Merge pull request #2 from SciSharp/master

Syncing original repo

README.md

@@ -149,6 +149,7 @@ Example runner will download all the required files like training data and model
 * [Object Detection](test/TensorFlowNET.Examples/ImageProcess/ObjectDetection.cs)
 * [Text Classification](test/TensorFlowNET.Examples/TextProcess/BinaryTextClassification.cs)
 * [CNN Text Classification](test/TensorFlowNET.Examples/TextProcess/cnn_models/VdCnn.cs)
+* [MNIST CNN](test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionCNN.cs)
 * [Named Entity Recognition](test/TensorFlowNET.Examples/TextProcess/NER)
 * [Transfer Learning for Image Classification in InceptionV3](test/TensorFlowNET.Examples/ImageProcess/RetrainImageClassifier.cs)
 

TensorFlow.NET.sln

@@ -1,7 +1,7 @@
 
 Microsoft Visual Studio Solution File, Format Version 12.00
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@@ -17,6 +17,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Keras.UnitTest", "test\Kera
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+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TensorFlowNet.Benchmark", "src\TensorFlowNet.Benchmarks\TensorFlowNet.Benchmark.csproj", "{68861442-971A-4196-876E-C9330F0B3C54}"
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src/SciSharp.TensorFlow.Redist/CommonPackage.props

@@ -0,0 +1,24 @@
+<Project xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+
+  <!--
+  NuGet packages.config doesn't support native assemblies automatically,
+  so copy the native assemblies to the output directory.
+  -->
+  <ItemGroup Condition="Exists('packages.config') OR
+                        Exists('$(MSBuildProjectName).packages.config') OR
+                        Exists('packages.$(MSBuildProjectName).config')">
+    <Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x64\native\*.dll"
+             Condition="'$(PlatformTarget)' == 'x64'">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+      <Visible>false</Visible>
+      <Link>%(Filename)%(Extension)</Link>
+    </Content>
+    <Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x86\native\*.dll"
+             Condition="'$(PlatformTarget)' == 'x86'">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+      <Visible>false</Visible>
+      <Link>%(Filename)%(Extension)</Link>
+    </Content>
+  </ItemGroup>
+
+</Project>

src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj

@@ -0,0 +1,188 @@
+<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>$(TensorFlowVersion)</PackageVersion>
+
+    <NoBuild>true</NoBuild>
+    <IncludeBuildOutput>false</IncludeBuildOutput>
+
+    <NuspecFile>Redist.nuspec</NuspecFile>
+    <NuspecProperties>packageId=$(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-cpu-%(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>
+
+    <Message Importance="High"
+             Text="%(FilesWithHashes.Identity): %(FilesWithHashes.FileHash)" />
+
+    <ItemGroup>
+      <TensorFlowArchive>
+        <DownloadSha>@(FilesWithHashes->'%(FileHash)')</DownloadSha>
+        <LocalSha>$([System.IO.File]::ReadAllText('%(LocalShaFile)'))</LocalSha>
+      </TensorFlowArchive>
+    </ItemGroup>
+
+    <!-- If specified we'll update the checked in SHAs with the downloaded ones. -->
+    <!--<WriteLinesToFile  Condition="'$(UpdateSHA)' == 'true'"
+          File="@(TensorFlowArchive->'%(LocalShaFile)')"
+          Lines="@(TensorFlowArchive->'%(LocalShaFile)')" />-->
+
+    <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: %(_downloadedTensorFlowArchive.LocalSha)%0A  actual: %(_downloadedTensorFlowArchive.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-cpu-darwin-x86_64-1.14.0.tar.gz.sha

@@ -0,0 +1 @@
+7002EF701BD23C5EF5FF94192E935F0DDF960A21BE2531CEE158586830C00E0BA889900F7F6E8AB568BEE0ACF1F5A6A246BB43D11C4109E9DC782B46377D8142

src/SciSharp.TensorFlow.Redist/libtensorflow-cpu-linux-x86_64-1.14.0.tar.gz.sha

@@ -0,0 +1 @@
+E3F6D0309117E9E45780ECF8BC4D0268B3FC9F12E3E38FFE58496789330A4ACD2DC8FF721F3B8900357F6155F8A54000E45B99495F823486B558E8B42532392D

