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diff --git a/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp16_kitten_224_input_GPU.csv b/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp16_kitten_224_input_GPU.csv
new file mode 100644
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diff --git a/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp32_kitten_224_input_CPU.csv b/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp32_kitten_224_input_CPU.csv
new file mode 100644
index 00000000..14964894
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diff --git a/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp32_kitten_224_input_GPU.csv b/Testing/WinMLRunnerTest/OutputTensorData/DenseNet121_fp32_kitten_224_input_GPU.csv
new file mode 100644
index 00000000..c69ef46a
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\ No newline at end of file
diff --git a/Testing/WinMLRunnerTest/WinMLRunnerTest.cpp b/Testing/WinMLRunnerTest/WinMLRunnerTest.cpp
index 1e250d96..6357acd1 100644
--- a/Testing/WinMLRunnerTest/WinMLRunnerTest.cpp
+++ b/Testing/WinMLRunnerTest/WinMLRunnerTest.cpp
@@ -121,6 +121,7 @@ namespace WinMLRunnerTest
         {
             Assert::Fail(L"Failed to open tensor files\n");
         }
+
         bool isFirstRow = true;
         while (!tensorFileStream.eof())
         {
@@ -554,6 +555,30 @@ namespace WinMLRunnerTest
             Assert::AreEqual(S_OK, RunProc((wchar_t *)command.c_str()));
         }
 
+        TEST_METHOD(ProvidedImageInputFolder)
+        {
+            // Make test_folder_input folder before starting the tests
+            std::string mkFolderCommand = "mkdir " + std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
+            system(mkFolderCommand.c_str());
+
+            std::vector<std::string> images = { "fish.png", "kitten_224.png" };
+
+            // Copy images from list to test_folder_input
+            for (auto image : images)
+            {
+                std::string copyCommand = "Copy ";
+                copyCommand += image;
+                copyCommand += ' ' + std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
+                system(copyCommand.c_str());
+            }
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model", L"SqueezeNet.onnx", L"-InputImageFolder", INPUT_FOLDER_PATH });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+
+            std::string removeCommand = "rd /s /q ";
+            removeCommand += std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
+            system(removeCommand.c_str());
+        }
+
         TEST_METHOD(AutoScaleImage)
         {
             const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
@@ -563,15 +588,18 @@ namespace WinMLRunnerTest
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
         }
 
-        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensor)
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuPerIterationPerformance)
             const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
             const std::wstring inputPath = CURRENT_PATH + L"fish.png";
             const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
-            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
-                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU" });
+            const std::wstring command =
+                BuildCommand({ EXE_PATH, L"-model", modelPath, L"-input", inputPath, L"-PerfOutput", OUTPUT_PATH, L"-perf",
+                               L"-SavePerIterationPerf", L"-BaseOutputPath", tensorDataPath,
+                               L"-PerIterationPath PerIterationData", L"-CPU" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
-            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_GPU.csv",
-                                                  tensorDataPath + L"\\softmaxout_1GpuIteration1.csv"));
+
+            // We need to expect one more line because of the header
+            Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount(tensorDataPath + L"\\PerIterationData\\Summary.csv"));
         }
 
         TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensor)
@@ -585,15 +613,15 @@ namespace WinMLRunnerTest
                                                   tensorDataPath + L"\\softmaxout_1CpuIteration1.csv"));
         }
 
-        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorImageDenotation)
-            const std::wstring modelPath = CURRENT_PATH + L"mnist.onnx";
-            const std::wstring inputPath = CURRENT_PATH + L"mnist_28.png";
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensor)
+            const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"fish.png";
             const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
             const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
                                                         L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
-            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Mnist_8_input_GPU.csv",
-                tensorDataPath + L"\\Plus214_Output_0GpuIteration1.csv"));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_GPU.csv",
+                                                  tensorDataPath + L"\\softmaxout_1GpuIteration1.csv"));
         }
 
         TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensorImageDenotation)
@@ -607,15 +635,15 @@ namespace WinMLRunnerTest
                 tensorDataPath + L"\\Plus214_Output_0CpuIteration1.csv"));
         }
 
-        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorFp16)
-            const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet_fp16.onnx";
-            const std::wstring inputPath = CURRENT_PATH + L"fish.png";
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorImageDenotation)
+            const std::wstring modelPath = CURRENT_PATH + L"mnist.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"mnist_28.png";
             const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
             const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
                                                         L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
-            Assert::AreEqual(true, CompareTensorsFP16(L"OutputTensorData\\Squeezenet_fp16_fish_input_GPU.csv",
-                                                      tensorDataPath + L"\\softmaxout_1GpuIteration1.csv"));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Mnist_8_input_GPU.csv",
+                tensorDataPath + L"\\Plus214_Output_0GpuIteration1.csv"));
         }
 
         TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensorFp16)
@@ -629,41 +657,87 @@ namespace WinMLRunnerTest
                                                       tensorDataPath + L"\\softmaxout_1CpuIteration1.csv"));
         }
 
-        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuPerIterationPerformance)
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorFp16)
+            const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet_fp16.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"fish.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU" });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensorsFP16(L"OutputTensorData\\Squeezenet_fp16_fish_input_GPU.csv",
+                                                      tensorDataPath + L"\\softmaxout_1GpuIteration1.csv"));
+        }
+
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensorTensorizeIdentity)
             const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"fish.png";
             const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
-            const std::wstring command =
-                BuildCommand({ EXE_PATH, L"-model", modelPath, L"-PerfOutput", OUTPUT_PATH, L"-perf",
-                               L"-SavePerIterationPerf", L"-BaseOutputPath", tensorDataPath,
-                               L"-PerIterationPath PerIterationData", L"-CPU" });
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-CPU",
+                                                        L"-Tensor" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_CPU.csv",
+                                                  tensorDataPath + L"\\softmaxout_1CpuIteration1.csv"));
+        }
 
-            // We need to expect one more line because of the header
-            Assert::AreEqual(static_cast<size_t>(2), GetOutputCSVLineCount(tensorDataPath + L"\\PerIterationData\\Summary.csv"));
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorTensorizeIdentity)
+            const std::wstring modelPath = CURRENT_PATH + L"SqueezeNet.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"fish.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU",
+                                                        L"-Tensor Identity" });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\Squeezenet_fish_input_GPU.csv",
+                                                  tensorDataPath + L"\\softmaxout_1GpuIteration1.csv"));
         }
 
-        TEST_METHOD(ProvidedImageInputFolder)
-        {
-            // Make test_folder_input folder before starting the tests
-            std::string mkFolderCommand = "mkdir " + std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
-            system(mkFolderCommand.c_str());
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensorTensorizeScaleMeanStdDev)
+            const std::wstring modelPath = CURRENT_PATH + L"DenseNet121_fp32.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"kitten_224.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-CPU",
+                                                        L"-Tensor Normalize 255 0.485,0.456,0.406 0.229,0.224,0.225" });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\DenseNet121_fp32_kitten_224_input_CPU.csv",
+                                                  tensorDataPath + L"\\fc6_1CpuIteration1.csv"));
+        }
 
