,11 @@ namespace Tensorflow
 
         private GraphDef _as_graph_def(bool add_shapes = false)
         {
-            var buffer = ToGraphDef(Status);
-            Status.Check();
+            var status = new Status();
+            var buffer = ToGraphDef(status);
+            status.Check(true);
+            status.Dispose();
+
             var def = GraphDef.Parser.ParseFrom(buffer);
             buffer.Dispose();
 

src/TensorFlowNET.Core/Graphs/Graph.Import.cs

@@ -43,16 +43,20 @@ namespace Tensorflow
             var bytes = File.ReadAllBytes(file_path);
             var graph_def = new Tensorflow.Buffer(bytes);
             var opts = c_api.TF_NewImportGraphDefOptions();
-            c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, Status);
-            return Status;
+            var status = new Status();
+            c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, status);
+            return status;
         }
 
-        public Status Import(byte[] bytes)
+        public Status Import(byte[] bytes, string prefix = "")
         {
             var graph_def = new Tensorflow.Buffer(bytes);
             var opts = c_api.TF_NewImportGraphDefOptions();
-            c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, Status);
-            return Status;
+            c_api.TF_ImportGraphDefOptionsSetPrefix(opts, prefix);
+            var status = new Status();
+            c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, status);
+            c_api.TF_DeleteImportGraphDefOptions(opts);
+            return status;
         }
 
         public static Graph ImportFromPB(string file_path, string name = null)

src/TensorFlowNET.Core/Graphs/Graph.Operation.cs

@@ -38,6 +38,31 @@ namespace Tensorflow
             return c_api.TF_NewOperation(_handle, opType, opName);
         }
 
+        public unsafe Operation[] ReturnOperations(IntPtr results)
+        {
+            TF_Operation return_oper_handle = new TF_Operation();
+            int num_return_opers = 0;
+            c_api.TF_ImportGraphDefResultsReturnOperations(results, ref num_return_opers, ref return_oper_handle);
+            Operation[] return_opers = new Operation[num_return_opers];
+            for (int i = 0; i < num_return_opers; i++)
+            {
+                var handle = return_oper_handle.node + Marshal.SizeOf<TF_Operation>() * i;
+                return_opers[i] = new Operation(*(IntPtr*)handle);
+            }
+
+            return return_opers;
+        }
+
+        public Operation OperationByName(string operName)
+        {
+            return c_api.TF_GraphOperationByName(_handle, operName);
+        }
+
+        public ITensorOrOperation[] get_operations()
+        {
+            return _nodes_by_name.Values.Select(x => x).ToArray();
+        }
+
         /// <summary>
         /// Returns the `Operation` with the given `name`.
         /// 

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Linq;
 using System.Runtime.InteropServices;
@@ -72,7 +73,7 @@ namespace Tensorflow
         all variables that are created during the construction of a graph. The caller
         may define additional collections by specifying a new name.     
      */
-    public partial class Graph : IPython, IDisposable
+    public partial class Graph : IPython, IDisposable, IEnumerable<Operation>
     {
         private IntPtr _handle;
         private Dictionary<int, ITensorOrOperation> _nodes_by_id;
@@ -87,8 +88,7 @@ namespace Tensorflow
         private string _graph_key;
         public string graph_key => _graph_key;
         public string _last_loss_reduction;
-        public bool _is_loss_scaled_by_optimizer { get; set; }
-        public Status Status { get; }
+        public bool _is_loss_scaled_by_optimizer { get; set; }
 
         /// <summary>
         /// True if the graph is considered "finalized".  In that case no
@@ -106,7 +106,6 @@ namespace Tensorflow
         public Graph()
         {
             _handle = c_api.TF_NewGraph();
-            Status = new Status();
             _nodes_by_id = new Dictionary<int, ITensorOrOperation>();
             _nodes_by_name = new Dictionary<string, ITensorOrOperation>();
             _names_in_use = new Dictionary<string, int>();
@@ -116,11 +115,14 @@ namespace Tensorflow
         public Graph(IntPtr handle)
         {
             _handle = handle;
-            Status = new Status();
             _nodes_by_id = new Dictionary<int, ITensorOrOperation>();
             _nodes_by_name = new Dictionary<string, ITensorOrOperation>();
             _names_in_use = new Dictionary<string, int>();
             _graph_key = $"grap-key-{ops.uid()}/";
+        }
+
+        public void __enter__()
+        {
         }
 
         public ITensorOrOperation as_graph_element(object obj, bool allow_tensor = true, bool allow_operation = true)
@@ -409,31 +411,6 @@ namespace Tensorflow
             return return_outputs;
         }
 
-        public unsafe Operation[] ReturnOperations(IntPtr results)
-        {
-            TF_Operation return_oper_handle = new TF_Operation();
-            int num_return_opers = 0;
-            c_api.TF_ImportGraphDefResultsReturnOperations(results, ref num_return_opers, ref return_oper_handle);
-            Operation[] return_opers = new Operation[num_return_opers];
-            for (int i = 0; i < num_return_opers; i++)
-            {
-                var handle = return_oper_handle.node + Marshal.SizeOf<TF_Operation>() * i;
-                return_opers[i] = new Operation(*(IntPtr*)handle);
-            }
-            
-            return return_opers;
-        }
-
-        public Operation OperationByName(string operName)
-        {
-            return c_api.TF_GraphOperationByName(_handle, operName);
-        }
-
-        public ITensorOrOperation[] get_operations()
-        {
-            return _nodes_by_name.Values.Select(x => x).ToArray();
-        }
-
         public string[] get_all_collection_keys()
         {
             return _collections.Keys.Where(x => !x.StartsWith("__")).ToArray();
@@ -468,7 +445,12 @@ namespace Tensorflow
 
         public void Dispose()
         {
-            // c_api.TF_DeleteGraph(_handle);
+            /*if (_handle != IntPtr.Zero)
+                c_api.TF_DeleteGraph(_handle);
+
+            _handle = IntPtr.Zero;
+
+            GC.SuppressFinalize(this);*/
         }
 
         /// <summary>
@@ -481,17 +463,46 @@ namespace Tensorflow
         public Tensor get_tensor_by_name(string name)
         {
             return (Tensor)this.as_graph_element(name, allow_tensor: true, allow_operation: false);
-        }
-
-        public void __enter__()
-        {
+        }
+
+        public TensorShape GetTensorShape(TF_Output output)
+        {
+            var status = new Status();
+            var ndim = c_api.TF_GraphGetTensorNumDims(_handle, output, status);
+            status.Check();
+
+            if (ndim == -1)
+                return new TensorShape();
+
+            var dims = new long[ndim];
+            c_api.TF_GraphGetTensorShape(_handle, output, dims, dims.Length, status);
+            status.Check();
+
+            return new TensorShape(dims.Select(x => (int)x).ToArray());
+        }
+
+        public override string ToString()
+        {
+            int len = 0;
+            return c_api.TF_GraphDebugString(_handle, out len);
         }
 
         public void __exit__()
         {
 
-        }
+        }
+
+        private IEnumerable<Operation> GetEnumerable()
+            => c_api_util.tf_operations(this);
 
+        IEnumerator<Operation> IEnumerable<Operation>.GetEnumerator()
+            => GetEnumerable().GetEnumerator();
+
+        IEnumerator IEnumerable.GetEnumerator()
+        {
+            throw new NotImplementedException();
+        }
+
         public static implicit operator IntPtr(Graph graph)
         {
             return graph._handle;

src/TensorFlowNET.Core/Graphs/c_api.graph.cs

@@ -43,6 +43,9 @@ namespace Tensorflow
         [DllImport(TensorFlowLibName)]
         public static extern void TF_DeleteImportGraphDefResults(IntPtr results);
 
+        [DllImport(TensorFlowLibName)]
+        public static extern string TF_GraphDebugString(IntPtr graph, out int len);
+
         [DllImport(TensorFlowLibName)]
         public static extern void TF_GraphGetOpDef(IntPtr graph, string op_name, IntPtr output_op_def, IntPtr status);
 
@@ -100,6 +103,7 @@ namespace Tensorflow
         /// <param name="status">TF_Status*</param>
         [DllImport(TensorFlowLibName)]
         public static extern void TF_GraphImportGraphDef(IntPtr graph, IntPtr graph_def, IntPtr options, IntPtr status);
+        
         /// <summary>
         /// Iterate through the operations of a graph.
         /// </summary>

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

@@ -207,7 +207,7 @@ namespace Tensorflow.Keras.Layers
 
         public Tensor _assign_moving_average(RefVariable variable, Tensor value, Tensor momentum)
         {
-            return Python.with(ops.name_scope(null, "AssignMovingAvg", new { variable, value, momentum }), scope =>
+            return Python.tf_with(ops.name_scope(null, "AssignMovingAvg", new { variable, value, momentum }), scope =>
             {
                 // var cm = ops.colocate_with(variable);
                 var decay = ops.convert_to_tensor(1.0f - momentum, name: "decay");

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

@@ -125,7 +125,7 @@ namespace Tensorflow.Keras.Layers
                 // Symbolic execution on symbolic tensors. We will attempt to build
                 // the corresponding TF subgraph inside `backend.get_graph()`
                 var graph = backend.get_graph().as_default();
-                with(ops.name_scope(_name_scope()), delegate
+                tf_with(ops.name_scope(_name_scope()), delegate
                 {
                     // Build layer if applicable (if the `build` method has been
                     // overridden).

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -72,7 +72,7 @@ namespace Tensorflow.Layers
             }
 
             Tensor outputs = null;
-            with(scope_context_manager, scope2 =>
+            tf_with(scope_context_manager, scope2 =>
             {
                 _current_scope = scope2;
                 // Actually call layer
@@ -136,12 +136,12 @@ namespace Tensorflow.Layers
 
             _set_scope();
             var reuse = built || (_reuse != null && _reuse.Value);
-            return with(tf.variable_scope(_scope,
+            return tf_with(tf.variable_scope(_scope,
                 reuse: reuse,
                 auxiliary_name_scope: false), scope =>
                 {
                     _current_scope = scope;
-                    return with(ops.name_scope(_name_scope()), delegate
+                    return tf_with(ops.name_scope(_name_scope()), delegate
                     {
                         var variable = base.add_weight(name,
                             shape,
@@ -183,7 +183,7 @@ namespace Tensorflow.Layers
                 }
                 else
                 {
-                    with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
+                    tf_with(tf.variable_scope(scope, default_name: _base_name), captured_scope =>
                     {
                         // convert variable_scope to VariableScope
                         _scope = captured_scope;

src/TensorFlowNET.Core/Operations/ControlFlows/CondContext.cs

@@ -122,7 +122,7 @@ namespace Tensorflow.Operations
                     _external_values[result.name] = result;
                 }
                 
-                with(ops.control_dependencies(null), ctrl =>
+                tf_with(ops.control_dependencies(null), ctrl =>
                 {
                     var results = control_flow_ops._SwitchRefOrTensor(result, _pred);
                     result = results[_branch];

src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs

@@ -58,7 +58,7 @@ namespace Tensorflow
 
         private Tensor _call_log_prob (Tensor value, string name)
         {
-            return with(ops.name_scope(name, "moments", new { value }), scope =>
+            return tf_with(ops.name_scope(name, "moments", new { value }), scope =>
             {
                 try
                 {

src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs

@@ -50,9 +50,9 @@ namespace Tensorflow
             parameters.Add("validate_args", validate_args);
             parameters.Add("allow_nan_stats", allow_nan_stats);
 
-            with(ops.name_scope(name, "", new { loc, scale }), scope => 
+            tf_with(ops.name_scope(name, "", new { loc, scale }), scope => 
             {
-                with(ops.control_dependencies(validate_args ? new Operation[] { scale.op} : new Operation[] { }), cd =>
+                tf_with(ops.control_dependencies(validate_args ? new Operation[] { scale.op} : new Operation[] { }), cd =>
                 {
                     this._loc = array_ops.identity(loc, name);
                     this._scale = array_ops.identity(scale, name);

src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs

@@ -24,7 +24,7 @@ namespace Tensorflow
         public Tensor compute_weighted_loss(Tensor losses, Tensor weights = null, string scope = null,
             string loss_collection = ops.GraphKeys.LOSSES, string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
-            return with(ops.name_scope(scope, default_name: "weighted_loss", (losses, weights)), delegate
+            return tf_with(ops.name_scope(scope, default_name: "weighted_loss", (losses, weights)), delegate
             {
                 // Save the `reduction` argument for loss normalization when distributing
                 // to multiple replicas. Used only for estimator + v1 optimizer flow.
@@ -77,7 +77,7 @@ namespace Tensorflow
 
         public Tensor _num_present(Tensor losses, Tensor weights, bool per_batch = false)
         {
-            return with(ops.name_scope(null, default_name: "num_present", (losses, weights)), name_scope =>
+            return tf_with(ops.name_scope(null, default_name: "num_present", (losses, weights)), name_scope =>
             {
                 string scope = name_scope;
                 weights = math_ops.cast(weights, dtype: dtypes.float32);
@@ -104,7 +104,7 @@ namespace Tensorflow
             string loss_collection= ops.GraphKeys.LOSSES,
             string reduction = Reduction.SUM_BY_NONZERO_WEIGHTS)
         {
-            return with(ops.name_scope(scope,
+            return tf_with(ops.name_scope(scope,
                 "sparse_softmax_cross_entropy_loss",
                 (logits, labels, weights)),
                 name_scope =>