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

@@ -0,0 +1 @@
+59A2B80B441439B851202358CE4A65BA0DDDB319A8A29E87B135DCD9954BC5B0628F2C0C8E72D6942EA3CDCE172805C2BD5421815B3D0210B62BC0936DC59A08

src/SciSharp.TensorFlow.Redist/redist.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 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/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -1,6 +1,6 @@
 /*****************************************************************************
    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
@@ -18,6 +18,8 @@ using NumSharp;
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using System.Numerics;
+using System.Runtime.CompilerServices;
 using System.Runtime.InteropServices;
 using System.Text;
 using static Tensorflow.c_api;
@@ -36,39 +38,246 @@ namespace Tensorflow
             _handle = handle;
         }
 
-        public Tensor(NDArray nd, TF_DataType? tensorDType = null)
+#if _REGEN
+        %types=["sbyte", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"]
+        %foreach types%
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(#1[] data)
         {
-            _handle = Allocate(nd, tensorDType: tensorDType);
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(#1)), new long[]{data.Length}, data, Marshal.SizeOf<#1>());
         }
 
-        public unsafe Tensor(byte[] buffer)
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(#1[] data, long[] shape)
         {
-            var size = c_api.TF_StringEncodedSize((UIntPtr)buffer.Length);
-            _handle = TF_AllocateTensor(TF_DataType.TF_STRING, IntPtr.Zero, 0, (UIntPtr)((ulong)size + 8));
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(#1)), shape, data, Marshal.SizeOf<#1>());
+        }
 
-            IntPtr tensor = c_api.TF_TensorData(_handle);
-            Marshal.WriteInt64(tensor, 0);
-            fixed (byte* src = &buffer[0])
-                c_api.TF_StringEncode(src, (UIntPtr)buffer.Length, (sbyte*)(tensor + sizeof(Int64)), size, status);
+        %
+#else
+        
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(sbyte[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(sbyte)), new long[]{data.Length}, data, Marshal.SizeOf<sbyte>());
+        }
 
-            status.Check(true);
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(sbyte[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(sbyte)), shape, data, Marshal.SizeOf<sbyte>());
         }
 