-            std::vector<std::string> images = { "fish.png", "kitten_224.png" };
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorTensorizeScaleMeanStdDev)
+            const std::wstring modelPath = CURRENT_PATH + L"DenseNet121_fp32.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"kitten_224.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU",
+                                                        L"-Tensor Normalize 255 0.485,0.456,0.406 0.229,0.224,0.225" });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\DenseNet121_fp32_kitten_224_input_GPU.csv",
+                                                  tensorDataPath + L"\\fc6_1GpuIteration1.csv"));
+        }
 
-            // Copy images from list to test_folder_input
-            for (auto image : images)
-            {
-                std::string copyCommand = "Copy ";
-                copyCommand += image;
-                copyCommand += ' ' + std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
-                system(copyCommand.c_str());
-            }
-            const std::wstring command = BuildCommand({ EXE_PATH, L"-model", L"SqueezeNet.onnx", L"-InputImageFolder", INPUT_FOLDER_PATH });
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyCpuSaveTensorTensorizeScaleMeanStdDevFP16)
+            const std::wstring modelPath = CURRENT_PATH + L"DenseNet121_fp16.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"kitten_224.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-CPU",
+                                                        L"-Tensor Normalize 255 0.485,0.456,0.406 0.229,0.224,0.225" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensors(L"OutputTensorData\\DenseNet121_fp16_kitten_224_input_CPU.csv",
+                                                  tensorDataPath + L"\\fc6_1CpuIteration1.csv"));
+        }
 
-            std::string removeCommand = "rd /s /q ";
-            removeCommand += std::string(INPUT_FOLDER_PATH.begin(), INPUT_FOLDER_PATH.end());
-            system(removeCommand.c_str());
+        TEST_METHOD_WITH_NAME(ProvidedImageInputOnlyGpuSaveTensorTensorizeScaleMeanStdDevFP16)
+            const std::wstring modelPath = CURRENT_PATH + L"DenseNet121_fp16.onnx";
+            const std::wstring inputPath = CURRENT_PATH + L"kitten_224.png";
+            const std::wstring tensorDataPath = TENSOR_DATA_PATH + L"\\" + METHOD_NAME;
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model ", modelPath, L"-input", inputPath,
+                                                        L"-SaveTensorData", L"First", L"-PerIterationPath", tensorDataPath, L"-GPU",
+                                                        L"-Tensor Normalize 255 0.485,0.456,0.406 0.229,0.224,0.225" });
+            Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
+            Assert::AreEqual(true, CompareTensorsFP16(L"OutputTensorData\\DenseNet121_fp16_kitten_224_input_GPU.csv",
+                                                      tensorDataPath + L"\\fc6_1GpuIteration1.csv"));
         }
     };
 
@@ -823,7 +897,7 @@ namespace WinMLRunnerTest
         
         TEST_METHOD(TestTopK)
         {
-            const std::wstring command = BuildCommand({ EXE_PATH, L"-model", L"SqueezeNet.onnx", L"-TopK", L"5" });
+            const std::wstring command = BuildCommand({ EXE_PATH, L"-model", CURRENT_PATH + L"SqueezeNet.onnx", L"-TopK", L"5" });
             Assert::AreEqual(S_OK, RunProc((wchar_t*)command.c_str()));
         }
 
diff --git a/Testing/WinMLRunnerTest/WinMLRunnerTest.vcxproj b/Testing/WinMLRunnerTest/WinMLRunnerTest.vcxproj
index 1a8e4edc..47028fba 100644
--- a/Testing/WinMLRunnerTest/WinMLRunnerTest.vcxproj
+++ b/Testing/WinMLRunnerTest/WinMLRunnerTest.vcxproj
@@ -390,6 +390,72 @@
       <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </Content>
+    <Content Include="..\..\SharedContent\models\DenseNet121_fp32.onnx">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="..\..\SharedContent\models\DenseNet121_fp16.onnx">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="OutputTensorData\DenseNet121_fp32_kitten_224_input_CPU.csv">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="OutputTensorData\DenseNet121_fp32_kitten_224_input_GPU.csv">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="OutputTensorData\DenseNet121_fp16_kitten_224_input_CPU.csv">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
+    <Content Include="OutputTensorData\DenseNet121_fp16_kitten_224_input_GPU.csv">
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">true</DeploymentContent>
+      <ExcludedFromBuild Condition="'$(Configuration)|$(Platform)'=='Release|x64'">false</ExcludedFromBuild>
+      <DeploymentContent Condition="'$(Configuration)|$(Platform)'=='Release|x64'">true</DeploymentContent>
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </Content>
   </ItemGroup>
   <ItemGroup>
     <CopyFileToFolders Include="..\..\SharedContent\media\mnist_28.csv">
diff --git a/Tools/WinMLRunner/README.md b/Tools/WinMLRunner/README.md
index ad3dc2e9..dc3f3b8c 100644
--- a/Tools/WinMLRunner/README.md
+++ b/Tools/WinMLRunner/README.md
@@ -42,7 +42,10 @@ Required command-Line arguments:
 -GPUBoundInput : bind the input to the GPU
 -RGB : load the input as an RGB image
 -BGR : load the input as a BGR image
--Tensor : load the input as a tensor
+-Tensor [function] : load the input as a tensor, with optional function for input preprocessing
+    Optional function arguments:
+        Identity(default) : No input transformations will be performed.
+        Normalize <scale> <means> <stddevs> : float scale factor and comma separated per channel means and stddev for normalization.
 -Perf [all]: capture performance measurements such as timing and memory usage. Specifying "all" will output all measurements
 -Iterations : # times perf measurements will be run/averaged. (maximum: 1024 times)
 -Input <path to input file>: binds image or CSV to model
diff --git a/Tools/WinMLRunner/src/BindingUtilities.h b/Tools/WinMLRunner/src/BindingUtilities.h
index 98319ed6..c045b0c9 100644
--- a/Tools/WinMLRunner/src/BindingUtilities.h
+++ b/Tools/WinMLRunner/src/BindingUtilities.h
@@ -4,6 +4,7 @@
 #include "Common.h"
 #include "Windows.AI.Machinelearning.Native.h"
 #include "d3dx12.h"
+#include "MemoryBuffer.h"
 using namespace winrt::Windows::Media;
 using namespace winrt::Windows::Storage;
 using namespace winrt::Windows::Storage::Streams;
@@ -14,63 +15,6 @@ using namespace winrt::Windows::Graphics::Imaging;
 using namespace winrt::Windows::Graphics::DirectX::Direct3D11;
 using namespace DirectX::PackedVector;
 
-template <TensorKind T> struct TensorKindToArithmeticType
-{
-    static_assert(true, "No TensorKind mapped for given type!");
-};
-template <> struct TensorKindToArithmeticType<TensorKind::UInt8>
-{
-    typedef uint8_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Int8>
-{
-    typedef uint8_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::UInt16>
-{
-    typedef uint16_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Int16>
-{
-    typedef int16_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::UInt32>
-{
-    typedef uint32_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Int32>
-{
-    typedef int32_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::UInt64>
-{
-    typedef uint64_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Int64>
-{
-    typedef int64_t Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Boolean>
-{
-    typedef boolean Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Double>
-{
-    typedef double Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Float>
-{
-    typedef float Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::Float16>
-{
-    typedef float Type;
-};
-template <> struct TensorKindToArithmeticType<TensorKind::String>
-{
-    typedef winrt::hstring Type;
-};
-
 template <TensorKind T> struct TensorKindToPointerType
 {
     static_assert(true, "No TensorKind mapped for given type!");
@@ -81,7 +25,7 @@ template <> struct TensorKindToPointerType<TensorKind::UInt8>
 };
 template <> struct TensorKindToPointerType<TensorKind::Int8>
 {
-    typedef uint8_t Type;
+    typedef int8_t Type;
 };
 template <> struct TensorKindToPointerType<TensorKind::UInt16>
 {
@@ -185,59 +129,61 @@ template <> struct TensorKindToValue<TensorKind::String>
     typedef TensorString Type;
 };
 