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

@@ -30,7 +30,7 @@ namespace Tensorflow.Operations
             string data_format = "NHWC",
             string name = null)
         {
-            return with(ops.name_scope(name, "MaxPool", value), scope => 
+            return tf_with(ops.name_scope(name, "MaxPool", value), scope => 
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");

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

@@ -29,7 +29,7 @@ namespace Tensorflow.Operations
             TF_DataType dtype = TF_DataType.DtInvalid,
             int? parallel_iterations = null, bool swap_memory = false, bool time_major = false)
         {
-            with(tf.variable_scope("rnn"), scope =>
+            tf_with(tf.variable_scope("rnn"), scope =>
             {
                 VariableScope varscope = scope;
                 var flat_input = nest.flatten(inputs_tensor);
@@ -139,7 +139,7 @@ namespace Tensorflow.Operations
             var time = array_ops.constant(0, dtype: dtypes.int32, name: "time");
 
             string base_name = null;
-            with(ops.name_scope("dynamic_rnn"), scope => base_name = scope);
+            tf_with(ops.name_scope("dynamic_rnn"), scope => base_name = scope);
 
             Func<string, TensorShape, TF_DataType, Tensor> _create_ta = (name, element_shape, dtype_) =>
             {

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -58,7 +58,7 @@ namespace Tensorflow
             var input_types = new List<TF_DataType>();
             object values = null;
 
-            return with(ops.name_scope(name), scope =>
+            return tf_with(ops.name_scope(name), scope =>
             {
                 var inferred_from = new Dictionary<string, object>();
                 var base_types = new List<TF_DataType>();

src/TensorFlowNET.Core/Operations/Operation.Implicit.cs

@@ -23,7 +23,10 @@ namespace Tensorflow
     /// </summary>
     public partial class Operation
     {
-        public static implicit operator Operation(IntPtr handle) => new Operation(handle);
+        // make sure the new op is in the same graph instance
+        public static implicit operator Operation(IntPtr handle) 
+            => new Operation(handle);
+
         public static implicit operator IntPtr(Operation op) => op._handle;
         public static implicit operator Tensor(Operation op) => op.output;
 

src/TensorFlowNET.Core/Operations/Operation.Input.cs

@@ -26,7 +26,18 @@ namespace Tensorflow
     {
         public TF_Output Input(int index) => c_api.TF_OperationInput(new TF_Input(_handle, index));
         public TF_DataType InputType(int index) => c_api.TF_OperationInputType(new TF_Input(_handle, index));
-        public int InputListLength(string name) => c_api.TF_OperationInputListLength(_handle, name, status);
+
+        public int InputListLength(string name)
+        {
+            int num = 0;
+            using(var status = new Status())
+            {
+                num = c_api.TF_OperationInputListLength(_handle, name, status);
+                status.Check(true);
+            }
+            return num;
+        }
+
         public int NumInputs => c_api.TF_OperationNumInputs(_handle);
         private TF_DataType[] _input_types => _inputs._inputs.Select(x => x.dtype).ToArray();
 

src/TensorFlowNET.Core/Operations/Operation.Output.cs

@@ -24,7 +24,18 @@ namespace Tensorflow
     {
         public int NumOutputs => c_api.TF_OperationNumOutputs(_handle);
         public TF_DataType OutputType(int index) => c_api.TF_OperationOutputType(new TF_Output(_handle, index));
-        public int OutputListLength(string name) => c_api.TF_OperationOutputListLength(_handle, name, status);
+
+        public int OutputListLength(string name)
+        {
+            int num = 0;
+            using (var status = new Status())
+            {
+                num = c_api.TF_OperationOutputListLength(_handle, name, status);
+                status.Check(true);
+            }
+
+            return num;
+        }
 
         private Tensor[] _outputs;
         public Tensor[] outputs => _outputs;
@@ -35,6 +46,8 @@ namespace Tensorflow
 
         public int OutputNumConsumers(int index) => c_api.TF_OperationOutputNumConsumers(new TF_Output(_handle, index));
 
+        public TF_Output this[int index] => _tf_output(index);
+
         public unsafe TF_Input[] OutputConsumers(int index, int max_consumers)
         {
             int size = Marshal.SizeOf<TF_Input>();

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -54,11 +54,10 @@ namespace Tensorflow
         public Operation op => this;
 
         public TF_DataType dtype => TF_DataType.DtInvalid;
-        private Status status = new Status();
 
         public string name => _handle == IntPtr.Zero ? null : c_api.StringPiece(c_api.TF_OperationName(_handle));
-        public string OpType => c_api.StringPiece(c_api.TF_OperationOpType(_handle));
-        public string Device => c_api.StringPiece(c_api.TF_OperationDevice(_handle));
+        public string OpType => _handle == IntPtr.Zero ? null : c_api.StringPiece(c_api.TF_OperationOpType(_handle));
+        public string Device => _handle == IntPtr.Zero ? null : c_api.StringPiece(c_api.TF_OperationDevice(_handle));
 
         private NodeDef _node_def;
         public NodeDef node_def
@@ -96,10 +95,14 @@ namespace Tensorflow
 
             _operDesc = c_api.TF_NewOperation(g, opType, oper_name);
             c_api.TF_SetAttrType(_operDesc, "dtype", TF_DataType.TF_INT32);
-            _handle = c_api.TF_FinishOperation(_operDesc, status);
-
-            // Dict mapping op name to file and line information for op colocation
-            // context managers.
+            using (var status = new Status())
+            {
+                _handle = c_api.TF_FinishOperation(_operDesc, status);
+                status.Check(true);
+            }
+
+            // Dict mapping op name to file and line information for op colocation
+            // context managers.
             _control_flow_context = graph._get_control_flow_context();
         }
 
@@ -220,6 +223,7 @@ namespace Tensorflow
         {
             AttrValue x = null;
 
+            using (var status = new Status())
             using (var buf = new Buffer())
             {
                 c_api.TF_OperationGetAttrValueProto(_handle, name, buf, status);
@@ -274,12 +278,15 @@ namespace Tensorflow
             var output = tensor._as_tf_output();
 
             // Reset cached inputs.
-            _inputs = null; 
+            _inputs = null;
             // after the c_api call next time _inputs is accessed 
             // the updated inputs are reloaded from the c_api
-            c_api.UpdateEdge(_graph, output, input, status);
-            //var updated_inputs = inputs;
-            status.Check();
+            using (var status = new Status())
+            {
+                c_api.UpdateEdge(_graph, output, input, status);
+                //var updated_inputs = inputs;
+                status.Check();
+            }
         }
 
         private void _assert_same_graph(Tensor tensor)

src/TensorFlowNET.Core/Operations/RNNCell.cs

@@ -82,7 +82,7 @@ namespace Tensorflow
         {
             Tensor output = null;
             var state_size = this.state_size;
-            with(ops.name_scope($"{this.GetType().Name}ZeroState", values: new { batch_size }), delegate
+            tf_with(ops.name_scope($"{this.GetType().Name}ZeroState", values: new { batch_size }), delegate
             {
                 output = _zero_state_tensors(state_size, batch_size, dtype);
             });

src/TensorFlowNET.Core/Operations/_GraphTensorArray.cs

@@ -66,7 +66,7 @@ namespace Tensorflow.Operations
                 _element_shape = new List<TensorShape> { };
             }
 
-            with(ops.name_scope(name, "", new { handle, size, flow }), scope =>
+            tf_with(ops.name_scope(name, "", new { handle, size, flow }), scope =>
             {
                 if(handle != null)
                 {

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

@@ -43,7 +43,7 @@ namespace Tensorflow
         public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "zeros", shape), scope =>
+            return tf_with(ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -67,7 +67,7 @@ namespace Tensorflow
         public static Tensor zeros(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "zeros", shape), scope =>
+            return tf_with(ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -140,7 +140,7 @@ namespace Tensorflow
         {
             var must_pack = false;
             var converted_elems = new List<object>();
-            return with(ops.name_scope(name), scope =>
+            return tf_with(ops.name_scope(name), scope =>
             {
                 foreach (var (i, elem) in enumerate(list_or_tuple))
                 {
@@ -189,7 +189,7 @@ namespace Tensorflow
 
         public static Tensor rank_internal(Tensor input, string name = null, bool optimize = true)
         {
-            return with(ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
+            return tf_with(ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
             {
                 name = scope;
                 var input_tensor = ops.convert_to_tensor(input);
@@ -217,7 +217,7 @@ namespace Tensorflow
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
-            return with(ops.name_scope(name, "ones_like", new { tensor }), scope =>
+            return tf_with(ops.name_scope(name, "ones_like", new { tensor }), scope =>
             {
                 name = scope;
                 var tensor1 = ops.convert_to_tensor(tensor, name: "tensor");
@@ -233,7 +233,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { shape }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = gen_array_ops.fill(shape, constant_op.constant(1.0f, dtype: dtype), name: name);
@@ -244,7 +244,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor[] shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { shape }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = _constant_if_small(1, shape[0]);
@@ -257,7 +257,7 @@ namespace Tensorflow
         public static Tensor ones(int[] dims, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return with(ops.name_scope(name, "ones", new { dims }), scope =>
+            return tf_with(ops.name_scope(name, "ones", new { dims }), scope =>
             {
                 name = scope;
                 var shape = ops.convert_to_tensor(dims, dtype: TF_DataType.TF_INT32);
@@ -273,7 +273,7 @@ namespace Tensorflow
             int axis = -1,
             string name = null)
         {
-            return with(ops.name_scope(name, "one_hot", new { indices, depth, dtype }), scope =>
+            return tf_with(ops.name_scope(name, "one_hot", new { indices, depth, dtype }), scope =>
             {
                 name = scope;
                 var on_exists = false;
@@ -341,7 +341,7 @@ namespace Tensorflow
 
         private static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return with(ops.name_scope(name, "Shape", new { input }), scope =>
+            return tf_with(ops.name_scope(name, "Shape", new { input }), scope =>
             {
                 name = scope;
 
@@ -362,7 +362,7 @@ namespace Tensorflow
 
         private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return with(ops.name_scope(name, "Size", new { input }), scope =>
+            return tf_with(ops.name_scope(name, "Size", new { input }), scope =>
             {
                 name = scope;
 
@@ -382,7 +382,7 @@ namespace Tensorflow
 
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
-            return with(ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
+            return tf_with(ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
             {
                 name = scope;
                 tensor = ops.convert_to_tensor(tensor, name: "tensor");
@@ -516,7 +516,7 @@ namespace Tensorflow
         {
             if(values.Length == 1) // Degenerate case of one tensor.
             {
-                return with(ops.name_scope(name), scope => {
+                return tf_with(ops.name_scope(name), scope => {
                     var t = ops.convert_to_tensor(axis, name: "concat_dim", dtype: TF_DataType.TF_INT32);
                     return identity(values[0], name: scope);
                 });
@@ -535,7 +535,7 @@ namespace Tensorflow
 
         public static Tensor transpose<T1, T2>(T1 a, T2 perm, string name = "transpose", bool conjugate = false)
         {
-            return with(ops.name_scope(name, "transpose", new { a }), scope =>
+            return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
             {
                 return gen_array_ops.transpose(a, perm, name: scope);
             });

src/TensorFlowNET.Core/Operations/check_ops.cs

@@ -31,7 +31,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_equal", new { t1, t2, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_equal", new { t1, t2, data }), delegate
             {
                 var x = ops.convert_to_tensor(t1, name: "x");
                 var y = ops.convert_to_tensor(t2, name: "y");
@@ -62,7 +62,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_positive", new { x, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_positive", new { x, data }), delegate
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (data == null)
@@ -86,7 +86,7 @@ namespace Tensorflow
             if (message == null)
                 message = "";
 