-        private IntPtr Allocate(NDArray nd, TF_DataType? tensorDType = null)
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(byte[] data)
         {
-            if (tensorDType == TF_DataType.TF_STRING &&
-                nd.dtype.Name == "Byte")
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(byte)), new long[]{data.Length}, data, Marshal.SizeOf<byte>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(byte[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(byte)), shape, data, Marshal.SizeOf<byte>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(short[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(short)), new long[]{data.Length}, data, Marshal.SizeOf<short>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(short[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(short)), shape, data, Marshal.SizeOf<short>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(ushort[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(ushort)), new long[]{data.Length}, data, Marshal.SizeOf<ushort>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(ushort[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(ushort)), shape, data, Marshal.SizeOf<ushort>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(int[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(int)), new long[]{data.Length}, data, Marshal.SizeOf<int>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(int[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(int)), shape, data, Marshal.SizeOf<int>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(uint[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(uint)), new long[]{data.Length}, data, Marshal.SizeOf<uint>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(uint[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(uint)), shape, data, Marshal.SizeOf<uint>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(long[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(long)), new long[]{data.Length}, data, Marshal.SizeOf<long>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(long[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(long)), shape, data, Marshal.SizeOf<long>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(ulong[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(ulong)), new long[]{data.Length}, data, Marshal.SizeOf<ulong>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(ulong[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(ulong)), shape, data, Marshal.SizeOf<ulong>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(float[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(float)), new long[]{data.Length}, data, Marshal.SizeOf<float>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(float[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(float)), shape, data, Marshal.SizeOf<float>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(double[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(double)), new long[]{data.Length}, data, Marshal.SizeOf<double>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(double[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(double)), shape, data, Marshal.SizeOf<double>());
+        }
+
+        
+        /// <summary>
+        /// Create a 1d Tensor from the given linear array and shape
+        /// </summary>
+        public Tensor(Complex[] data)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(Complex)), new long[]{data.Length}, data, Marshal.SizeOf<Complex>());
+        }
+
+        /// <summary>
+        /// Create a N-dimensional Tensor from the given array
+        /// </summary>
+        public Tensor(Complex[] data, long[] shape)
+        {
+            _handle = CreateTensorWithoutCopying(dtypes.as_dtype(typeof(Complex)), shape, data, Marshal.SizeOf<Complex>());
+        }
+#endif
+
+        public Tensor(NDArray nd, TF_DataType? tensorDType = null)
+        {
+            _handle = Allocate(nd, tensorDType: tensorDType);
+        }
+
+        private unsafe IntPtr Allocate(NDArray nd, TF_DataType? tensorDType = null)
+        {
+            if (tensorDType == TF_DataType.TF_STRING && nd.dtype.Name == "Byte")
             {
-                return new Tensor(nd.Data<byte>());
+                var buffer=nd.Data<byte>();
+                var size = c_api.TF_StringEncodedSize((UIntPtr)buffer.Length);
+                var handle = TF_AllocateTensor(TF_DataType.TF_STRING, IntPtr.Zero, 0, (UIntPtr)((ulong)size + 8));
+
+                IntPtr tensor = c_api.TF_TensorData(handle);
+                Marshal.WriteInt64(tensor, 0);
+                fixed (byte* src = &buffer[0])
+                    c_api.TF_StringEncode(src, (UIntPtr)buffer.Length, (sbyte*)(tensor + sizeof(Int64)), size, status);
+
+                status.Check(true);
+                return handle;
             }
 
             IntPtr dotHandle = IntPtr.Zero;
-            ulong size = 0;
+            int buffersize = 0;
 
             if (nd.dtype.Name != "String")
             {
-                dotHandle = Marshal.AllocHGlobal(nd.dtypesize * nd.size);
-                size = (ulong)(nd.size * nd.dtypesize);
+                buffersize = (nd.size * nd.dtypesize);
+                dotHandle = Marshal.AllocHGlobal(buffersize);
             }
 
             var dataType = ToTFDataType(nd.dtype);
@@ -116,7 +325,7 @@ namespace Tensorflow
                 dims,
                 dims.Length,
                 dotHandle,
-                (UIntPtr)size,
+                (UIntPtr)buffersize,
                 deallocator,
                 ref deallocator_called);
 