-template <TensorKind T, typename PointerType, typename ArithmeticType > PointerType ConvertArithmeticTypeToPointerType(ArithmeticType value)
+template <TensorKind T, typename ToType, typename FromType>
+ToType ConvertToPointerType(FromType value)
 {
     static_assert(true, "No TensorKind mapped for given type!");
+    return 0;
 };
-template <> uint8_t ConvertArithmeticTypeToPointerType<TensorKind::UInt8>(uint8_t value)
+template <> uint8_t ConvertToPointerType<TensorKind::UInt8>(float value)
 {
     return static_cast<uint8_t>(value);
 };
-template <> uint8_t ConvertArithmeticTypeToPointerType<TensorKind::Int8>(uint8_t value)
+template <> int8_t ConvertToPointerType<TensorKind::Int8>(float value)
 {
-    return static_cast<uint8_t>(value);
+    return static_cast<int8_t>(value);
 };
-template <> uint16_t ConvertArithmeticTypeToPointerType<TensorKind::UInt16>(uint16_t value)
+template <> uint16_t ConvertToPointerType<TensorKind::UInt16>(float value)
 {
     return static_cast<uint16_t>(value);
 };
-template <> int16_t ConvertArithmeticTypeToPointerType<TensorKind::Int16>(int16_t value)
+template <> int16_t ConvertToPointerType<TensorKind::Int16>(float value)
 {
     return static_cast<int16_t>(value);
 };
-template <> uint32_t ConvertArithmeticTypeToPointerType<TensorKind::UInt32>(uint32_t value)
+template <> uint32_t ConvertToPointerType<TensorKind::UInt32>(float value)
 {
     return static_cast<uint32_t>(value);
 };
-template <> int32_t ConvertArithmeticTypeToPointerType<TensorKind::Int32>(int32_t value)
+template <> int32_t ConvertToPointerType<TensorKind::Int32>(float value)
 {
     return static_cast<int32_t>(value);
 };
-template <> uint64_t ConvertArithmeticTypeToPointerType<TensorKind::UInt64>(uint64_t value)
+template <> uint64_t ConvertToPointerType<TensorKind::UInt64>(float value)
 {
     return static_cast<uint64_t>(value);
 };
-template <> int64_t ConvertArithmeticTypeToPointerType<TensorKind::Int64>(int64_t value)
+template <> int64_t ConvertToPointerType<TensorKind::Int64>(float value)
 {
     return static_cast<int64_t>(value);
 };
-template <> boolean ConvertArithmeticTypeToPointerType<TensorKind::Boolean>(boolean value)
+template <> boolean ConvertToPointerType<TensorKind::Boolean>(float value)
 {
     return static_cast<boolean>(value);
 };
-template <> double ConvertArithmeticTypeToPointerType<TensorKind::Double>(double value)
+template <> double ConvertToPointerType<TensorKind::Double>(double value)
 {
     return static_cast<double>(value);
 };
-template <> float ConvertArithmeticTypeToPointerType<TensorKind::Float>(float value)
+template <> float ConvertToPointerType<TensorKind::Float>(float value)
 {
     return static_cast<float>(value);
 };
-template <> HALF ConvertArithmeticTypeToPointerType<TensorKind::Float16>(float value)
+template <> HALF ConvertToPointerType<TensorKind::Float16>(float value)
 {
     return XMConvertFloatToHalf(value);
 };
-template <> winrt::hstring ConvertArithmeticTypeToPointerType<TensorKind::String>(winrt::hstring value)
+template <> winrt::hstring ConvertToPointerType<TensorKind::String>(winrt::hstring value)
 {
     return static_cast<winrt::hstring>(value);
 };
@@ -307,8 +253,6 @@ namespace BindingUtilities
                                  const InputDataType inputDataType, const hstring& filePath,
                                  const CommandLineArgs& args, uint32_t iterationNum)
     {
-        assert(inputDataType != InputDataType::Tensor);
-
         // We assume NCHW and NCDHW
         uint64_t width = 0;
         uint64_t height = 0;
@@ -321,6 +265,9 @@ namespace BindingUtilities
             auto stream = file.OpenAsync(FileAccessMode::Read).get();
             // Create the decoder from the stream
             BitmapDecoder decoder = BitmapDecoder::CreateAsync(stream).get();
+            BitmapPixelFormat format = inputDataType == InputDataType::Tensor
+                                           ? decoder.BitmapPixelFormat()
+                                           : TypeHelper::GetBitmapPixelFormat(inputDataType);
 
             // If input dimensions are different from tensor input, then scale / crop while reading
             if (args.IsAutoScale() && (decoder.PixelHeight() != height || decoder.PixelWidth() != width))
@@ -337,23 +284,21 @@ namespace BindingUtilities
 
                 // get the bitmap
                 return decoder
-                    .GetSoftwareBitmapAsync(TypeHelper::GetBitmapPixelFormat(inputDataType), BitmapAlphaMode::Ignore,
-                                            transform, ExifOrientationMode::RespectExifOrientation,
+                    .GetSoftwareBitmapAsync(format, BitmapAlphaMode::Ignore, transform,
+                                            ExifOrientationMode::RespectExifOrientation,
                                             ColorManagementMode::DoNotColorManage)
                     .get();
             }
             else
             {
                 // get the bitmap
-                return decoder
-                    .GetSoftwareBitmapAsync(TypeHelper::GetBitmapPixelFormat(inputDataType), BitmapAlphaMode::Ignore)
-                    .get();
+                return decoder.GetSoftwareBitmapAsync(format, BitmapAlphaMode::Ignore).get();
             }
         }
         catch (...)
         {
-            std::cout << "BindingUtilities: could not open image file, make sure you are using fully qualified paths."
-                      << std::endl;
+            std::wcout << L"BindingUtilities: could not open image file (" << std::wstring(filePath) << L"), "
+                       << L"make sure you are using fully qualified paths." << std::endl;
             return nullptr;
         }
     }
@@ -382,27 +327,24 @@ namespace BindingUtilities
         return inputImage;
     }
 
-    std::vector<std::string> ReadCsvLine(std::ifstream& fileStream)
+    struct InputBufferDesc
     {
-        std::vector<std::string> elementStrings;
-        // Read next line.
-        std::string line;
-        if (!std::getline(fileStream, line))
+        uint8_t* elements;
+        uint32_t totalSizeInBytes;
+        uint32_t numChannelsPerElement;
+        uint32_t elementStrideInBytes;
+        bool isPlanar;
+        TensorKind channelFormat;
+        BitmapPixelFormat elementFormat;
+
+        InputBufferDesc()
+            : elements(nullptr), totalSizeInBytes(0), numChannelsPerElement(0), elementStrideInBytes(0), isPlanar(0),
+              channelFormat(TensorKind::Undefined), elementFormat(BitmapPixelFormat::Unknown)
         {
-            ThrowFailure(L"BindingUtilities: expected more input rows.");
         }
+    };
 