-            return with(ops.name_scope(name, "assert_less", new { x, y, data }), delegate
+            return tf_with(ops.name_scope(name, "assert_less", new { x, y, data }), delegate
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 y = ops.convert_to_tensor(y, name: "y");

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

@@ -34,7 +34,7 @@ namespace Tensorflow
             int expected_rank_diff = 0,
             string name = null)
         {
-            return with(ops.name_scope(name, default_name: "remove_squeezable_dimensions", (labels, predictions)), delegate
+            return tf_with(ops.name_scope(name, default_name: "remove_squeezable_dimensions", (labels, predictions)), delegate
             {
                 predictions = ops.convert_to_tensor(predictions);
                 labels = ops.convert_to_tensor(labels);

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

@@ -28,7 +28,7 @@ namespace Tensorflow
     {
         public static Operation Assert(Tensor condition, object[] data, int? summarize = null, string name = null)
         {
-            return with(ops.name_scope(name, "Assert", new { condition, data }), scope =>
+            return tf_with(ops.name_scope(name, "Assert", new { condition, data }), scope =>
             {
                 name = scope;
                 var xs = ops.convert_n_to_tensor(data);
@@ -53,7 +53,7 @@ namespace Tensorflow
 
         public static Operation group<T>(T[] inputs, string name = null) where T : ITensorOrOperation
         {
-            return with(ops.name_scope(name, "group_deps", inputs), scope =>
+            return tf_with(ops.name_scope(name, "group_deps", inputs), scope =>
             {
                 name = scope;
 
@@ -91,7 +91,7 @@ namespace Tensorflow
 
         private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = null)
         {
-            return with(ops.control_dependencies(deps), ctl =>
+            return tf_with(ops.control_dependencies(deps), ctl =>
             {
                 if (dev == null)
                 {
@@ -135,7 +135,7 @@ namespace Tensorflow
 
         public static Tensor[] tuple(Tensor[] tensors, string name = null, Operation[] control_inputs = null)
         {
-            return with(ops.name_scope(name, "tuple", tensors), scope =>
+            return tf_with(ops.name_scope(name, "tuple", tensors), scope =>
             {
                 name = scope;
                 var gating_ops = tensors.Where(x => x != null).Select(x => x.op).ToList();
@@ -189,13 +189,13 @@ namespace Tensorflow
             values.AddRange(dependencies);
             values.Add(output_tensor);
 
-            return with(ops.name_scope(name, "control_dependency", values), scope =>
+            return tf_with(ops.name_scope(name, "control_dependency", values), scope =>
             {
                 name = scope;
                 // TODO: missing original code
                 //with ops.colocate_with(output_tensor):
                 {
-                    return with(ops.control_dependencies(dependencies), ctl =>
+                    return tf_with(ops.control_dependencies(dependencies), ctl =>
                     {
                         output_tensor = ops.convert_to_tensor_or_composite(output_tensor);
                         return _Identity(output_tensor, name: name);
@@ -306,7 +306,7 @@ namespace Tensorflow
             bool strict = false,
             string name = null)
         {
-            return with(ops.name_scope(name, "cond", new { pred }), delegate
+            return tf_with(ops.name_scope(name, "cond", new { pred }), delegate
             {
                 // TODO: here a chunk of original code is missing
                 /*
@@ -398,7 +398,7 @@ namespace Tensorflow
             bool strict = false,
             string name = null)
         {
-            return with(ops.name_scope(name, "cond", new { pred }), delegate
+            return tf_with(ops.name_scope(name, "cond", new { pred }), delegate
             {
                 // Add the Switch to the graph.
                 var switch_result = @switch(pred, pred);
@@ -467,7 +467,7 @@ namespace Tensorflow
         {
             if (inputs.Any(x => x == null))
                 throw new ValueError($"At least one of the merge inputs is null: {inputs}");
-            return with(ops.name_scope(name, "Merge", inputs), scope =>
+            return tf_with(ops.name_scope(name, "Merge", inputs), scope =>
             {
                 name = scope;
                 inputs = inputs.Select(inp =>
@@ -489,7 +489,7 @@ namespace Tensorflow
             TF_DataType dtype = TF_DataType.DtInvalid, 
             string name = null)
         {
-            return with(ops.name_scope(name, "Switch", new { data, pred }), scope =>
+            return tf_with(ops.name_scope(name, "Switch", new { data, pred }), scope =>
             {
                 name = scope;
                 data = ops.internal_convert_to_tensor_or_indexed_slices(data,

src/TensorFlowNET.Core/Operations/embedding_ops.cs

@@ -35,7 +35,7 @@ namespace Tensorflow
                 string name = null,
                 string max_norm = null)
         {
-            return with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
+            return tf_with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
             {
                 name = scope;
                 int np = 1;
@@ -58,7 +58,7 @@ namespace Tensorflow
                 string name = null,
                 string max_norm = null)
         {
-            return with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
+            return tf_with(ops.name_scope(name, "embedding_lookup", new { @params, ids }), scope =>
             {
                 name = scope;
                 int np = @params.Length;

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

@@ -28,7 +28,7 @@ namespace Tensorflow
             if (dtype == image.dtype)
                 return array_ops.identity(image, name: name);
 
-            return with(ops.name_scope(name, "convert_image", image), scope =>
+            return tf_with(ops.name_scope(name, "convert_image", image), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -350,6 +350,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor logical_and(Tensor x, Tensor y, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("LogicalAnd", name, args: new { x, y });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor squared_difference(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("SquaredDifference", name, args: new { x, y, name });

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -29,7 +29,7 @@ namespace Tensorflow
     {
         public static Tensor abs(Tensor x, string name = null)
         {
-            return with(ops.name_scope(name, "Abs", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "Abs", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.is_complex())
@@ -69,7 +69,7 @@ namespace Tensorflow
             if(base_type == x.dtype)
                 return x;
 
-            return with(ops.name_scope(name, "Cast", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -82,7 +82,7 @@ namespace Tensorflow
 
         public static Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
         {
-            return with(ops.name_scope(name, "Cumsum", new {x}), scope =>
+            return tf_with(ops.name_scope(name, "Cumsum", new {x}), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -110,7 +110,7 @@ namespace Tensorflow
         /// <returns>`x / y` returns the quotient of x and y.</returns>
         public static Tensor div(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "div", (x, y)), name_scope =>
+            return tf_with(ops.name_scope(name, "div", (x, y)), name_scope =>
             {
                 name = name_scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -146,7 +146,7 @@ namespace Tensorflow
         /// </remarks>
         public static Tensor div_no_nan(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "div_no_nan", (x, y)), name_scope =>
+            return tf_with(ops.name_scope(name, "div_no_nan", (x, y)), name_scope =>
             {
                 name = name_scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -229,7 +229,7 @@ namespace Tensorflow
 
         public static Tensor sign(Tensor x, string name = null)
         {
-            return with(ops.name_scope(name, "Sign", new {x}), scope =>
+            return tf_with(ops.name_scope(name, "Sign", new {x}), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 return gen_math_ops.sign(x);
@@ -337,7 +337,7 @@ namespace Tensorflow
         /// <returns> The reduced tensor.</returns>
         public static Tensor reduce_logsumexp(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
         {
-            return with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
+            return tf_with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
             {
                 var raw_max = reduce_max(input_tensor, axis, true);
                 var my_max = array_ops.stop_gradient(array_ops.where(gen_math_ops.is_finite(raw_max), raw_max, array_ops.zeros_like(raw_max)));
@@ -497,7 +497,7 @@ namespace Tensorflow
             if (delta == null)
                 delta = 1;
 
-            return with(ops.name_scope(name, "Range", new { start, limit, delta }), scope =>
+            return tf_with(ops.name_scope(name, "Range", new { start, limit, delta }), scope =>
             {
                 name = scope;
                 var start1 = ops.convert_to_tensor(start, name: "start");
@@ -510,7 +510,7 @@ namespace Tensorflow
 
         public static Tensor floordiv(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "floordiv", new { x, y }), scope =>
+            return tf_with(ops.name_scope(name, "floordiv", new { x, y }), scope =>
             {
                 return gen_math_ops.floor_div(x, y, scope);
             });
@@ -527,7 +527,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            with(ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
+            tf_with(ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
             {
                 name = scope;
 
@@ -551,7 +551,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            with(ops.name_scope(name, "MatMul", new Tensor[] { x, y }), scope =>
+            tf_with(ops.name_scope(name, "MatMul", new Tensor[] { x, y }), scope =>
             {
                 name = scope;
 
@@ -576,7 +576,7 @@ namespace Tensorflow
             if (dt.is_floating() || dt.is_integer())
                 return x;
 
-            return with(ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
+            return tf_with(ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
             {
 
                 return x;
@@ -591,7 +591,7 @@ namespace Tensorflow
 
         public static Tensor _truediv_python3(Tensor x, Tensor y, string name = null)
         {
-            return with(ops.name_scope(name, "truediv", new { x, y }), scope =>
+            return tf_with(ops.name_scope(name, "truediv", new { x, y }), scope =>
             {
                 name = scope;
                 var x_dtype = x.dtype.as_base_dtype();

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

@@ -34,7 +34,7 @@ namespace Tensorflow
             float epsilon = 1e-12f,
             string name = null)
         {
-            return with(ops.name_scope(name, "l2_normalize", new { x }), scope =>
+            return tf_with(ops.name_scope(name, "l2_normalize", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 var sq = math_ops.square(x);
@@ -57,7 +57,7 @@ namespace Tensorflow
             string name = null,
             bool keep_dims = false)
         {
-            return with(ops.name_scope(name, "moments", new { x, axes }), scope =>
+            return tf_with(ops.name_scope(name, "moments", new { x, axes }), scope =>
             {
                 // The dynamic range of fp16 is too limited to support the collection of
                 // sufficient statistics. As a workaround we simply perform the operations
@@ -123,7 +123,7 @@ namespace Tensorflow
         /// <returns>number of nonzero values with type dtype</returns>
         private static Tensor _count_nonzero(Tensor input_tensor, TF_DataType dtype = TF_DataType.TF_INT64)
         {
-            return with(ops.name_scope("count_nonzero", "count_nonzero", new { input_tensor }), scope =>
+            return tf_with(ops.name_scope("count_nonzero", "count_nonzero", new { input_tensor }), scope =>
             {
                 var zero = array_ops.zeros(new NumSharp.Shape(), dtype: input_tensor.dtype);
                 var nonzero_count = math_ops.reduce_sum(
@@ -140,7 +140,7 @@ namespace Tensorflow
         /// <returns>The fraction of zeros in value, with type float32.</returns>
         public static Tensor zero_fraction(Tensor value, string name = null)
         {
-            return with(ops.name_scope(name, "zero_fraction", new { value }), scope =>
+            return tf_with(ops.name_scope(name, "zero_fraction", new { value }), scope =>
             {
                 value = ops.convert_to_tensor(value, name: "value");
                 Tensor size = array_ops.size(value, out_type: dtypes.int64);
@@ -153,7 +153,7 @@ namespace Tensorflow
                         () => _count_nonzero(value, dtype: dtypes.int64)
                         );
 
-                with(ops.name_scope("counts_to_fraction"), count_scope =>
+                tf_with(ops.name_scope("counts_to_fraction"), count_scope =>
                 {
                     var num_zero = math_ops.subtract(math_ops.cast(size, TF_DataType.TF_INT64), num_nonzero);
                     var num_zero_float32 = math_ops.cast(num_zero, dtype: dtypes.float32);

src/TensorFlowNET.Core/Operations/nn_ops.cs

@@ -50,7 +50,7 @@ namespace Tensorflow
             string data_format = null, 
             string name = null)
         {
-            return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
+            return Python.tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");
@@ -70,7 +70,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor dropout_v2(Tensor x, Tensor rate, Tensor noise_shape = null, int? seed = null, string name = null)
         {
-            return with(ops.name_scope(name, "dropout", x), scope =>
+            return tf_with(ops.name_scope(name, "dropout", x), scope =>
             {
                 name = scope;
                 x = ops.convert_to_tensor(x, name: "x");
@@ -134,7 +134,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null)
         {
-            return with(ops.name_scope(name, "MaxPool", value), scope =>
+            return tf_with(ops.name_scope(name, "MaxPool", value), scope =>
             {
                 name = scope;
                 value = ops.convert_to_tensor(value, name: "input");
@@ -171,7 +171,7 @@ namespace Tensorflow
             Tensor logits = null, string name = null)
         {
             // Reshape logits and labels to rank 2.
-            return with(ops.name_scope(name, default_name: "SparseSoftmaxCrossEntropyWithLogits", (labels, logits)), delegate
+            return tf_with(ops.name_scope(name, default_name: "SparseSoftmaxCrossEntropyWithLogits", (labels, logits)), delegate
             {
                 labels = ops.convert_to_tensor(labels);
                 logits = ops.convert_to_tensor(logits);
@@ -206,7 +206,7 @@ namespace Tensorflow
             int axis = -1,
             string name = null)
         {
-            return with(ops.name_scope(name, "softmax_cross_entropy_with_logits", new { logits, labels }), scope =>
+            return tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits", new { logits, labels }), scope =>
             {
                 name = scope;
                 var precise_logits = logits;