@@ -130,5 +339,59 @@ namespace Tensorflow
             _dtype = dtype;
             _id = ops.uid();
         }
+
+        /// <summary>
+        /// Creates a new tensor from the given array without copying memory. The array is pinned down and the pointer passed on.
+        /// </summary>
+        /// <param name="shape">Represents the tensor shape.</param>
+        /// <param name="data">The linear array of data, the data must fit in the tensor with the specified dimensions.</param>
+        /// <param name="element_size">The number of bytes in memory of a single array element</param>
+        /// <remarks>
+        /// Use the FromBuffer method to create a tensor that has the specified dimensions
+        /// and is initialized with data from the data array.   The data is copied starting
+        /// at the start offset, for count bytes and is laid out into the tensor following the
+        /// specified dimensions.
+        /// </remarks>
+        [MethodImpl(MethodImplOptions.AggressiveInlining)]
+        protected IntPtr CreateTensorWithoutCopying(TF_DataType dt, long[] shape, Array data, int element_size)
+        {
+            return CreateTensorWithoutCopying(dt, shape, data, 0, data.Length, element_size);
+        }
+
+        /// <summary>
+        /// Creates a new tensor from a subsection of the given array without copying memory. The array is pinned down and the pointer passed on.
+        /// </summary>
+        /// <param name="shape">Represents the tensor shape.</param>
+        /// <param name="data">The linear array of data, the data must fit in the tensor with the specified dimensions.</param>
+        /// <param name="start">The offset into the provided data array where the data resides.</param>
+        /// <param name="count">The number of elements to copy from data.</param>
+        /// <param name="element_size">The number of bytes in memory of a single array element</param>
+        /// <remarks>
+        /// Use the FromBuffer method to create a tensor that has the specified dimensions
+        /// and is initialized with data from the data array.   The data is copied starting
+        /// at the start offset, for count bytes and is laid out into the tensor following the
+        /// specified dimensions.
+        /// </remarks>
+        [MethodImpl(MethodImplOptions.AggressiveInlining)]
+        protected IntPtr CreateTensorWithoutCopying(TF_DataType dt, long[] shape, Array data, int start, int count, int element_size)
+        {
+            if (start < 0 || start > data.Length - count)
+                throw new ArgumentException($"Array length {data.Length} does not match the given shape {new Shape(shape.Cast<int>().ToArray())}");
+            
+            // get a handle to the pinned array which we will pass on to the tensor computation engine to use
+            var dataHandle = GCHandle.Alloc(data, GCHandleType.Pinned);
+
+            // Free the original buffer and set flag
+            Deallocator deallocator = (IntPtr values, IntPtr len, ref bool closure) =>
+            {
+                dataHandle.Free();
+                closure = true;
+            };
+
+            if (shape == null)
+                return TF_NewTensor(dt, null, 0, dataHandle.AddrOfPinnedObject() + start * element_size, (UIntPtr)(count * element_size), deallocator, ref deallocator_called);
+            else
+                return TF_NewTensor(dt, shape, shape.Length, dataHandle.AddrOfPinnedObject() + start * element_size, (UIntPtr)(count * element_size), deallocator, ref deallocator_called);
+        }
     }
 }

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -31,7 +31,7 @@ namespace Tensorflow
     /// </summary>
     public partial class Tensor : IDisposable, ITensorOrOperation
     {
-        private readonly IntPtr _handle;
+        private IntPtr _handle;
 
         private int _id;
         private Operation _op;
@@ -351,6 +351,7 @@ namespace Tensorflow
         public void Dispose()
         {
             c_api.TF_DeleteTensor(_handle);
+            _handle = IntPtr.Zero;
             status.Dispose();
         }
 

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -52,32 +52,51 @@ namespace Tensorflow
             }
         }
 
+        // "sbyte", "byte", "short", "ushort", "int", "uint", "long", "ulong", "float", "double", "Complex"
         public static TF_DataType as_dtype(Type type)
         {
             TF_DataType dtype = TF_DataType.DtInvalid;
-
+            
             switch (type.Name)
             {
-                case "Boolean":
-                    dtype = TF_DataType.TF_BOOL;
+                case "SByte":
+                    dtype = TF_DataType.TF_INT8;
+                    break;
+                case "Byte":
+                    dtype = TF_DataType.TF_UINT8;
+                    break;
+                case "Int16":
+                    dtype = TF_DataType.TF_INT16;
+                    break;
+                case "UInt16":
+                    dtype = TF_DataType.TF_UINT16;
                     break;
                 case "Int32":
                     dtype = TF_DataType.TF_INT32;
                     break;
+                case "UInt32":
+                    dtype = TF_DataType.TF_UINT32;
+                    break;
                 case "Int64":
                     dtype = TF_DataType.TF_INT64;
                     break;
+                case "UInt64":
+                    dtype = TF_DataType.TF_UINT64;
+                    break;
                 case "Single":
                     dtype = TF_DataType.TF_FLOAT;
                     break;
                 case "Double":
                     dtype = TF_DataType.TF_DOUBLE;
                     break;
+                case "Complex":
+                    dtype = TF_DataType.TF_COMPLEX128;
+                    break;
                 case "String":
                     dtype = TF_DataType.TF_STRING;
                     break;
-                case "Byte":
-                    dtype = TF_DataType.TF_STRING;
+                case "Boolean":
+                    dtype = TF_DataType.TF_BOOL;
                     break;
                 default:
                     throw new Exception("as_dtype Not Implemented");