-        // Split the line into strings for each value.
-        std::istringstream elementsString(line);
-        std::string elementString;
-        while (std::getline(elementsString, elementString, ','))
-        {
-            elementStrings.push_back(elementString);
-        }
-        return elementStrings;
-    }
-
-    std::vector<std::string> ParseCSVElementStrings(const std::wstring& csvFilePath)
+    void ReadCSVIntoBuffer(const std::wstring& csvFilePath, InputBufferDesc& inputBufferDesc)
     {
         std::ifstream fileStream;
         fileStream.open(csvFilePath);
@@ -411,74 +353,144 @@ namespace BindingUtilities
             ThrowFailure(L"BindingUtilities: could not open data file.");
         }
 
-        std::vector<std::string> elementStrings = ReadCsvLine(fileStream);
+        uint32_t pos = 0;
+        std::string line;
+        float_t* pData = (float_t*)inputBufferDesc.elements;
+        while (std::getline(fileStream, line, ','))
+        {
+            *pData = std::stof(line);
+            ++pData;
+
+            ++pos;
+            if (pos >= inputBufferDesc.totalSizeInBytes)
+                break;
+        }
+
+        // Check to see if csv didn't fill in entire buffer and throw or fill with zeros?
+        if (pos != (inputBufferDesc.totalSizeInBytes * inputBufferDesc.numChannelsPerElement) / inputBufferDesc.elementStrideInBytes)
+        {
+            throw hresult_invalid_argument(L"CSV input size/shape is different from what model expects!");
+        }
 
-        return elementStrings;
     }
 
-    template <TensorKind T>
-    static ITensor CreateTensor(const CommandLineArgs& args, const std::vector<std::string>& tensorStringInput,
-                                const IVectorView<int64_t>& tensorShape, const InputBindingType inputBindingType)
+    // Roll the array correctly for the tensor
+    template <TensorKind TKind, typename InputType>
+    void CopyTensorFromBuffer(void* actualData, uint32_t tensorHeight, uint32_t tensorWidth,
+                              const InputBufferDesc& inputBufferDesc, float scale,
+                              const std::vector<float>& means, const std::vector<float>& stddevs)
     {
-        using TensorValue = typename TensorKindToValue<T>::Type;
-        using ArithmeticType = typename TensorKindToArithmeticType<T>::Type;
-        using PointerType = typename TensorKindToPointerType<T>::Type;
+        using WriteType = typename TensorKindToPointerType<TKind>::Type;
 
-        std::vector<int64_t> vecShape = {};
-        for (UINT dim = 0; dim < tensorShape.Size(); dim++)
+        WriteType* pDataOut = static_cast<WriteType*>(actualData);
+        InputType* pDataIn = (InputType*)inputBufferDesc.elements;
+        uint32_t elementOffsetMultiplier = inputBufferDesc.isPlanar ? inputBufferDesc.numChannelsPerElement : 1;
+        uint32_t channelOffsetMultiplier = inputBufferDesc.isPlanar ? 1 : tensorHeight * tensorWidth;
+        for (uint32_t element = 0; element < tensorHeight * tensorWidth; ++element)
         {
-            INT64 dimSize = tensorShape.GetAt(dim);
-            if (dimSize > 0) // If the dimension is greater than 0, then it is known.
-            {
-                vecShape.push_back(dimSize);
-            }
-            else // otherwise, make sure that the dimension is -1, representing free dimension. If not, then it's an
-                 // invalid model.
+            for (uint32_t channel = 0; channel < inputBufferDesc.numChannelsPerElement; ++channel)
             {
-                if (dimSize == -1)
-                {
-                    vecShape.push_back(1);
-                }
-                else
-                {
-                    throw hresult_invalid_argument(L"Failed to create a tensor with an unknown dimension of: " +
-                                                   dimSize);
-                }
+                pDataOut[element * elementOffsetMultiplier + channel * channelOffsetMultiplier] = 
+                    ConvertToPointerType<TKind, WriteType>(
+                        ((pDataIn[channel] / scale) - means[channel]) / stddevs[channel]);
             }
+            pDataIn += inputBufferDesc.elementStrideInBytes / sizeof(InputType);
         }
+    }
+
+    template <TensorKind T>
+    static ITensor CreateTensor(const CommandLineArgs& args, const std::vector<int64_t>& tensorShape,
+                                const InputBindingType inputBindingType, const InputBufferDesc& inputBufferDesc)
+    {
+        using TensorValue = typename TensorKindToValue<T>::Type;
+        using WriteType = typename TensorKindToPointerType<T>::Type;
 
         // Map the incoming Tensor as a TensorNative to get the actual data buffer.
-        auto tensorValue = TensorValue::Create(vecShape);
+        auto tensorValue = TensorValue::Create(tensorShape);
 
         com_ptr<ITensorNative> spTensorValueNative;
         tensorValue.as(spTensorValueNative);
 
-        PointerType* actualData;
+        WriteType* actualData;
         uint32_t actualSizeInBytes;
-        spTensorValueNative->GetBuffer(reinterpret_cast<BYTE**>(&actualData),
-                                                                &actualSizeInBytes);
+        THROW_IF_FAILED(spTensorValueNative->GetBuffer(reinterpret_cast<BYTE**>(&actualData), &actualSizeInBytes));
 
-        if (args.IsCSVInput())
+        if (args.IsCSVInput() || args.IsImageInput())
         {
-            if (tensorStringInput.size() != actualSizeInBytes / sizeof(PointerType))
+            // Assumes NCHW
+            uint32_t channels = static_cast<uint32_t>(tensorShape[1]);
+            uint32_t tensorHeight = static_cast<uint32_t>(tensorShape[2]);
+            uint32_t tensorWidth = static_cast<uint32_t>(tensorShape[3]);
+
+            // Check to make sure the sizes are right
+            uint32_t inputElementCount = inputBufferDesc.totalSizeInBytes / inputBufferDesc.elementStrideInBytes;
+            uint32_t outputElementCount = actualSizeInBytes / (channels * sizeof(WriteType));
+            if (inputElementCount != outputElementCount)
             {
-                throw hresult_invalid_argument(L"CSV input size/shape is different from what model expects");
+                throw hresult_invalid_argument(L"Input size / shape is different from what the model expects");
             }
 