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

@@ -37,7 +37,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return with(ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
+            return tf_with(ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
             {
                 var shape_tensor = _ShapeTensor(shape);
                 var mean_tensor = ops.convert_to_tensor(mean, dtype: dtype, name: "mean");
@@ -67,7 +67,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            return tf_with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
                 var tensorShape = _ShapeTensor(shape);
@@ -85,7 +85,7 @@ namespace Tensorflow
             int? seed = null,
             string name = null)
         {
-            return with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            return tf_with(ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
             {
                 name = scope;
                 var minTensor = ops.convert_to_tensor(minval, dtype: dtype, name: "min");
@@ -110,7 +110,7 @@ namespace Tensorflow
             int? seed = null,
             string name = null)
         {
-            return with(ops.name_scope(name, "truncated_normal", new { shape, mean, stddev }), scope =>
+            return tf_with(ops.name_scope(name, "truncated_normal", new { shape, mean, stddev }), scope =>
             {
                 name = scope;
                 var shape_tensor = _ShapeTensor(shape);

src/TensorFlowNET.Core/Operations/weights_broadcast_ops.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
     {
         public static Tensor broadcast_weights(Tensor weights, Tensor values)
         {
-            return with(ops.name_scope(null, "broadcast_weights", (weights, values)), scope =>
+            return tf_with(ops.name_scope(null, "broadcast_weights", (weights, values)), scope =>
             {
                 values = ops.convert_to_tensor(values, name: "values");
                 weights = ops.convert_to_tensor(

src/TensorFlowNET.Core/Python.cs

@@ -75,7 +75,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static void with(IPython py, Action<IPython> action)
+        public static void tf_with(IPython py, Action<IPython> action)
         {
             try
             {
@@ -95,7 +95,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static void with<T>(T py, Action<T> action) where T : IPython
+        public static void tf_with<T>(T py, Action<T> action) where T : IPython
         {
             try
             {
@@ -115,7 +115,7 @@ namespace Tensorflow
         }
 
         [DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
-        public static TOut with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
+        public static TOut tf_with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
         {
             try
             {

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -24,7 +24,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class BaseSession
+    public class BaseSession : DisposableObject
     {
         protected Graph _graph;
         protected bool _opened;
@@ -32,28 +32,23 @@ namespace Tensorflow
         protected int _current_version;
         protected byte[] _target;
         protected IntPtr _session;
-        public Status Status;
         public Graph graph => _graph;
 
         public BaseSession(string target = "", Graph g = null, SessionOptions opts = null)
         {
             _graph = g is null ? ops.get_default_graph() : g;
-
+            _graph.as_default();
             _target = UTF8Encoding.UTF8.GetBytes(target);
 
             SessionOptions newOpts = null;
             if (opts == null)
-                newOpts = c_api.TF_NewSessionOptions();
-
-            Status = new Status();
+                newOpts = new SessionOptions();
 
-            _session = c_api.TF_NewSession(_graph, opts ?? newOpts, Status);
+            var status = new Status();
 
-            // dispose newOpts
-            if (opts == null)
-                c_api.TF_DeleteSessionOptions(newOpts);
+            _session = c_api.TF_NewSession(_graph, opts ?? newOpts, status);
 
-            Status.Check(true);
+            status.Check(true);
         }
 
         public virtual NDArray run(object fetches, params FeedItem[] feed_dict)
@@ -325,5 +320,19 @@ namespace Tensorflow
         {
 
         }
+
+        public void close()
+        {
+            Dispose();
+        }
+
+        protected override void DisposeUnManagedState(IntPtr handle)
+        {
+            using (var status = new Status())
+            {
+                c_api.TF_DeleteSession(handle, status);
+                status.Check(true);
+            }
+        }
     }
 }

src/TensorFlowNET.Core/Sessions/Session.cs

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using System.Runtime.InteropServices;
 
 namespace Tensorflow
 {
@@ -26,8 +27,8 @@ namespace Tensorflow
 
         }
 
-        public Session(IntPtr handle)
-            : base("", null, null)
+        public Session(IntPtr handle, Graph g = null)
+            : base("", g, null)
         {
             _session = handle;
         }
@@ -36,7 +37,7 @@ namespace Tensorflow
             : base("", g, opts)
         {
             if (s == null)
-                s = Status;
+                s = new Status();
         }
 
         public Session as_default()
@@ -49,35 +50,32 @@ namespace Tensorflow
         {
             var graph = c_api.TF_NewGraph();
             var status = new Status();
-            var opt = c_api.TF_NewSessionOptions();
+            var opt = new SessionOptions();
 
+            var tags = new string[] { "serve" };
             var buffer = new TF_Buffer();
-            var sess = c_api.TF_LoadSessionFromSavedModel(opt, IntPtr.Zero, path, new string[0], 0, graph, ref buffer, status);
 
-            //var bytes = new Buffer(buffer.data).Data;
-            //var meta_graph = MetaGraphDef.Parser.ParseFrom(bytes);
-
-            status.Check();
-
-            new Graph(graph).as_default();
-
-            return sess;
+            var sess = c_api.TF_LoadSessionFromSavedModel(opt,
+                IntPtr.Zero,
+                path,
+                tags,
+                tags.Length,
+                graph,
+                ref buffer,
+                status);
+
+            // load graph bytes
+            // var data = new byte[buffer.length];
+            // Marshal.Copy(buffer.data, data, 0, (int)buffer.length);
+            // var meta_graph = MetaGraphDef.Parser.ParseFrom(data);*/
+            status.Check(true);
+
+            return new Session(sess, g: new Graph(graph).as_default());
         }
 
         public static implicit operator IntPtr(Session session) => session._session;
         public static implicit operator Session(IntPtr handle) => new Session(handle);
 
-        public void close()
-        {
-            Dispose();
-        }
-
-        public void Dispose()
-        {
-            c_api.TF_DeleteSession(_session, Status);
-            Status.Dispose();
-        }
-
         public void __enter__()
         {
 

src/TensorFlowNET.Core/Sessions/SessionOptions.cs

@@ -20,37 +20,34 @@ using System.Runtime.InteropServices;
 
 namespace Tensorflow
 {
-    public class SessionOptions : IDisposable
+    public class SessionOptions : DisposableObject
     {
-        private IntPtr _handle;
-        private Status _status;
-
-        public unsafe SessionOptions()
+        public SessionOptions()
         {
-            var opts = c_api.TF_NewSessionOptions();
-            _handle = opts;
-            _status = new Status();
+            _handle = c_api.TF_NewSessionOptions();
         }
 
-        public unsafe SessionOptions(IntPtr handle)
+        public SessionOptions(IntPtr handle)
         {
             _handle = handle;
         }
 
-        public void Dispose()
-        {
-            c_api.TF_DeleteSessionOptions(_handle);
-            _status.Dispose();
-        }
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteSessionOptions(handle);
 
-        public Status SetConfig(ConfigProto config)
+        public void SetConfig(ConfigProto config)
         {
             var bytes = config.ToByteArray();
             var proto = Marshal.AllocHGlobal(bytes.Length);
             Marshal.Copy(bytes, 0, proto, bytes.Length);
-            c_api.TF_SetConfig(_handle, proto, (ulong)bytes.Length, _status);
-            _status.Check(false);
-            return _status;
+
+            using (var status = new Status())
+            {
+                c_api.TF_SetConfig(_handle, proto, (ulong)bytes.Length, status);
+                status.Check(false);
+            }
+
+            Marshal.FreeHGlobal(proto);
         }
 
         public static implicit operator IntPtr(SessionOptions opts) => opts._handle;

src/TensorFlowNET.Core/Status/Status.cs

@@ -22,10 +22,8 @@ namespace Tensorflow
     /// TF_Status holds error information. It either has an OK code, or
     /// else an error code with an associated error message.
     /// </summary>
-    public class Status : IDisposable
+    public class Status : DisposableObject
     {
-        protected IntPtr _handle;
-
         /// <summary>
         /// Error message
         /// </summary>
@@ -67,22 +65,7 @@ namespace Tensorflow
             return status._handle;
         }
 
-        public void Dispose()
-        {
-            IntPtr h = IntPtr.Zero;
-            lock (this)
-            {
-                h = _handle;
-                _handle = IntPtr.Zero;
-            }
-            if (h != IntPtr.Zero)
-                c_api.TF_DeleteStatus(h);
-            GC.SuppressFinalize(this);
-        }
-
-        ~Status()
-        {
-            Dispose();
-        }
+        protected override void DisposeUnManagedState(IntPtr handle)
+            => c_api.TF_DeleteStatus(handle);
     }
 }

src/TensorFlowNET.Core/Summaries/Summary.cs

@@ -55,7 +55,7 @@ namespace Tensorflow.Summaries
         /// <returns></returns>
         public Tensor merge(Tensor[] inputs, string[] collections = null, string name = null)
         {
-            return with(ops.name_scope(name, "Merge", inputs), delegate
+            return tf_with(ops.name_scope(name, "Merge", inputs), delegate
             {
                 var val = gen_logging_ops.merge_summary(inputs: inputs, name: name);
                 collect(val, collections?.ToList(), new List<string>());
@@ -88,7 +88,7 @@ namespace Tensorflow.Summaries
         public (string, string) summary_scope(string name, string family = null, string default_name = null, Tensor[] values = null)
         {
             string scope_base_name = string.IsNullOrEmpty(family) ? name : $"{family}/{name}";
-            return with(ops.name_scope(scope_base_name, default_name: default_name, values), scope =>
+            return tf_with(ops.name_scope(scope_base_name, default_name: default_name, values), scope =>
             {
                 var tag = scope._name_scope;
                 if (string.IsNullOrEmpty(family))

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -5,7 +5,7 @@
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
     <TargetTensorFlow>1.14.0</TargetTensorFlow>
-    <Version>0.10.3</Version>
+    <Version>0.10.8</Version>
     <Authors>Haiping Chen, Meinrad Recheis</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -17,7 +17,7 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET, C#</PackageTags>
     <Description>Google's TensorFlow full binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.10.3.0</AssemblyVersion>
+    <AssemblyVersion>0.10.8.0</AssemblyVersion>
     <PackageReleaseNotes>Changes since v0.9.0:
 
 1. Added full connected Convolution Neural Network example.
@@ -29,9 +29,15 @@ Docs: https://tensorflownet.readthedocs.io</Description>
 7. Add BatchMatMulGrad.
 8. Upgrade NumSharp.
 9. Fix strided_slice_grad type convention error.
-10. Add AbsGrad.</PackageReleaseNotes>
+10. Add AbsGrad.
+11. Fix Session.LoadFromSavedModel(string).
+12. Add Tensor operator overloads.
+13. Fix default graph and operation issue when import model.
+14. Fix TF_String endcode and decode.
+15. Fix Tensor memory leak.
+16. Rename with to tf_with that is only used to build graph purpose.</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.10.3.0</FileVersion>
+    <FileVersion>0.10.8.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
     <PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
     <SignAssembly>true</SignAssembly>
@@ -62,7 +68,7 @@ Docs: https://tensorflownet.readthedocs.io</Description>
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="Google.Protobuf" Version="3.9.0" />
+    <PackageReference Include="Google.Protobuf" Version="3.5.1" />
     <PackageReference Include="NumSharp" Version="0.10.6" />
   </ItemGroup>
 

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

@@ -15,6 +15,7 @@
 ******************************************************************************/
 
 using System;
+using System.Linq;
 using static Tensorflow.Python;
 
 namespace Tensorflow
@@ -63,22 +64,56 @@ namespace Tensorflow
         public static Tensor operator *(long constant, Tensor tensor) => BinaryOpWrapper("mul", constant, tensor);
         public static Tensor operator *(ulong constant, Tensor tensor) => BinaryOpWrapper("mul", constant, tensor);
 