src/TensorFlowNET.Core/Variables/_VariableScopeStore.cs

@@ -41,7 +41,11 @@ namespace Tensorflow
 
         public void close_variable_subscopes(string scope_name)
         {
+            var variable_scopes_count_tmp = new Dictionary<string, int>();
             foreach (var k in variable_scopes_count.Keys)
+                variable_scopes_count_tmp.Add(k, variable_scopes_count[k]);
+
+            foreach (var k in variable_scopes_count_tmp.Keys)
                 if (scope_name == null || k.StartsWith(scope_name + "/"))
                     variable_scopes_count[k] = 0;
         }

src/TensorFlowNet.Benchmarks/Program.cs

@@ -0,0 +1,40 @@
+using System;
+using System.Reflection;
+using BenchmarkDotNet.Configs;
+using BenchmarkDotNet.Running;
+
+namespace TensorFlowNet.Benchmark
+{
+    class Program
+    {
+        /// <summary>
+        /// dotnet NumSharp.Benchmark.dll (Benchmark Class Name)
+        /// dotnet NumSharp.Benchmark.dll nparange
+        /// </summary>
+        /// <param name="args"></param>
+        static void Main(string[] args)
+        {
+#if DEBUG
+            IConfig config = new DebugInProcessConfig();
+#else
+            IConfig config = null;
+#endif
+
+            if (args?.Length > 0)
+            {
+                for (int i = 0; i < args.Length; i++)
+                {
+                    string name = $"TensorFlowNet.Benchmark.{args[i]}";
+                    var type = Type.GetType(name);
+                    BenchmarkRunner.Run(type, config);
+                }
+            }
+            else
+            {
+                BenchmarkSwitcher.FromAssembly(Assembly.GetExecutingAssembly()).Run(args, config);
+            }
+
+            Console.ReadLine();
+        }
+    }
+}

src/TensorFlowNet.Benchmarks/TensorCreation.cs

@@ -0,0 +1,55 @@
+using System;
+using System.Linq;
+using BenchmarkDotNet.Attributes;
+using BenchmarkDotNet.Engines;
+using NumSharp;
+using Tensorflow;
+
+namespace TensorFlowNet.Benchmark
+{
+    [SimpleJob(launchCount: 1, warmupCount: 10, targetCount: 30)]
+    [MinColumn, MaxColumn, MeanColumn, MedianColumn]
+    public class TensorCreation
+    {
+        private double[] data;
+
+        [GlobalSetup]
+        public void Setup()
+        {
+            data = new double[1000];
+        }
+
+        [Benchmark]
+        public void TensorFromArray()
+        {
+            var g=new Graph();
+            for (int i = 0; i < 100; i++)
+            {
+                var tensor = new Tensor(data);
+            }
+        }
+
+
+        [Benchmark]
+        public void TensorFromNDArray()
+        {
+            var g = new Graph();
+            for (int i = 0; i < 100; i++)
+            {
+                var tensor = new Tensor(new NDArray(data));
+            }
+        }
+
+        //[Benchmark]
+        //public void Constant()
+        //{
+        //    for (int i = 0; i < 100; i++)
+        //    {
+        //        //var tensor = new Tensor(new NDArray(data));
+        //        var c = tf.constant(42.0);
+        //    }
+        //}
+
+    }
+}
+

src/TensorFlowNet.Benchmarks/TensorFlowNet.Benchmark.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="BenchmarkDotNet" Version="0.11.5" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="tensorflow.dll">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>

test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionCNN.cs → test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -73,6 +73,10 @@ namespace TensorFlowNET.Examples.ImageProcess
         float accuracy_test = 0f;
         float loss_test = 1f;
 
+        NDArray x_train, y_train;
+        NDArray x_valid, y_valid;
+        NDArray x_test, y_test;
+
         public bool Run()
         {
             PrepareData();
@@ -133,6 +137,62 @@ namespace TensorFlowNET.Examples.ImageProcess
             return graph;
         }
 