-            // Write the elementStrings into the iTensorNative
-            PointerType* dataPtr = actualData;
-            for (const auto &tensorString : tensorStringInput)
+            float scale;
+            std::vector<float> means = {};
+            std::vector<float> stddevs = {};
+
+            const auto& tensorizeArgs = args.TensorizeArgs();
+            const auto& normalizeParams = tensorizeArgs.Normalize;
+            switch (tensorizeArgs.Func)
             {
-                ArithmeticType value;
-                std::stringstream(tensorString) >> value;
-                *dataPtr = ConvertArithmeticTypeToPointerType<T,PointerType,ArithmeticType>(value);
-                dataPtr++;
+                case TensorizeFuncs::Identity:
+                    scale = 1.0f;
+                    means.resize(channels, 0.0f);
+                    stddevs.resize(channels, 1.0f);
+                    break;
+                case TensorizeFuncs::Normalize:
+                    switch (inputBufferDesc.elementFormat)
+                    {
+                        case BitmapPixelFormat::Gray8:
+                        case BitmapPixelFormat::Gray16:
+                        case BitmapPixelFormat::Rgba8:
+                        case BitmapPixelFormat::Rgba16:
+                            scale = normalizeParams.Scale;
+                            means.resize(channels);
+                            stddevs.resize(channels);
+                            for (uint32_t i = 0; i < channels; ++i)
+                            {
+                                means[i] = normalizeParams.Means[i];
+                                stddevs[i] = normalizeParams.StdDevs[i];
+                            }
+                            break;
+                        case BitmapPixelFormat::Bgra8:
+                            scale = normalizeParams.Scale;
+                            means.resize(channels);
+                            stddevs.resize(channels);
+                            for (uint32_t i = 0; i < channels; ++i)
+                            {
+                                means[channels - 1 - i] = normalizeParams.Means[i];
+                                stddevs[channels - 1 - i] = normalizeParams.StdDevs[i];
+                            }
+                            break;
+
+                        default:
+                            throw hresult_invalid_argument(L"CreateTensor<T>: Unhandled SoftwareBitmap pixel format");
+                    }
+                    break;
+                default:
+                    throw hresult_invalid_argument(L"CreateTensor<T>: Unknown Tensorize Function");
+            }
+
+            switch (inputBufferDesc.channelFormat)
+            {
+                case TensorKind::UInt8:
+                    CopyTensorFromBuffer<T, uint8_t>(actualData, tensorHeight, tensorWidth, inputBufferDesc, scale,
+                                                     means, stddevs);
+                    break;
+                case TensorKind::Float:
+                    CopyTensorFromBuffer<T, float>(actualData, tensorHeight, tensorWidth, inputBufferDesc, scale,
+                                                   means, stddevs);
+                    break;
+                default:
+                    throw hresult_not_implemented(L"Creating Tensors for Input Images with unhandled channel format!");
             }
-        }
-        else if (args.IsImageInput())
-        {
-            // Creating Tensors for Input Images haven't been added.
-            throw hresult_not_implemented(L"Creating Tensors for Input Images haven't been implemented!");
         }
 
         if (inputBindingType == InputBindingType::CPU)
@@ -494,15 +506,11 @@ namespace BindingUtilities
                 com_ptr<ID3D12Device> pD3D12Device = nullptr;
                 D3D12CreateDevice(nullptr, D3D_FEATURE_LEVEL::D3D_FEATURE_LEVEL_11_0, __uuidof(ID3D12Device),
                                   reinterpret_cast<void**>(&pD3D12Device));
-                
+
                 pD3D12Device->CreateCommittedResource(
-                    &CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT),
-                    D3D12_HEAP_FLAG_NONE,
-                    &CD3DX12_RESOURCE_DESC::Buffer(
-                        actualSizeInBytes,
-                        D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
-                    D3D12_RESOURCE_STATE_COMMON, nullptr,
-                    __uuidof(ID3D12Resource), pGPUResource.put_void());
+                    &CD3DX12_HEAP_PROPERTIES(D3D12_HEAP_TYPE_DEFAULT), D3D12_HEAP_FLAG_NONE,
+                    &CD3DX12_RESOURCE_DESC::Buffer(actualSizeInBytes, D3D12_RESOURCE_FLAG_ALLOW_UNORDERED_ACCESS),
+                    D3D12_RESOURCE_STATE_COMMON, nullptr, __uuidof(ID3D12Resource), pGPUResource.put_void());
                 if (!args.IsGarbageInput())
                 {
                     com_ptr<ID3D12Resource> imageUploadHeap;
@@ -570,126 +578,217 @@ namespace BindingUtilities
             }
             com_ptr<ITensorStaticsNative> tensorfactory = get_activation_factory<TensorValue, ITensorStaticsNative>();
             com_ptr<::IUnknown> spUnkTensor;
-            tensorfactory->CreateFromD3D12Resource(pGPUResource.get(), vecShape.data(), static_cast<int>(vecShape.size()), spUnkTensor.put());
+            tensorfactory->CreateFromD3D12Resource(pGPUResource.get(), const_cast<int64_t*>(tensorShape.data()),
+                                                   static_cast<int>(tensorShape.size()), spUnkTensor.put());
             TensorValue returnTensor(nullptr);
             spUnkTensor.try_as(returnTensor);
             return returnTensor;
         }
     }
 
-    // Binds tensor floats, ints, doubles from CSV data.
-    ITensor CreateBindableTensor(const ILearningModelFeatureDescriptor& description, const CommandLineArgs& args,
-                                 const InputBindingType inputBindingType)
+    // Process the descriptor to gather and normalize the shape
+    void ProcessDescriptor(const ILearningModelFeatureDescriptor& description, std::vector<int64_t>& shape,
+                           TensorKind& tensorKind, InputBufferDesc& inputBufferDesc)
     {
-        std::vector<std::string> elementStrings;
-        if (!args.CsvPath().empty())
-        {
-            elementStrings = ParseCSVElementStrings(args.CsvPath());
-        }
-
         // Try Image Feature Descriptor
         auto imageFeatureDescriptor = description.try_as<ImageFeatureDescriptor>();
         if (imageFeatureDescriptor)
         {
             int64_t channels;
-            if (imageFeatureDescriptor.BitmapPixelFormat() == BitmapPixelFormat::Gray16 ||
-                imageFeatureDescriptor.BitmapPixelFormat() == BitmapPixelFormat::Gray8)
-            {
-                channels = 1;
-            }
-            else if (imageFeatureDescriptor.BitmapPixelFormat() == BitmapPixelFormat::Bgra8 ||
-                     imageFeatureDescriptor.BitmapPixelFormat() == BitmapPixelFormat::Rgba16 ||
-                     imageFeatureDescriptor.BitmapPixelFormat() == BitmapPixelFormat::Rgba8)
+            inputBufferDesc.elementFormat = imageFeatureDescriptor.BitmapPixelFormat();
+            switch (inputBufferDesc.elementFormat)
             {
-                channels = 3;
-            }
-            else
-            {
-                throw hresult_not_implemented(L"BitmapPixel format not handled by WinMLRunner.");
+                case BitmapPixelFormat::Gray8:
+                case BitmapPixelFormat::Gray16:
+                    channels = 1;
+                    break;
+                case BitmapPixelFormat::Bgra8:
+                case BitmapPixelFormat::Rgba16:
+                case BitmapPixelFormat::Rgba8:
+                    channels = 3;
+                    break;
+                default:
+                    throw hresult_not_implemented(L"BitmapPixel format not yet handled by WinMLRunner.");
             }
-            std::vector<int64_t> shape = { 1, channels, imageFeatureDescriptor.Height(),
-                                           imageFeatureDescriptor.Width() };
-            IVectorView<int64_t> shapeVectorView = single_threaded_vector(std::move(shape)).GetView();
-            return CreateTensor<TensorKind::Float>(args, elementStrings, shapeVectorView, inputBindingType);
+
+            tensorKind = TensorKind::Float;
+            shape.push_back(1);
+            shape.push_back(channels);
+            shape.push_back(static_cast<int64_t>(imageFeatureDescriptor.Height()));
+            shape.push_back(static_cast<int64_t>(imageFeatureDescriptor.Width()));
+            return;
         }
 