-        public static Tensor operator /(Tensor x, Tensor y) => BinaryOpWrapper("truediv", x, y);
-        public static Tensor operator /(Tensor x, float y) => BinaryOpWrapper("truediv", x, y);
+        private static readonly TF_DataType[] _intTfDataTypes = {
+            TF_DataType.TF_INT8, TF_DataType.TF_INT16, TF_DataType.TF_INT32, TF_DataType.TF_INT64,
+            TF_DataType.TF_QINT8, TF_DataType.TF_QINT16, TF_DataType.TF_QINT32,
+            TF_DataType.TF_UINT8, TF_DataType.TF_UINT16, TF_DataType.TF_UINT32, TF_DataType.TF_UINT64
+        };
+        public static Tensor operator /(double x, Tensor y) => BinaryOpWrapper("truediv", x, y);
         public static Tensor operator /(float x, Tensor y) => BinaryOpWrapper("truediv", x, y);
+        public static Tensor operator /(int x, Tensor y) => BinaryOpWrapper("floordiv", x, y);
+        public static Tensor operator /(Tensor x, Tensor y) =>
+            _intTfDataTypes.Contains(x._dtype)
+                ? BinaryOpWrapper("floordiv", x, y)
+                : BinaryOpWrapper("truediv", x, y);
+        public static Tensor operator /(Tensor x, int y) => BinaryOpWrapper("floordiv", x, y);
+        public static Tensor operator /(Tensor x, float y) => BinaryOpWrapper("truediv", x, y);
         public static Tensor operator /(Tensor x, double y) => BinaryOpWrapper("truediv", x, y);
 
         public static Tensor operator %(Tensor x, Tensor y) => BinaryOpWrapper("mod", x, y);
 
+        public static Tensor operator >(double x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(float x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(int x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(Tensor x, Tensor y) => gen_math_ops.greater(x, y);
         public static Tensor operator >(Tensor x, int y) => gen_math_ops.greater(x, y);
-        public static Tensor operator >=(Tensor x, Tensor y) => gen_math_ops.greater_equal(x, y);
         public static Tensor operator >(Tensor x, float y) => gen_math_ops.greater(x, y);
         public static Tensor operator >(Tensor x, double y) => gen_math_ops.greater(x, y);
+
+        public static Tensor operator <(double x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(float x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(int x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(Tensor x, Tensor y) => gen_math_ops.less(x, y);
         public static Tensor operator <(Tensor x, int y) => gen_math_ops.less(x, y);
-        public static Tensor operator <=(Tensor x, Tensor y) => gen_math_ops.less_equal(x, y);
         public static Tensor operator <(Tensor x, float y) => gen_math_ops.less(x, y);
         public static Tensor operator <(Tensor x, double y) => gen_math_ops.less(x, y);
 
+        public static Tensor operator >=(double x, Tensor y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(float x, Tensor y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(int x, Tensor y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(Tensor x, Tensor y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(Tensor x, int y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(Tensor x, float y) => gen_math_ops.greater_equal(x, y);
+        public static Tensor operator >=(Tensor x, double y) => gen_math_ops.greater_equal(x, y);
+
+        public static Tensor operator <=(int x, Tensor y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(float x, Tensor y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(double x, Tensor y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(Tensor x, Tensor y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(Tensor x, int y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(Tensor x, float y) => gen_math_ops.less_equal(x, y);
+        public static Tensor operator <=(Tensor x, double y) => gen_math_ops.less_equal(x, y);
+
         private static Tensor BinaryOpWrapper<Tx, Ty>(string name, Tx x, Ty y)
         {
             TF_DataType dtype = TF_DataType.DtInvalid;
@@ -88,7 +123,7 @@ namespace Tensorflow
                 dtype = tr.dtype.as_base_dtype();
 
             var namescope = ops.name_scope(null, name, new { x, y });
-            return with(namescope, scope =>
+            return tf_with(namescope, scope =>
             {
                 Tensor result = null;
                 var x1 = ops.convert_to_tensor(x, dtype: dtype, name: "x");
@@ -99,6 +134,9 @@ namespace Tensorflow
                     case "add":
                         result = gen_math_ops.add(x1, y1, name: scope);
                         break;
+                    case "floordiv":
+                        result = gen_math_ops.floor_div(x1, y1, name: scope);
+                        break;
                     case "truediv":
                         result = gen_math_ops.real_div(x1, y1, name: scope);
                         break;

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -19,6 +19,7 @@ using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Runtime.InteropServices;
+using System.Text;
 using Tensorflow.Framework;
 using static Tensorflow.Python;
 
@@ -28,10 +29,8 @@ namespace Tensorflow
     /// A tensor is a generalization of vectors and matrices to potentially higher dimensions. 
     /// Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes.
     /// </summary>
-    public partial class Tensor : IDisposable, ITensorOrOperation, _TensorLike
+    public partial class Tensor : DisposableObject, ITensorOrOperation, _TensorLike
     {
-        private IntPtr _handle;
-
         private int _id;
         private Operation _op;
 
@@ -48,8 +47,6 @@ namespace Tensorflow
         private int _value_index;
         public int value_index => _value_index;
 
-        private Status status = new Status();
-
         private TF_DataType _dtype = TF_DataType.DtInvalid;
         public TF_DataType dtype => _handle == IntPtr.Zero ? _dtype : c_api.TF_TensorType(_handle);
 
@@ -76,6 +73,7 @@ namespace Tensorflow
 
                 if (_handle == IntPtr.Zero)
                 {
+                    var status = new Status();
                     c_api.TF_GraphGetTensorShape(op.graph, _as_tf_output(), dims, rank, status);
                     status.Check();
                 }
@@ -90,6 +88,8 @@ namespace Tensorflow
 
             set
             {
+                var status = new Status();
+
                 if (value == null)
                     c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), null, -1, status);
                 else
@@ -131,8 +131,11 @@ namespace Tensorflow
             {
                 if (_handle == IntPtr.Zero)
                 {
+                    var status = new Status();
                     var output = _as_tf_output();
-                    return c_api.TF_GraphGetTensorNumDims(op.graph, output, status);
+                    int ndim = c_api.TF_GraphGetTensorNumDims(op.graph, output, status);
+                    status.Check();
+                    return ndim;
                 }
                 else
                 {
@@ -184,6 +187,41 @@ namespace Tensorflow
             return data;
         }
 
+        public unsafe string[] StringData()
+        {
+            //
+            // TF_STRING tensors are encoded with a table of 8-byte offsets followed by TF_StringEncode-encoded bytes.
+            // [offset1, offset2,...,offsetn, s1size, s1bytes, s2size, s2bytes,...,snsize,snbytes]
+            //
+            long size = 1;
+            foreach (var s in TensorShape.Dimensions)
+                size *= s;
+
+            var buffer = new byte[size][];
+            var src = c_api.TF_TensorData(_handle);
+            var srcLen = (IntPtr)(src.ToInt64() + (long)bytesize);
+            src += (int)(size * 8);
+            for (int i = 0; i < buffer.Length; i++)
+            {
+                using (var status = new Status())
+                {
+                    IntPtr dst = IntPtr.Zero;
+                    UIntPtr dstLen = UIntPtr.Zero;
+                    var read = c_api.TF_StringDecode((byte*)src, (UIntPtr)(srcLen.ToInt64() - src.ToInt64()), (byte**)&dst, &dstLen, status);
+                    status.Check(true);
+                    buffer[i] = new byte[(int)dstLen];
+                    Marshal.Copy(dst, buffer[i], 0, buffer[i].Length);
+                    src += (int)read;
+                }
+            }
+
+            var _str = new string[buffer.Length];
+            for (int i = 0; i < _str.Length; i++)
+                _str[i] = Encoding.UTF8.GetString(buffer[i]);
+
+            return _str;
+        }
+
         public Tensor MaybeMove()
         {
             var tensor = c_api.TF_TensorMaybeMove(_handle);
@@ -262,7 +300,7 @@ namespace Tensorflow
                 index += 1;
             }
 
-            return with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
+            return tf_with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
             {
                 string name = scope;
                 if (begin != null)
@@ -311,7 +349,7 @@ namespace Tensorflow
                 index += 1;
             }
 
-            return with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
+            return tf_with(ops.name_scope(null, "strided_slice", new { begin, end, strides }), scope =>
             {
                 string name = scope;
                 if (begin != null)
@@ -354,26 +392,12 @@ namespace Tensorflow
             return $"tf.Tensor '{name}' shape=({string.Join(",", shape)}) dtype={dtype}";
         }
 
-        public void Dispose()
+        protected override void DisposeUnManagedState(IntPtr handle)
         {
-            IntPtr h=IntPtr.Zero;
-            lock (this)
+            if(handle != IntPtr.Zero)
             {
-                h = _handle;
-                _handle=IntPtr.Zero;
+                c_api.TF_DeleteTensor(handle);
             }
-            if (h != IntPtr.Zero)
-                c_api.TF_DeleteTensor(_handle);
-            status.Dispose();
-            GC.SuppressFinalize(this);
-        }
-
-        /// <summary>
-        /// Dispose the tensor when it gets garbage collected
-        /// </summary>
-        ~Tensor()
-        {
-            Dispose();
         }
 
         public bool IsDisposed

src/TensorFlowNET.Core/Tensors/c_api.tensor.cs

@@ -32,6 +32,12 @@ namespace Tensorflow
         [DllImport(TensorFlowLibName)]
         public static extern IntPtr TF_AllocateTensor(TF_DataType dtype, IntPtr dims, int num_dims, UIntPtr len);
 
+        [DllImport(TensorFlowLibName)]
+        public static extern IntPtr TF_AllocateTensor(TF_DataType dtype, long[] dims, int num_dims, ulong len);
+
+        [DllImport(TensorFlowLibName)]
+        public static extern IntPtr TF_AllocateTensor(TF_DataType dtype, long[] dims, int num_dims, UIntPtr len);
+
         /// <summary>
         /// returns the sizeof() for the underlying type corresponding to the given TF_DataType enum value.
         /// </summary>
@@ -150,5 +156,8 @@ namespace Tensorflow
         /// <returns></returns>
         [DllImport(TensorFlowLibName)]
         public static extern ulong TF_StringDecode(IntPtr src, ulong src_len, IntPtr dst, ref ulong dst_len, IntPtr status);
+
+        [DllImport(TensorFlowLibName)]
+        public static extern unsafe UIntPtr TF_StringDecode(byte* src, UIntPtr src_len, byte** dst, UIntPtr* dst_len, IntPtr status);
     }
 }

src/TensorFlowNET.Core/Train/AdamOptimizer.cs

@@ -81,7 +81,7 @@ namespace Tensorflow.Train
             var m = get_slot(var, "m");
             var m_scaled_g_values = grad * (1 - beta1_t);
             var m_t = state_ops.assign(m, m * beta1_t, use_locking: _use_locking);
-            with(ops.control_dependencies(new[] { m_t }), delegate
+            tf_with(ops.control_dependencies(new[] { m_t }), delegate
             {
                 m_t = scatter_add(m, indices, m_scaled_g_values);
             });
@@ -89,7 +89,7 @@ namespace Tensorflow.Train
             var v = get_slot(var, "v");
             var v_scaled_g_values = (grad * grad) * (1 - beta2_t);
             var v_t = state_ops.assign(v, v * beta2_t, use_locking: _use_locking);
-            with(ops.control_dependencies(new[] { v_t }), delegate
+            tf_with(ops.control_dependencies(new[] { v_t }), delegate
             {
                 v_t = scatter_add(v, indices, v_scaled_g_values);
             });
@@ -117,7 +117,7 @@ namespace Tensorflow.Train
             var operations = new List<ITensorOrOperation>();
             operations.AddRange(update_ops);
 
-            with(ops.control_dependencies(update_ops), delegate
+            tf_with(ops.control_dependencies(update_ops), delegate
             {
                 var (beta1_power, beta2_power) = _get_beta_accumulators();
                 ops.colocate_with(beta1_power);

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -151,7 +151,7 @@ namespace Tensorflow
             _create_slots(var_list);
 
             var update_ops = new List<Operation>();
-            return with(ops.name_scope(name, Name), scope =>
+            return tf_with(ops.name_scope(name, Name), scope =>
             {
                 name = scope;
                 _prepare();
@@ -162,7 +162,7 @@ namespace Tensorflow
                         continue;
 
                     var scope_name = var.op.name;
-                    with(ops.name_scope("update_" + scope_name), scope2 =>
+                    tf_with(ops.name_scope("update_" + scope_name), scope2 =>
                     {
                         var op = processor.update_op(this, grad);
                         update_ops.Add(op);
@@ -176,7 +176,7 @@ namespace Tensorflow
                 }
                 else
                 {
-                    with(ops.control_dependencies(new object[] {_finish(update_ops.ToArray(), "update")}), dep =>
+                    tf_with(ops.control_dependencies(new object[] {_finish(update_ops.ToArray(), "update")}), dep =>
                     {
                         ops.colocate_with(global_step);
                         // TODO: port this if branch once ResourceVariable has been ported!