+        public void Train(Session sess)
+        {
+            // Number of training iterations in each epoch
+            var num_tr_iter = y_train.len / batch_size;
+
+            var init = tf.global_variables_initializer();
+            sess.run(init);
+
+            float loss_val = 100.0f;
+            float accuracy_val = 0f;
+
+            foreach (var epoch in range(epochs))
+            {
+                print($"Training epoch: {epoch + 1}");
+                // Randomly shuffle the training data at the beginning of each epoch 
+                (x_train, y_train) = mnist.Randomize(x_train, y_train);
+
+                foreach (var iteration in range(num_tr_iter))
+                {
+                    var start = iteration * batch_size;
+                    var end = (iteration + 1) * batch_size;
+                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
+
+                    // Run optimization op (backprop)
+                    sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+
+                    if (iteration % display_freq == 0)
+                    {
+                        // Calculate and display the batch loss and accuracy
+                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+                        loss_val = result[0];
+                        accuracy_val = result[1];
+                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
+                    }
+                }
+
+                // Run validation after every epoch
+                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_valid), new FeedItem(y, y_valid));
+                loss_val = results1[0];
+                accuracy_val = results1[1];
+                print("---------------------------------------------------------");
+                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
+                print("---------------------------------------------------------");
+            }
+        }
+
+        public void Test(Session sess)
+        {
+            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_test), new FeedItem(y, y_test));
+            loss_test = result[0];
+            accuracy_test = result[1];
+            print("---------------------------------------------------------");
+            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
+            print("---------------------------------------------------------");
+        }
+
         /// <summary>
         /// Create a 2D convolution layer
         /// </summary>
@@ -217,6 +277,14 @@ namespace TensorFlowNET.Examples.ImageProcess
                            initializer: initial);
         }
 
+        /// <summary>
+        /// Create a fully-connected layer
+        /// </summary>
+        /// <param name="x">input from previous layer</param>
+        /// <param name="num_units">number of hidden units in the fully-connected layer</param>
+        /// <param name="name">layer name</param>
+        /// <param name="use_relu">boolean to add ReLU non-linearity (or not)</param>
+        /// <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
@@ -233,73 +301,36 @@ namespace TensorFlowNET.Examples.ImageProcess
                 return layer;
             });
         } 
-
-        public Graph ImportGraph() => throw new NotImplementedException();
-
-        public void Predict(Session sess) => throw new NotImplementedException();
             
         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);
+
             print("Size of:");
             print($"- Training-set:\t\t{len(mnist.train.data)}");
             print($"- Validation-set:\t{len(mnist.validation.data)}");
         }
 
-        public void Train(Session sess)
+        /// <summary>
+        /// Reformats the data to the format acceptable for convolutional layers
+        /// </summary>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <returns></returns>
+        private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
         {
-            // Number of training iterations in each epoch
-            var num_tr_iter = mnist.train.labels.len / batch_size;
-
-            var init = tf.global_variables_initializer();
-            sess.run(init);
-
-            float loss_val = 100.0f;
-            float accuracy_val = 0f;
-
-            foreach (var epoch in range(epochs))
-            {
-                print($"Training epoch: {epoch + 1}");
-                // Randomly shuffle the training data at the beginning of each epoch 
-                var (x_train, y_train) = mnist.Randomize(mnist.train.data, mnist.train.labels);
-
-                foreach (var iteration in range(num_tr_iter))
-                {
-                    var start = iteration * batch_size;
-                    var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
-
-                    // Run optimization op (backprop)
-                    sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
-
-                    if (iteration % display_freq == 0)
-                    {
-                        // Calculate and display the batch loss and accuracy
-                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
-                        loss_val = result[0];
-                        accuracy_val = result[1];
-                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
-                    }
-                }
-
-                // Run validation after every epoch
-                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("---------------------------------------------------------");
-                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
-                print("---------------------------------------------------------");
-            }
+            var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]), 1, len(np.unique<int>(np.argmax(y, 1))));
+            var dataset = x.reshape(x.shape[0], img_size, img_size, num_ch).astype(np.float32);
+            //y[0] = np.arange(num_class) == y[0];
+            //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32);
+            return (dataset, y);
         }
 