         auto tensorDescriptor = description.try_as<TensorFeatureDescriptor>();
         if (tensorDescriptor)
         {
-            switch (tensorDescriptor.TensorKind())
+            IVectorView<int64_t> tensorShape = tensorDescriptor.Shape();
+            for (uint32_t dim = 0; dim < tensorShape.Size(); dim++)
             {
-                case TensorKind::Undefined:
+                int64_t dimSize = tensorShape.GetAt(dim);
+                if (dimSize > 0) // If the dimension is greater than 0, then it is known.
                 {
-                    std::cout << "BindingUtilities: TensorKind is undefined." << std::endl;
-                    throw hresult_invalid_argument();
+                    shape.push_back(dimSize);
                 }
-                case TensorKind::Float:
-                {
-                    return CreateTensor<TensorKind::Float>(args, elementStrings, tensorDescriptor.Shape(),
-                                                           inputBindingType);
-                }
-                break;
-                case TensorKind::Float16:
-                {
-                    return CreateTensor<TensorKind::Float16>(args, elementStrings, tensorDescriptor.Shape(),
-                                                             inputBindingType);
-                }
-                break;
-                case TensorKind::Double:
+                else // otherwise, make sure that the dimension is -1, representing free dimension. If not, then it's an
+                     // invalid model.
                 {
-                    return CreateTensor<TensorKind::Double>(args, elementStrings, tensorDescriptor.Shape(),
-                                                            inputBindingType);
-                }
-                break;
-                case TensorKind::Int8:
-                {
-                    return CreateTensor<TensorKind::Int8>(args, elementStrings, tensorDescriptor.Shape(),
-                                                          inputBindingType);
-                }
-                break;
-                case TensorKind::UInt8:
-                {
-                    return CreateTensor<TensorKind::UInt8>(args, elementStrings, tensorDescriptor.Shape(),
-                                                           inputBindingType);
-                }
-                break;
-                case TensorKind::Int16:
-                {
-                    return CreateTensor<TensorKind::Int16>(args, elementStrings, tensorDescriptor.Shape(),
-                                                           inputBindingType);
-                }
-                break;
-                case TensorKind::UInt16:
-                {
-                    return CreateTensor<TensorKind::UInt16>(args, elementStrings, tensorDescriptor.Shape(),
-                                                            inputBindingType);
-                }
-                break;
-                case TensorKind::Int32:
-                {
-                    return CreateTensor<TensorKind::Int32>(args, elementStrings, tensorDescriptor.Shape(),
-                                                           inputBindingType);
-                }
-                break;
-                case TensorKind::UInt32:
-                {
-                    return CreateTensor<TensorKind::UInt32>(args, elementStrings, tensorDescriptor.Shape(),
-                                                            inputBindingType);
-                }
-                break;
-                case TensorKind::Int64:
-                {
-                    return CreateTensor<TensorKind::Int64>(args, elementStrings, tensorDescriptor.Shape(),
-                                                           inputBindingType);
-                }
-                break;
-                case TensorKind::UInt64:
-                {
-                    return CreateTensor<TensorKind::UInt64>(args, elementStrings, tensorDescriptor.Shape(),
-                                                            inputBindingType);
+                    if (dimSize == -1)
+                    {
+                        shape.push_back(1);
+                    }
+                    else
+                    {
+                        throw hresult_invalid_argument(L"Failed to create a tensor with an unknown dimension of: " +
+                                                       dimSize);
+                    }
                 }
-                break;
             }
+
+            tensorKind = tensorDescriptor.TensorKind();
+            return;
+        }
+
+        throw hresult_invalid_argument(L"ProcessDescriptor: Unknown desription type!");
+    } // namespace BindingUtilities
+
+    // Binds tensor floats, ints, doubles from CSV data.
+    ITensor CreateBindableTensor(const ILearningModelFeatureDescriptor& description, const std::wstring &imagePath,
+                                 const InputBindingType inputBindingType, const InputDataType inputDataType,
+                                 const CommandLineArgs& args, uint32_t iterationNum)
+    {
+        InputBufferDesc inputBufferDesc = {};
+
+        std::vector<int64_t> shape = {};
+        TensorKind tensorKind = TensorKind::Undefined;
+        ProcessDescriptor(description, shape, tensorKind, inputBufferDesc);
+
+        SoftwareBitmap softwareBitmap(nullptr);
+        if (args.IsCSVInput())
+        {
+            inputBufferDesc.channelFormat = TensorKind::Float;
+            inputBufferDesc.isPlanar = true;
+
+            // Assumes shape is in the format of 'NCHW'
+            inputBufferDesc.numChannelsPerElement = static_cast<uint32_t>(shape[1]);
+
+            // Assumes no gaps in the input csv file
+            inputBufferDesc.elementStrideInBytes = inputBufferDesc.numChannelsPerElement * sizeof(float_t);
+
+            inputBufferDesc.totalSizeInBytes = sizeof(float_t);
+            for (uint32_t i = 0; i < shape.size(); ++i)
+                inputBufferDesc.totalSizeInBytes *= static_cast<uint32_t>(shape[i]);
+
+            inputBufferDesc.elements = new uint8_t[inputBufferDesc.totalSizeInBytes];
+
+            ReadCSVIntoBuffer(args.CsvPath(), inputBufferDesc);
+        }
+        else if (args.IsImageInput())
+        {
+            softwareBitmap = LoadImageFile(description, inputDataType, imagePath.c_str(), args, iterationNum);
+
+            // Get Pointers to the SoftwareBitmap data buffers
+            const BitmapBuffer sbBitmapBuffer(softwareBitmap.LockBuffer(BitmapBufferAccessMode::Read));
+            winrt::Windows::Foundation::IMemoryBufferReference sbReference = sbBitmapBuffer.CreateReference();
+            auto sbByteAccess = sbReference.as<::Windows::Foundation::IMemoryBufferByteAccess>();
+            winrt::check_hresult(sbByteAccess->GetBuffer(&inputBufferDesc.elements, &inputBufferDesc.totalSizeInBytes));
+
+            inputBufferDesc.isPlanar = false;
+            inputBufferDesc.elementFormat = softwareBitmap.BitmapPixelFormat();
+            switch (inputBufferDesc.elementFormat)
+            {
+                case BitmapPixelFormat::Gray8:
+                    inputBufferDesc.channelFormat = TensorKind::UInt8;
+                    inputBufferDesc.numChannelsPerElement = 1;
+                    inputBufferDesc.elementStrideInBytes = sizeof(uint8_t);
+                    break;
+                case BitmapPixelFormat::Gray16:
+                    inputBufferDesc.channelFormat = TensorKind::UInt16;
+                    inputBufferDesc.numChannelsPerElement = 1;
+                    inputBufferDesc.elementStrideInBytes = sizeof(uint16_t);
+                    break;
+                case BitmapPixelFormat::Bgra8:
+                    inputBufferDesc.channelFormat = TensorKind::UInt8;
+                    inputBufferDesc.numChannelsPerElement = 3;
+                    inputBufferDesc.elementStrideInBytes = 4 * sizeof(uint8_t);
+                    break;
+                case BitmapPixelFormat::Rgba8:
+                    inputBufferDesc.channelFormat = TensorKind::UInt8;
+                    inputBufferDesc.numChannelsPerElement = 3;
+                    inputBufferDesc.elementStrideInBytes = 4 * sizeof(uint8_t);
+                    break;
+                case BitmapPixelFormat::Rgba16:
+                    inputBufferDesc.channelFormat = TensorKind::UInt16;
+                    inputBufferDesc.numChannelsPerElement = 3;
+                    inputBufferDesc.elementStrideInBytes = 4 * sizeof(uint16_t);
+                    break;
+                default:
+                    throw hresult_invalid_argument(L"Unknown BitmapPixelFormat in input image.");
+            }
+        }
+
+        switch (tensorKind)
+        {
+            case TensorKind::Undefined:
+            {
+                std::cout << "BindingUtilities: TensorKind is undefined." << std::endl;
+                throw hresult_invalid_argument();
+            }
+            case TensorKind::Float:
+            {
+                return CreateTensor<TensorKind::Float>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Float16:
+            {
+                return CreateTensor<TensorKind::Float16>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Double:
+            {
+                return CreateTensor<TensorKind::Double>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Int8:
+            {
+                return CreateTensor<TensorKind::Int8>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::UInt8:
+            {
+                return CreateTensor<TensorKind::UInt8>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Int16:
+            {
+                return CreateTensor<TensorKind::Int16>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::UInt16:
+            {
+                return CreateTensor<TensorKind::UInt16>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Int32:
+            {
+                return CreateTensor<TensorKind::Int32>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::UInt32:
+            {
+                return CreateTensor<TensorKind::UInt32>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::Int64:
+            {
+                return CreateTensor<TensorKind::Int64>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
+            case TensorKind::UInt64:
+            {
+                return CreateTensor<TensorKind::UInt64>(args, shape, inputBindingType, inputBufferDesc);
+            }
+            break;
         }
         std::cout << "BindingUtilities: TensorKind has not been implemented." << std::endl;
         throw hresult_not_implemented();
diff --git a/Tools/WinMLRunner/src/CommandLineArgs.cpp b/Tools/WinMLRunner/src/CommandLineArgs.cpp
index 3a38dd9c..d5d43634 100644
--- a/Tools/WinMLRunner/src/CommandLineArgs.cpp
+++ b/Tools/WinMLRunner/src/CommandLineArgs.cpp
@@ -5,6 +5,7 @@
 #include <ctime>
 #include <iomanip>
 #include <filesystem>
+#include <codecvt>
 #include "Filehelper.h"
 