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

@@ -102,7 +102,7 @@ namespace Tensorflow
             Tensor save_tensor = null;
             Operation restore_op = null;
 
-            return with(ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
+            return tf_with(ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Train/SlotCreator.cs

@@ -57,7 +57,7 @@ namespace Tensorflow.Train
         {
             var validate_shape = shape.is_fully_defined();
             var prefix = primary.op.name;
-            return with(new variable_scope(string.Empty, prefix + "/" + name), delegate
+            return tf_with(new variable_scope(string.Empty, prefix + "/" + name), delegate
             {
                 return _create_slot_var(primary, initializer, "", validate_shape, shape, dtype);
             });

src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs

@@ -32,7 +32,7 @@ namespace Tensorflow
         private static Tensor op_helper<T>(string default_name, RefVariable x, T y)
         {
             var tensor1 = x.value();
-            return with(ops.name_scope(null, default_name, new { tensor1, y }), scope => {
+            return tf_with(ops.name_scope(null, default_name, new { tensor1, y }), scope => {
                 var tensor2 = ops.convert_to_tensor(y, tensor1.dtype.as_base_dtype(), "y");
                 return gen_math_ops.add(tensor1, tensor2, scope);
             });

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -134,7 +134,7 @@ namespace Tensorflow
 
             ops.init_scope();
             var values = init_from_fn ? new object[0] : new object[] { initial_value };
-            with(ops.name_scope(name, "Variable", values), scope =>
+            tf_with(ops.name_scope(name, "Variable", values), scope =>
             {
                 name = scope;
                 if (init_from_fn)
@@ -148,7 +148,7 @@ namespace Tensorflow
                         List = new AttrValue.Types.ListValue()
                     };
                     attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
-                    with(ops.name_scope("Initializer"), scope2 =>
+                    tf_with(ops.name_scope("Initializer"), scope2 =>
                     {
                         _initial_value = (initial_value as Func<Tensor>)();
                         _initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -56,7 +56,7 @@ namespace Tensorflow
             VariableAggregation aggregation= VariableAggregation.None)
         {
             string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
-            return with(ops.name_scope(null), scope =>
+            return tf_with(ops.name_scope(null), scope =>
             {
                 if (dtype == TF_DataType.DtInvalid)
                     dtype = _dtype;

src/TensorFlowNET.Core/ops.py.cs

@@ -295,7 +295,7 @@ namespace Tensorflow
             // inner_device_stack = default_graph._device_function_stack
             // var outer_context = default_graph.as_default;
 
-            with(ops.control_dependencies(null), delegate
+            tf_with(ops.control_dependencies(null), delegate
             {
                 var outer_graph = get_default_graph();
                 // outer_device_stack = None

tensorflowlib/README.md

@@ -16,6 +16,8 @@ Here are some pre-built TensorFlow binaries you can use for each platform:
   - CPU-only: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-1.14.0.zip
   - GPU-enabled: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-1.14.0.zip
 
+
+
 ### Run in Linux
 
 `Install-Package TensorFlow.NET`
@@ -31,10 +33,21 @@ sudo apt install libgdiplus
 
 More information about [System.Drawing on Linux](<https://www.hanselman.com/blog/HowDoYouUseSystemDrawingInNETCore.aspx>).
 
+
+
 ### Run in Mac OS
 
-### GPU Tensorflow for windows
-Before running verify you installed  CUDA and cuDNN
+
+
+### Tensorflow GPU for Windows
+
+Before running verify you installed  CUDA and cuDNN (TensorFlow v1.14 is compatible with CUDA v10.0 and cuDNN v7.4), and make sure the corresponding cuda version is compatible. 
+
+```powershell
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
+```
+
+
 
 ### Build from source for Windows
 

test/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs

@@ -77,7 +77,7 @@ let run()=
     let init = tf.global_variables_initializer()
     
     
-    Tensorflow.Python.``with``(tf.Session(), fun (sess:Session) ->
+    Tensorflow.Python.``tf_with``(tf.Session(), fun (sess:Session) ->
         sess.run(init)  |> ignore  
         // Loop over epochs
         for epoch in [0..training_epochs] do

test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs

@@ -18,7 +18,7 @@ using NumSharp;
 using System;
 using System.Diagnostics;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -39,7 +39,7 @@ namespace TensorFlowNET.Examples
         public int? test_size = null;
         public int batch_size = 1024; // The number of samples per batch
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
         NDArray full_data_x;
         int num_steps = 20; // Total steps to train
         int k = 25; // The number of clusters
@@ -52,29 +52,41 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
             var graph = ImportGraph();
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 Train(sess);
-            });
+            }
 
             return accuray_test > 0.70;
         }
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size:validation_size, test_size:test_size);
-            full_data_x = mnist.train.data;
+            var loader = new MnistModelLoader();
+
+            var setting = new ModelLoadSetting
+            {
+                TrainDir = ".resources/mnist",
+                OneHot = true,
+                TrainSize = train_size,
+                ValidationSize = validation_size,
+                TestSize = test_size
+            };
+
+            mnist = loader.LoadAsync(setting).Result;
+
+            full_data_x = mnist.Train.Data;
 
             // download graph meta data
             string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/kmeans.meta";
-            Web.Download(url, "graph", "kmeans.meta");
+            loader.DownloadAsync(url, ".resources/graph", "kmeans.meta").Wait();
         }
 
         public Graph ImportGraph()
         {
             var graph = tf.Graph().as_default();
 
-            tf.train.import_meta_graph("graph/kmeans.meta");
+            tf.train.import_meta_graph(".resources/graph/kmeans.meta");
 
             return graph;
         }
@@ -132,7 +144,7 @@ namespace TensorFlowNET.Examples
             sw.Start();
             foreach (var i in range(idx.Length))
             {
-                var x = mnist.train.labels[i];
+                var x = mnist.Train.Labels[i];
                 counts[idx[i]] += x;
             }
 
@@ -153,7 +165,7 @@ namespace TensorFlowNET.Examples
             var accuracy_op = tf.reduce_mean(cast);
 
             // Test Model
-            var (test_x, test_y) = (mnist.test.data, mnist.test.labels);
+            var (test_x, test_y) = (mnist.Test.Data, mnist.Test.Labels);
             result = sess.run(accuracy_op, new FeedItem(X, test_x), new FeedItem(Y, test_y));
             accuray_test = result;
             print($"Test Accuracy: {accuray_test}");

test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs

@@ -71,7 +71,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
 
             // Start training
-            return with(tf.Session(), sess => 
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -114,7 +114,7 @@ namespace TensorFlowNET.Examples
                 Console.WriteLine($"Absolute mean square loss difference: {diff}");
 
                 return diff < 0.01;
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs

@@ -19,7 +19,7 @@ using System;
 using System.Diagnostics;
 using System.IO;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples
         private float learning_rate = 0.01f;
         private int display_step = 1;
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         public bool Run()
         {
@@ -73,7 +73,7 @@ namespace TensorFlowNET.Examples
 
             var sw = new Stopwatch();
 
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -84,11 +84,11 @@ namespace TensorFlowNET.Examples
                     sw.Start();
 
                     var avg_cost = 0.0f;
-                    var total_batch = mnist.train.num_examples / batch_size;
+                    var total_batch = mnist.Train.NumOfExamples / batch_size;
                     // Loop over all batches
                     foreach (var i in range(total_batch))
                     {
-                        var (batch_xs, batch_ys) = mnist.train.next_batch(batch_size);
+                        var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(batch_size);
                         // Run optimization op (backprop) and cost op (to get loss value)
                         var result = sess.run(new object[] { optimizer, cost },
                             new FeedItem(x, batch_xs),
@@ -115,32 +115,32 @@ namespace TensorFlowNET.Examples
                 var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1));
                 // Calculate accuracy
                 var accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
-                float acc = accuracy.eval(new FeedItem(x, mnist.test.data), new FeedItem(y, mnist.test.labels));
+                float acc = accuracy.eval(new FeedItem(x, mnist.Test.Data), new FeedItem(y, mnist.Test.Labels));
                 print($"Accuracy: {acc.ToString("F4")}");
 
                 return acc > 0.9;
-            });
+            }
         }
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: train_size, validationSize: validation_size, testSize: test_size).Result;
         }
 
         public void SaveModel(Session sess)
         {
             var saver = tf.train.Saver();
-            var save_path = saver.save(sess, "logistic_regression/model.ckpt");
-            tf.train.write_graph(sess.graph, "logistic_regression", "model.pbtxt", as_text: true);
+            var save_path = saver.save(sess, ".resources/logistic_regression/model.ckpt");
+            tf.train.write_graph(sess.graph, ".resources/logistic_regression", "model.pbtxt", as_text: true);
 
-            FreezeGraph.freeze_graph(input_graph: "logistic_regression/model.pbtxt",
+            FreezeGraph.freeze_graph(input_graph: ".resources/logistic_regression/model.pbtxt",
                               input_saver: "",
                               input_binary: false,
-                              input_checkpoint: "logistic_regression/model.ckpt",
+                              input_checkpoint: ".resources/logistic_regression/model.ckpt",
                               output_node_names: "Softmax",
                               restore_op_name: "save/restore_all",
                               filename_tensor_name: "save/Const:0",
-                              output_graph: "logistic_regression/model.pb",
+                              output_graph: ".resources/logistic_regression/model.pb",
                               clear_devices: true,
                               initializer_nodes: "");
         }
@@ -148,7 +148,7 @@ namespace TensorFlowNET.Examples
         public void Predict(Session sess)
         {
             var graph = new Graph().as_default();
-            graph.Import(Path.Join("logistic_regression", "model.pb"));
+            graph.Import(Path.Join(".resources/logistic_regression", "model.pb"));
 
             // restoring the model
             // var saver = tf.train.import_meta_graph("logistic_regression/tensorflowModel.ckpt.meta");
@@ -159,7 +159,7 @@ namespace TensorFlowNET.Examples
             var input = x.outputs[0];
 
             // predict
-            var (batch_xs, batch_ys) = mnist.train.next_batch(10);
+            var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10);
             var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)]));
 
             if (results.argmax() == (batch_ys[0] as NDArray).argmax())

test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs

@@ -48,14 +48,14 @@ namespace TensorFlowNET.Examples
             float y_max = X.amax(0).Data<float>(1) + 0.5f;
 
             var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30));
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 //var samples = np.vstack<float>(xx.ravel(), yy.ravel());
                 //samples = np.transpose(samples);
                 var array = np.Load<double[,]>(Path.Join("nb", "nb_example.npy"));
                 var samples = np.array(array).astype(np.float32);
                 var Z = sess.run(predict(samples));
-            });
+            }
 
             return true;
         }

test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs

@@ -17,7 +17,7 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
 namespace TensorFlowNET.Examples
@@ -31,7 +31,7 @@ namespace TensorFlowNET.Examples
     {
         public bool Enabled { get; set; } = true;
         public string Name => "Nearest Neighbor";
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
         NDArray Xtr, Ytr, Xte, Yte;
         public int? TrainSize = null;
         public int ValidationSize = 5000;
@@ -54,7 +54,7 @@ namespace TensorFlowNET.Examples
             float accuracy = 0f;
             // Initialize the variables (i.e. assign their default value)
             var init = tf.global_variables_initializer();
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the initializer
                 sess.run(init);
@@ -77,17 +77,17 @@ namespace TensorFlowNET.Examples
                 }
 
                 print($"Accuracy: {accuracy}");
-            });
+            }
 
             return accuracy > 0.8;
         }
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size:ValidationSize, test_size:TestSize);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: TrainSize, validationSize: ValidationSize, testSize: TestSize).Result;
             // In this example, we limit mnist data
-            (Xtr, Ytr) = mnist.train.next_batch(TrainSize==null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
-            (Xte, Yte) = mnist.test.next_batch(TestSize==null ? 200 : TestSize.Value / 100); // 200 for testing
+            (Xtr, Ytr) = mnist.Train.GetNextBatch(TrainSize == null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
+            (Xte, Yte) = mnist.Test.GetNextBatch(TestSize == null ? 200 : TestSize.Value / 100); // 200 for testing
         }
 
         public Graph ImportGraph()

test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs

@@ -90,7 +90,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
             float loss_value = 0;
             // Start tf session
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -110,7 +110,7 @@ namespace TensorFlowNET.Examples
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }
@@ -128,7 +128,7 @@ namespace TensorFlowNET.Examples
 
             float loss_value = 0;
             // Start tf session
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 sess.run(init);
                 var step = 0;
@@ -143,7 +143,7 @@ namespace TensorFlowNET.Examples
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
-            });
+            }
 
             return loss_value;
         }

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -134,7 +134,7 @@ namespace TensorFlowNET.Examples
                     3, 3, 2));
             var batchMul = tf.batch_matmul(firstTensor, secondTensor);
             var checkTensor = np.array<float>(0, 6, 0, 15, 0, 24, 3, 1, 6, 4, 9, 7, 6, 0, 15, 0, 24, 0);
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 var result = sess.run(batchMul);
                 Console.WriteLine(result.ToString());
@@ -152,7 +152,7 @@ namespace TensorFlowNET.Examples
                 //         [24,  0]]])
                 return np.reshape(result, 18)
                     .array_equal(checkTensor);
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -25,13 +25,13 @@ namespace TensorFlowNET.Examples
             var hello = tf.constant(str);
 