-        public void Test(Session sess)
-        {
-            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("---------------------------------------------------------");
-        }
+        public Graph ImportGraph() => throw new NotImplementedException();
+
+        public void Predict(Session sess) => throw new NotImplementedException();
     }
 }

test/TensorFlowNET.Examples/ImageProcess/ObjectDetection.cs → test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -32,7 +32,7 @@ namespace TensorFlowNET.Examples
     {
         public bool Enabled { get; set; } = true;
         public string Name => "Object Detection";
-        public bool IsImportingGraph { get; set; } = false;
+        public bool IsImportingGraph { get; set; } = true;
 
         public float MIN_SCORE = 0.5f;
 
@@ -42,16 +42,34 @@ namespace TensorFlowNET.Examples
         string labelFile = "mscoco_label_map.pbtxt";
         string picFile = "input.jpg";
 
+        NDArray imgArr;
+
         public bool Run()
         {
             PrepareData();
 
             // read in the input image
-            var imgArr = ReadTensorFromImageFile(Path.Join(imageDir, "input.jpg"));
+            imgArr = ReadTensorFromImageFile(Path.Join(imageDir, "input.jpg"));
+
+            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
+
+            with(tf.Session(graph), sess => Predict(sess));
 
-            var graph = new Graph().as_default();          
+            return true;
+        }
+
+        public Graph ImportGraph()
+        {
+            var graph = new Graph().as_default();
             graph.Import(Path.Join(modelDir, pbFile));
 
+            return graph;
+        }
+
+        public void Predict(Session sess)
+        {
+            var graph = tf.get_default_graph();
+
             Tensor tensorNum = graph.OperationByName("num_detections");
             Tensor tensorBoxes = graph.OperationByName("detection_boxes");
             Tensor tensorScores = graph.OperationByName("detection_scores");
@@ -59,16 +77,11 @@ namespace TensorFlowNET.Examples
             Tensor imgTensor = graph.OperationByName("image_tensor");
             Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses };
 
-            with(tf.Session(graph), sess =>
-            {
-                var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
-                
-                NDArray[] resultArr = results.Data<NDArray>();
-                
-                buildOutputImage(resultArr);
-            });
+            var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
 
-            return true;
+            NDArray[] resultArr = results.Data<NDArray>();
+
+            buildOutputImage(resultArr);
         }
 
         public void PrepareData()
@@ -159,29 +172,8 @@ namespace TensorFlowNET.Examples
             }
         }
 
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
+        public Graph BuildGraph() => throw new NotImplementedException();
+        public void Train(Session sess) => throw new NotImplementedException();
+        public void Test(Session sess) => throw new NotImplementedException();
     }
 }

test/TensorFlowNET.Examples/Utility/Datasets.cs

@@ -28,7 +28,7 @@ namespace TensorFlowNET.Examples.Utility
             var perm = np.random.permutation(y.shape[0]);
 
             np.random.shuffle(perm);
-            return (train.data[perm], train.labels[perm]);
+            return (x[perm], y[perm]);
         }
 
         /// <summary>

test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs

@@ -39,6 +39,7 @@ namespace TensorFlowNET.ExamplesTests
             new InceptionArchGoogLeNet() { Enabled = true }.Run();
         }
 
+        [Ignore]
         [TestMethod]
         public void KMeansClustering()
         {
@@ -83,10 +84,12 @@ namespace TensorFlowNET.ExamplesTests
             new NearestNeighbor() { Enabled = true, TrainSize = 500, ValidationSize = 100, TestSize = 100 }.Run();
         }
 
+        [Ignore]
         [TestMethod]
         public void WordCnnTextClassification()
             => new CnnTextClassification { Enabled = true, ModelName = "word_cnn", DataLimit =100 }.Run();
 
+        [Ignore]
         [TestMethod]
         public void CharCnnTextClassification()
             => new CnnTextClassification { Enabled = true, ModelName = "char_cnn", DataLimit = 100 }.Run();