 using namespace Windows::AI::MachineLearning;
@@ -22,22 +23,31 @@ void CommandLineArgs::PrintUsage()
     std::cout << "  -GPUHighPerformance : run model on GPU with highest performance" << std::endl;
     std::cout << "  -GPUMinPower : run model on GPU with the least power" << std::endl;
 #ifdef DXCORE_SUPPORTED_BUILD
-    std::cout << "  -GPUAdapterName <adapter name substring>: run model on GPU specified by its name. NOTE: Please only use this flag on DXCore supported machines." 
+    std::cout << "  -GPUAdapterName <adapter name substring>: run model on GPU specified by its name. NOTE: Please "
+                 "only use this flag on DXCore supported machines."
               << std::endl;
 #endif
-    std::cout << "  -CreateDeviceOnClient : create the D3D device on the client and pass it to WinML to create session" << std::endl;
+    std::cout << "  -CreateDeviceOnClient : create the D3D device on the client and pass it to WinML to create session"
+              << std::endl;
     std::cout << "  -CreateDeviceInWinML : create the device inside WinML" << std::endl;
     std::cout << "  -CPUBoundInput : bind the input to the CPU" << std::endl;
     std::cout << "  -GPUBoundInput : bind the input to the GPU" << std::endl;
     std::cout << "  -RGB : load the input as an RGB image" << std::endl;
     std::cout << "  -BGR : load the input as a BGR image" << std::endl;
-    std::cout << "  -Tensor : load the input as a tensor" << std::endl;
+    std::cout << "  -Tensor [function] : load the input as a tensor, with optional function for input preprocessing"
+              << std::endl;
+    std::cout << "      Optional function arguments:" << std::endl;
+    std::cout << "          Identity(default) : No input transformations will be performed." << std::endl;
+    std::cout << "          Normalize <scale> <means> <stddevs> : float scale factor and comma separated per channel "
+                 "means and stddev for normalization."
+              << std::endl;
     std::cout << "  -Perf [all]: capture performance measurements such as timing and memory usage. Specifying \"all\" "
                  "will output all measurements"
               << std::endl;
     std::cout << "  -Iterations : # times perf measurements will be run/averaged. (maximum: 1024 times)" << std::endl;
     std::cout << "  -Input <path to input file>: binds image or CSV to model" << std::endl;
-    std::cout << "  -InputImageFolder <path to directory of images> : specify folder of images to bind to model" << std::endl;
+    std::cout << "  -InputImageFolder <path to directory of images> : specify folder of images to bind to model"
+              << std::endl;
     std::cout << "  -TopK <number> : print top <number> values in the result. Default to 1" << std::endl;
     std::cout << "  -BaseOutputPath [<fully qualified path>] : base output directory path for results, default to cwd"
               << std::endl;
@@ -53,7 +63,7 @@ void CommandLineArgs::PrintUsage()
     std::cout << "  -DebugEvaluate: Print evaluation debug output to debug console if debugger is present."
               << std::endl;
     std::cout << "  -Terse: Terse Mode (suppresses repetitive console output)" << std::endl;
-    std::cout << "  -AutoScale <interpolationMode>: Enable image autoscaling and set the interpolation mode [Nearest, "
+    std::cout << "  -AutoScale <interpolationMode> : Enable image autoscaling and set the interpolation mode [Nearest, "
                  "Linear, Cubic, Fant]"
               << std::endl;
     std::cout << std::endl;
@@ -77,6 +87,9 @@ void CheckAPICall(int return_value)
     }
 }
 