             // Start tf session
-            return with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 // Run the op
                 var result = sess.run(hello);
                 Console.WriteLine(result.ToString());
                 return result.ToString().Equals(str);
-            });
+            }
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/CIFAR10-CNN.cs

@@ -0,0 +1,74 @@
+/*****************************************************************************
+   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+******************************************************************************/
+
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+using TensorFlowDatasets;
+using static Tensorflow.Python;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// https://www.tensorflow.org/tutorials/images/deep_cnn
+    /// </summary>
+    public class CIFAR10_CNN : IExample
+    {
+        public bool Enabled { get; set; } = true;
+        public bool IsImportingGraph { get; set; } = false;
+
+        public string Name => "CIFAR-10 CNN";
+
+        public bool Run()
+        {
+            PrepareData();
+
+            return true;
+        }
+
+        public Graph BuildGraph()
+        {
+            throw new NotImplementedException();
+        }
+
+        public Graph ImportGraph()
+        {
+            throw new NotImplementedException();
+        }
+
+        public void Predict(Session sess)
+        {
+            throw new NotImplementedException();
+        }
+
+        public void PrepareData()
+        {
+            var tfds = new DatasetBuilder();
+            tfds.download_and_prepare();
+        }
+
+        public void Test(Session sess)
+        {
+            throw new NotImplementedException();
+        }
+
+        public void Train(Session sess)
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -16,11 +16,12 @@
 
 using NumSharp;
 using System;
+using System.Diagnostics;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
-namespace TensorFlowNET.Examples.ImageProcess
+namespace TensorFlowNET.Examples
 {
     /// <summary>
     /// Convolutional Neural Network classifier for Hand Written Digits
@@ -45,7 +46,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int epochs = 5; // accuracy > 98%
         int batch_size = 100;
         float learning_rate = 0.001f;
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         // Network configuration
         // 1st Convolutional Layer
@@ -78,11 +79,11 @@ namespace TensorFlowNET.Examples.ImageProcess
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            }
 
             return loss_test < 0.05 && accuracy_test > 0.98;
         }
@@ -91,7 +92,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         {
             var graph = new Graph().as_default();
 
-            with(tf.name_scope("Input"), delegate
+            tf_with(tf.name_scope("Input"), delegate
             {
                 // Placeholders for inputs (x) and outputs(y)
                 x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
@@ -106,25 +107,25 @@ namespace TensorFlowNET.Examples.ImageProcess
             var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
             var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
 
-            with(tf.variable_scope("Train"), delegate
+            tf_with(tf.variable_scope("Train"), delegate
             {
-                with(tf.variable_scope("Loss"), delegate
+                tf_with(tf.variable_scope("Loss"), delegate
                 {
                     loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss");
                 });
 
-                with(tf.variable_scope("Optimizer"), delegate
+                tf_with(tf.variable_scope("Optimizer"), delegate
                 {
                     optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
                 });
 
-                with(tf.variable_scope("Accuracy"), delegate
+                tf_with(tf.variable_scope("Accuracy"), delegate
                 {
                     var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
                     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
                 });
 
-                with(tf.variable_scope("Prediction"), delegate
+                tf_with(tf.variable_scope("Prediction"), delegate
                 {
                     cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
                 });
@@ -144,6 +145,8 @@ namespace TensorFlowNET.Examples.ImageProcess
             float loss_val = 100.0f;
             float accuracy_val = 0f;
 
+            var sw = new Stopwatch();
+            sw.Start();
             foreach (var epoch in range(epochs))
             {
                 print($"Training epoch: {epoch + 1}");
@@ -165,7 +168,8 @@ namespace TensorFlowNET.Examples.ImageProcess
                         var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
                         loss_val = result[0];
                         accuracy_val = result[1];
-                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
+                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms");
+                        sw.Restart();
                     }
                 }
 
@@ -200,7 +204,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <returns>The output array</returns>
         private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
         {
-            return with(tf.variable_scope(name), delegate {
+            return tf_with(tf.variable_scope(name), delegate {
 
                 var num_in_channel = x.shape[x.NDims - 1];
                 var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
@@ -240,7 +244,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <returns>flattened array</returns>
         private Tensor flatten_layer(Tensor layer)
         {
-            return with(tf.variable_scope("Flatten_layer"), delegate
+            return tf_with(tf.variable_scope("Flatten_layer"), delegate
             {
                 var layer_shape = layer.TensorShape;
                 var num_features = layer_shape[new Slice(1, 4)].Size;
@@ -289,7 +293,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <returns>The output array</returns>
         private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
         {
-            return with(tf.variable_scope(name), delegate
+            return tf_with(tf.variable_scope(name), delegate
             {
                 var in_dim = x.shape[1];
 
@@ -306,14 +310,14 @@ namespace TensorFlowNET.Examples.ImageProcess
             
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
-            (x_train, y_train) = Reformat(mnist.train.data, mnist.train.labels);
-            (x_valid, y_valid) = Reformat(mnist.validation.data, mnist.validation.labels);
-            (x_test, y_test) = Reformat(mnist.test.data, mnist.test.labels);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
+            (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels);
+            (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels);
+            (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels);
 
             print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.train.data)}");
-            print($"- Validation-set:\t{len(mnist.validation.data)}");
+            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
+            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
         }
 
         /// <summary>

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -17,10 +17,10 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
-namespace TensorFlowNET.Examples.ImageProcess
+namespace TensorFlowNET.Examples
 {
     /// <summary>
     /// Neural Network classifier for Hand Written Digits
@@ -44,7 +44,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int batch_size = 100;
         float learning_rate = 0.001f;
         int h1 = 200; // number of nodes in the 1st hidden layer
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         Tensor x, y;
         Tensor loss, accuracy;
@@ -59,11 +59,11 @@ namespace TensorFlowNET.Examples.ImageProcess
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            };
 
             return loss_test < 0.09 && accuracy_test > 0.95;
         }
@@ -121,13 +121,13 @@ namespace TensorFlowNET.Examples.ImageProcess
             
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
         }
 
         public void Train(Session sess)
         {
             // Number of training iterations in each epoch
-            var num_tr_iter = mnist.train.labels.len / batch_size;
+            var num_tr_iter = mnist.Train.Labels.len / batch_size;
 
             var init = tf.global_variables_initializer();
             sess.run(init);
@@ -139,13 +139,13 @@ namespace TensorFlowNET.Examples.ImageProcess
             {
                 print($"Training epoch: {epoch + 1}");
                 // Randomly shuffle the training data at the beginning of each epoch 
-                var (x_train, y_train) = randomize(mnist.train.data, mnist.train.labels);
+                var (x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels);
 
                 foreach (var iteration in range(num_tr_iter))
                 {
                     var start = iteration * batch_size;
                     var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = get_next_batch(x_train, y_train, start, end);
+                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
 
                     // Run optimization op (backprop)
                     sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
@@ -161,7 +161,8 @@ namespace TensorFlowNET.Examples.ImageProcess
                 }
 
                 // Run validation after every epoch
-                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.validation.data), new FeedItem(y, mnist.validation.labels));
+                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.Validation.Data), new FeedItem(y, mnist.Validation.Labels));
+                
                 loss_val = results1[0];
                 accuracy_val = results1[1];
                 print("---------------------------------------------------------");
@@ -172,35 +173,12 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public void Test(Session sess)
         {
-            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.test.data), new FeedItem(y, mnist.test.labels));
+            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.Test.Data), new FeedItem(y, mnist.Test.Labels));
             loss_test = result[0];
             accuracy_test = result[1];
             print("---------------------------------------------------------");
             print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
             print("---------------------------------------------------------");
         }
-
-        private (NDArray, NDArray) randomize(NDArray x, NDArray y)
-        {
-            var perm = np.random.permutation(y.shape[0]);
-
-            np.random.shuffle(perm);
-            return (mnist.train.data[perm], mnist.train.labels[perm]);
-        }
-
-        /// <summary>
-        /// selects a few number of images determined by the batch_size variable (if you don't know why, read about Stochastic Gradient Method)
-        /// </summary>
-        /// <param name="x"></param>
-        /// <param name="y"></param>
-        /// <param name="start"></param>
-        /// <param name="end"></param>
-        /// <returns></returns>
-        private (NDArray, NDArray) get_next_batch(NDArray x, NDArray y, int start, int end)
-        {
-            var x_batch = x[$"{start}:{end}"];
-            var y_batch = y[$"{start}:{end}"];
-            return (x_batch, y_batch);
-        }
     }
 }

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -17,10 +17,10 @@
 using NumSharp;
 using System;
 using Tensorflow;
-using TensorFlowNET.Examples.Utility;
+using Tensorflow.Hub;
 using static Tensorflow.Python;
 
-namespace TensorFlowNET.Examples.ImageProcess
+namespace TensorFlowNET.Examples
 {
     /// <summary>
     /// Recurrent Neural Network for handwritten digits MNIST.
@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         int n_inputs = 28;
         int n_outputs = 10;
 
-        Datasets<DataSetMnist> mnist;
+        Datasets<MnistDataSet> mnist;
 
         Tensor x, y;
         Tensor loss, accuracy, cls_prediction;
@@ -64,11 +64,11 @@ namespace TensorFlowNET.Examples.ImageProcess
             PrepareData();
             BuildGraph();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 Train(sess);
                 Test(sess);
-            });
+            }
 
             return loss_test < 0.09 && accuracy_test > 0.95;
         }
@@ -143,15 +143,15 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public void PrepareData()
         {
-            mnist = MNIST.read_data_sets("mnist", one_hot: true);
-            (x_train, y_train) = (mnist.train.data, mnist.train.labels);
-            (x_valid, y_valid) = (mnist.validation.data, mnist.validation.labels);
-            (x_test, y_test) = (mnist.test.data, mnist.test.labels);
+            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true).Result;
+            (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels);
+            (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels);
+            (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels);
 
             print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.train.data)}");
-            print($"- Validation-set:\t{len(mnist.validation.data)}");
-            print($"- Test-set:\t\t{len(mnist.test.data)}");
+            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
+            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
+            print($"- Test-set:\t\t{len(mnist.Test.Data)}");
         }
 
         public Graph ImportGraph() => throw new NotImplementedException();

test/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs

@@ -4,7 +4,7 @@ using Tensorflow;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
 
-namespace TensorFlowNET.Examples.ImageProcess
+namespace TensorFlowNET.Examples
 {
     /// <summary>
     /// This example removes the background from an input image.
@@ -32,11 +32,11 @@ namespace TensorFlowNET.Examples.ImageProcess
 
             Tensor output = graph.OperationByName("SemanticPredictions");
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 // Runs inference on a single image.
                 sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]"));
-            });
+            }
 
             return false;
         }

test/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs

@@ -45,7 +45,7 @@ namespace TensorFlowNET.Examples
             var result_labels = new List<string>();
             var sw = new Stopwatch();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 foreach (var nd in file_ndarrays)
                 {
@@ -58,7 +58,7 @@ namespace TensorFlowNET.Examples
                     Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan);
                     result_labels.Add(labels[idx]);
                 }
-            });
+            }
             
             return result_labels.Contains("military uniform");
         }
@@ -69,19 +69,19 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 117,
                                 int input_std = 1)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-                var cast = tf.cast(decodeJpeg, tf.float32);
-                var dims_expander = tf.expand_dims(cast, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
+            var cast = tf.cast(decodeJpeg, tf.float32);
+            var dims_expander = tf.expand_dims(cast, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs

@@ -45,9 +45,12 @@ namespace TensorFlowNET.Examples
             var input_operation = graph.get_operation_by_name(input_name);
             var output_operation = graph.get_operation_by_name(output_name);
 
-            var results = with(tf.Session(graph),
-                sess => sess.run(output_operation.outputs[0],
-                    new FeedItem(input_operation.outputs[0], nd)));
+            NDArray results;
+            using (var sess = tf.Session(graph))
+            {
+                results = sess.run(output_operation.outputs[0],
+                    new FeedItem(input_operation.outputs[0], nd));
+            }
 
             results = np.squeeze(results);
 
@@ -69,19 +72,19 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 0,
                                 int input_std = 255)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-                var caster = tf.cast(image_reader, tf.float32);
-                var dims_expander = tf.expand_dims(caster, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
+            var caster = tf.cast(image_reader, tf.float32);
+            var dims_expander = tf.expand_dims(caster, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void PrepareData()

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -51,7 +51,8 @@ namespace TensorFlowNET.Examples
 
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
 
-            with(tf.Session(graph), sess => Predict(sess));
+            using (var sess = tf.Session(graph))
+                Predict(sess);
 
             return true;
         }
@@ -101,14 +102,15 @@ namespace TensorFlowNET.Examples
 
         private NDArray ReadTensorFromImageFile(string file_name)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-                var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8);
-                var dims_expander = tf.expand_dims(casted, 0);
-                return with(tf.Session(graph), sess => sess.run(dims_expander));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
+            var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8);
+            var dims_expander = tf.expand_dims(casted, 0);
+
+            using (var sess = tf.Session(graph))
+                return sess.run(dims_expander);
         }
 
         private void buildOutputImage(NDArray[] resultArr)

test/TensorFlowNET.Examples/ImageProcessing/RetrainImageClassifier.cs

@@ -25,7 +25,7 @@ using Tensorflow;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
 
-namespace TensorFlowNET.Examples.ImageProcess
+namespace TensorFlowNET.Examples
 {
     /// <summary>
     /// In this tutorial, we will reuse the feature extraction capabilities from powerful image classifiers trained on ImageNet 
@@ -83,19 +83,19 @@ namespace TensorFlowNET.Examples.ImageProcess
             #region For debug purpose
             
             // predict images
-            Predict(null);
+            // Predict(null);
 
             // load saved pb and test new images.
-            Test(null); 
+            // Test(null); 
             
             #endregion
 
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 Train(sess);
-            });
+            }
 
             return test_accuracy > 0.75f;
         }
@@ -141,20 +141,18 @@ namespace TensorFlowNET.Examples.ImageProcess
             Tensor evaluation_step = null;
             Tensor prediction = null;
 
-            with(eval_graph.as_default(), graph =>
-            {
-                // Add the new layer for exporting.
-                var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
-                    add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
-                        wants_quantization, is_training: false);
+            var graph = eval_graph.as_default();
+            // Add the new layer for exporting.
+            var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
+                add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
+                    wants_quantization, is_training: false);
 
-                // Now we need to restore the values from the training graph to the eval
-                // graph.
-                tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME);
+            // Now we need to restore the values from the training graph to the eval
+            // graph.
+            tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME);
 
-                (evaluation_step, prediction) = add_evaluation_step(final_tensor,
-                                                      ground_truth_input);
-            });
+            (evaluation_step, prediction) = add_evaluation_step(final_tensor,
+                                                    ground_truth_input);
 
             return (eval_sess, resized_input_tensor, bottleneck_input, ground_truth_input,
                 evaluation_step, prediction);
@@ -180,7 +178,7 @@ namespace TensorFlowNET.Examples.ImageProcess
             Tensor bottleneck_tensor, bool quantize_layer, bool is_training)
         {
             var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.TensorShape.Dimensions[0], bottleneck_tensor.TensorShape.Dimensions[1]);
-            with(tf.name_scope("input"), scope =>
+            tf_with(tf.name_scope("input"), scope =>
             {
                 bottleneck_input = tf.placeholder_with_default(
                     bottleneck_tensor,
@@ -193,10 +191,10 @@ namespace TensorFlowNET.Examples.ImageProcess
             // Organizing the following ops so they are easier to see in TensorBoard.
             string layer_name = "final_retrain_ops";
             Tensor logits = null;
-            with(tf.name_scope(layer_name), scope =>
+            tf_with(tf.name_scope(layer_name), scope =>
             {
                 RefVariable layer_weights = null;
-                with(tf.name_scope("weights"), delegate
+                tf_with(tf.name_scope("weights"), delegate
                 {
                     var initial_value = tf.truncated_normal(new int[] { bottleneck_tensor_size, class_count }, stddev: 0.001f);
                     layer_weights = tf.Variable(initial_value, name: "final_weights");
@@ -204,13 +202,13 @@ namespace TensorFlowNET.Examples.ImageProcess
                 });
 
                 RefVariable layer_biases = null;
-                with(tf.name_scope("biases"), delegate
+                tf_with(tf.name_scope("biases"), delegate
                 {
                     layer_biases = tf.Variable(tf.zeros(class_count), name: "final_biases");
                     variable_summaries(layer_biases);
                 });
 
-                with(tf.name_scope("Wx_plus_b"), delegate
+                tf_with(tf.name_scope("Wx_plus_b"), delegate
                 {
                     logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases;
                     tf.summary.histogram("pre_activations", logits);
@@ -239,7 +237,7 @@ namespace TensorFlowNET.Examples.ImageProcess
                 return (null, null, bottleneck_input, ground_truth_input, final_tensor);
 
             Tensor cross_entropy_mean = null;
-            with(tf.name_scope("cross_entropy"), delegate
+            tf_with(tf.name_scope("cross_entropy"), delegate
             {
                 cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
                     labels: ground_truth_input, logits: logits);
@@ -247,7 +245,7 @@ namespace TensorFlowNET.Examples.ImageProcess
 
             tf.summary.scalar("cross_entropy", cross_entropy_mean);
 
-            with(tf.name_scope("train"), delegate
+            tf_with(tf.name_scope("train"), delegate
             {
                 var optimizer = tf.train.GradientDescentOptimizer(learning_rate);
                 train_step = optimizer.minimize(cross_entropy_mean);
@@ -259,12 +257,12 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         private void variable_summaries(RefVariable var)
         {
-            with(tf.name_scope("summaries"), delegate
+            tf_with(tf.name_scope("summaries"), delegate
             {
                 var mean = tf.reduce_mean(var);
                 tf.summary.scalar("mean", mean);
                 Tensor stddev = null;
-                with(tf.name_scope("stddev"), delegate
+                tf_with(tf.name_scope("stddev"), delegate
                 {
                     stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)));
                 });
@@ -279,7 +277,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         {
             var (height, width) = (299, 299);
 
-            return with(tf.Graph().as_default(), graph =>
+            return tf_with(tf.Graph().as_default(), graph =>
             {
                 tf.train.import_meta_graph("graph/InceptionV3.meta");
                 Tensor resized_input_tensor = graph.OperationByName("Placeholder"); //tf.placeholder(tf.float32, new TensorShape(-1, height, width, 3));
@@ -350,15 +348,15 @@ namespace TensorFlowNET.Examples.ImageProcess
         {
             Tensor evaluation_step = null, correct_prediction = null, prediction = null;
 
-            with(tf.name_scope("accuracy"), scope =>
+            tf_with(tf.name_scope("accuracy"), scope =>
             {
-                with(tf.name_scope("correct_prediction"), delegate
+                tf_with(tf.name_scope("correct_prediction"), delegate
                 {
                     prediction = tf.argmax(result_tensor, 1);
                     correct_prediction = tf.equal(prediction, ground_truth_tensor);
                 });
 
-                with(tf.name_scope("accuracy"), delegate
+                tf_with(tf.name_scope("accuracy"), delegate
                 {
                     evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
                 });
@@ -596,7 +594,7 @@ namespace TensorFlowNET.Examples.ImageProcess
                 create_module_graph();
 
             // Add the new layer that we'll be training.
-            with(graph.as_default(), delegate
+            tf_with(graph.as_default(), delegate
             {
                 (train_step, cross_entropy, bottleneck_input,
                  ground_truth_input, final_tensor) = add_final_retrain_ops(
@@ -745,13 +743,13 @@ namespace TensorFlowNET.Examples.ImageProcess
             Tensor input = graph.OperationByName("Placeholder");
             Tensor output = graph.OperationByName("final_result");
 
-            with(tf.Session(graph), sess =>
+            using (var sess = tf.Session(graph))
             {
                 var result = sess.run(output, new FeedItem(input, fileBytes));
                 var prob = np.squeeze(result);
                 var idx = np.argmax(prob);
                 print($"Prediction result: [{labels[idx]} {prob[idx][0]}] for {img_path}.");
-            });
+            }
         }
 
         private NDArray ReadTensorFromImageFile(string file_name,
@@ -760,19 +758,19 @@ namespace TensorFlowNET.Examples.ImageProcess
                         int input_mean = 0,
                         int input_std = 255)
         {
-            return with(tf.Graph().as_default(), graph =>
-            {
-                var file_reader = tf.read_file(file_name, "file_reader");
-                var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-                var caster = tf.cast(image_reader, tf.float32);
-                var dims_expander = tf.expand_dims(caster, 0);
-                var resize = tf.constant(new int[] { input_height, input_width });
-                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-                var sub = tf.subtract(bilinear, new float[] { input_mean });
-                var normalized = tf.divide(sub, new float[] { input_std });
-
-                return with(tf.Session(graph), sess => sess.run(normalized));
-            });
+            var graph = tf.Graph().as_default();
+
+            var file_reader = tf.read_file(file_name, "file_reader");
+            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
+            var caster = tf.cast(image_reader, tf.float32);
+            var dims_expander = tf.expand_dims(caster, 0);
+            var resize = tf.constant(new int[] { input_height, input_width });
+            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
+            var sub = tf.subtract(bilinear, new float[] { input_mean });
+            var normalized = tf.divide(sub, new float[] { input_std });
+
+            using (var sess = tf.Session(graph))
+                return sess.run(normalized);
         }
 
         public void Test(Session sess_)
@@ -783,7 +781,7 @@ namespace TensorFlowNET.Examples.ImageProcess
             var graph = Graph.ImportFromPB(output_graph);
             var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
 
-            with(tf.Session(graph), sess =>
+            tf_with(tf.Session(graph), sess =>
             {
                 (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
                                jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,

test/TensorFlowNET.Examples/TensorFlowNET.Examples.GPU.csproj

@@ -0,0 +1,24 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
+    <GeneratePackageOnBuild>false</GeneratePackageOnBuild>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Colorful.Console" Version="1.2.9" />
+    <PackageReference Include="Newtonsoft.Json" Version="12.0.2" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist-Windows-GPU" Version="1.14.0" />
+    <PackageReference Include="SharpZipLib" Version="1.1.0" />
+    <PackageReference Include="System.Drawing.Common" Version="4.5.1" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\src\KerasNET.Core\Keras.Core.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowDatasets\TensorFlowDatasets.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowText\TensorFlowText.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowHub\TensorFlowHub.csproj" />
+  </ItemGroup>
+</Project>

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -16,7 +16,9 @@
 
   <ItemGroup>
     <ProjectReference Include="..\..\src\KerasNET.Core\Keras.Core.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowDatasets\TensorFlowDatasets.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowText\TensorFlowText.csproj" />
+    <ProjectReference Include="..\..\src\TensorFlowHub\TensorFlowHub.csproj" />
   </ItemGroup>
 </Project>

test/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs

@@ -64,7 +64,9 @@ namespace TensorFlowNET.Examples
         {
             PrepareData();
             var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-            with(tf.Session(graph), sess => Train(sess));
+
+            using (var sess = tf.Session(graph))
+                Train(sess);
 
             return max_accuracy > 0.9;
         }

test/TensorFlowNET.Examples/TextProcessing/NER/LstmCrfNer.cs

@@ -63,7 +63,7 @@ namespace TensorFlowNET.Examples.Text.NER
 
             var init = tf.global_variables_initializer();
 
-            with(tf.Session(), sess =>
+            using (var sess = tf.Session())
             {
                 sess.run(init);
 
@@ -73,7 +73,7 @@ namespace TensorFlowNET.Examples.Text.NER
                     loss_value = run_epoch(sess, train, dev, epoch);
                     print($"train loss: {loss_value}");
                 }
-            });
+            }
 
             return loss_value < 0.1;
         }