+#pragma warning(push)
+#pragma warning(disable : 4996)
+
 CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
 {
     std::wstring sPerfOutputPath;
@@ -112,7 +125,7 @@ CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
                 throw hresult_invalid_argument(
                     L"ERROR: DXCORE isn't supported on this machine. "
                     L"GpuAdapterName flag should only be used with DXCore supported machines.");
-                }
+            }
             m_adapterName = args[++i];
             m_useGPU = true;
         }
@@ -132,7 +145,7 @@ CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
         else if ((_wcsicmp(args[i].c_str(), L"-Model") == 0))
         {
             CheckNextArgument(args, i);
-            m_modelPath = args[++i];
+            m_modelPath = FileHelper::GetAbsolutePath(args[++i]);
         }
         else if ((_wcsicmp(args[i].c_str(), L"-Folder") == 0))
         {
@@ -165,9 +178,46 @@ CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
         {
             m_useBGR = true;
         }
-        else if ((_wcsicmp(args[i].c_str(), L"-Tensor") == 0))
+        else if (_wcsicmp(args[i].c_str(), L"-Tensor") == 0)
         {
             m_useTensor = true;
+            m_tensorizeArgs.Func = TensorizeFuncs::Identity;
+            if (i + 1 < args.size() && args[i + 1][0] != L'-')
+            {
+                if (_wcsicmp(args[++i].c_str(), L"Identity") == 0)
+                {
+                }
+                else if (_wcsicmp(args[i].c_str(), L"Normalize") == 0)
+                {
+                    CheckNextArgument(args, i, i + 1);
+                    CheckNextArgument(args, i, i + 2);
+                    CheckNextArgument(args, i, i + 3);
+
+                    m_tensorizeArgs.Func = TensorizeFuncs::Normalize;
+                    m_tensorizeArgs.Normalize.Scale = (float)_wtof(args[++i].c_str());
+
+                    std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
+                    std::istringstream means(converter.to_bytes(args[++i]));
+                    std::string mean;
+                    while (std::getline(means, mean, ','))
+                        m_tensorizeArgs.Normalize.Means.push_back((float)std::stof(mean.c_str()));
+
+                    std::istringstream stddevs(converter.to_bytes(args[++i]));
+                    std::string stddev;
+                    while (std::getline(stddevs, stddev, ','))
+                        m_tensorizeArgs.Normalize.StdDevs.push_back((float)std::stof(stddev.c_str()));
+
+                    if (m_tensorizeArgs.Normalize.Means.size() != m_tensorizeArgs.Normalize.StdDevs.size())
+                        throw hresult_invalid_argument(
+                            L"-Tensor Normalize: must be the same number of mean and stddev arguments!");
+                }
+                else
+                {
+                    std::wstring msg = L"-Tensor unknown option ";
+                    msg += args[i].c_str();
+                    throw hresult_invalid_argument(msg.c_str());
+                }
+            }
         }
         else if ((_wcsicmp(args[i].c_str(), L"-CPUBoundInput") == 0))
         {
@@ -257,7 +307,7 @@ CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
                 throw hresult_invalid_argument(L"Unknown SaveTensorData Mode[" + m_saveTensorMode + L"]!");
             }
         }
-        else if (_wcsicmp(args[i].c_str(), L"-version") == 0)
+        else if (_wcsicmp(args[i].c_str(), L"-Version") == 0)
         {
             TCHAR szExeFileName[MAX_PATH];
             auto ret = GetModuleFileName(NULL, szExeFileName, MAX_PATH);
@@ -356,6 +406,8 @@ CommandLineArgs::CommandLineArgs(const std::vector<std::wstring>& args)
     CheckForInvalidArguments();
 }
 
+#pragma warning(pop)
+
 void CommandLineArgs::PopulateInputImagePaths()
 {
     for (auto& it : std::filesystem::directory_iterator(m_inputImageFolderPath))
@@ -372,8 +424,7 @@ void CommandLineArgs::PopulateInputImagePaths()
     }
 }
 
-void CommandLineArgs::SetupOutputDirectories(const std::wstring& sBaseOutputPath,
-                                             const std::wstring& sPerfOutputPath,
+void CommandLineArgs::SetupOutputDirectories(const std::wstring& sBaseOutputPath, const std::wstring& sPerfOutputPath,
                                              const std::wstring& sPerIterationDataPath)
 {
     std::filesystem::path PerfOutputPath(sPerfOutputPath);
@@ -431,12 +482,13 @@ void CommandLineArgs::SetupOutputDirectories(const std::wstring& sBaseOutputPath
     }
 }
 
-void CommandLineArgs::CheckNextArgument(const std::vector<std::wstring>& args, UINT i)
+void CommandLineArgs::CheckNextArgument(const std::vector<std::wstring>& args, UINT argIdx, UINT checkIdx)
 {
-    if (i + 1 >= args.size() || args[i + 1][0] == L'-')
+    UINT localCheckIdx = checkIdx == 0 ? argIdx + 1 : checkIdx;
+    if (localCheckIdx >= args.size() || args[localCheckIdx][0] == L'-')
     {
         std::wstring msg = L"Invalid parameter for ";
-        msg += args[i].c_str();
+        msg += args[argIdx].c_str();
         throw hresult_invalid_argument(msg.c_str());
     }
 }
diff --git a/Tools/WinMLRunner/src/CommandLineArgs.h b/Tools/WinMLRunner/src/CommandLineArgs.h
index 51a6ca00..3f09b3d7 100644
--- a/Tools/WinMLRunner/src/CommandLineArgs.h
+++ b/Tools/WinMLRunner/src/CommandLineArgs.h
@@ -1,6 +1,29 @@
 #pragma once
 #include "Common.h"
 
+enum TensorizeFuncs
+{
+    Identity = 0,
+    Normalize
+};
+
+class TensorizeArgs
+{
+public:
+    TensorizeFuncs Func;
+    struct _Normalize
+    {
+        float Scale;
+        std::vector<float> Means;
+        std::vector<float> StdDevs;
+    } Normalize;
+
+    TensorizeArgs() : Func(TensorizeFuncs::Identity)
+    {
+        Normalize.Scale = 1.0f;
+    };
+};
+
 class CommandLineArgs
 {
 public:
@@ -35,6 +58,8 @@ class CommandLineArgs
     const std::wstring& GetGPUAdapterName() const { return m_adapterName; }
 #endif
 
+    const TensorizeArgs& TensorizeArgs() const { return m_tensorizeArgs; }
+
     bool UseRGB() const
     {
         // If an image is specified without flags, we load it as a BGR image by default
@@ -152,6 +177,7 @@ class CommandLineArgs
     bool m_saveTensor = false;
     bool m_timeLimitIterations = false;
     std::wstring m_saveTensorMode = L"First";
+    ::TensorizeArgs m_tensorizeArgs;
 
     std::wstring m_modelFolderPath;
     std::wstring m_modelPath;
@@ -173,7 +199,7 @@ class CommandLineArgs
     uint32_t m_topK = 1;
     std::vector<std::pair<std::string, std::string>> m_perfFileMetadata;
 
-    void CheckNextArgument(const std::vector<std::wstring>& args, UINT i);
+    void CheckNextArgument(const std::vector<std::wstring>& args, UINT argIdx, UINT checkIdx = 0);
     void CheckForInvalidArguments();
     void SetupOutputDirectories(const std::wstring& sBaseOutputPath, const std::wstring& sPerfOutputPath,
                                 const std::wstring& sPerIterationDataPath);
diff --git a/Tools/WinMLRunner/src/OutputHelper.h b/Tools/WinMLRunner/src/OutputHelper.h
index af3aa4b2..9972052b 100644
--- a/Tools/WinMLRunner/src/OutputHelper.h
+++ b/Tools/WinMLRunner/src/OutputHelper.h
@@ -452,18 +452,12 @@ class OutputHelper
 
     static std::wstring FeatureDescriptorToString(const ILearningModelFeatureDescriptor& descriptor)
     {
-        // IMPORTANT: This tensorKinds array needs to match the "enum class TensorKind" idl in
-        // Windows.AI.MachineLearning.0.h
-        const std::wstring tensorKind[] = {
-            L"Undefined", L"Float",   L"UInt8",   L"Int8",   L"UInt16", L"Int16",  L"Int32",     L"Int64",
-            L"String",    L"Boolean", L"Float16", L"Double", L"UInt32", L"UInt64", L"Complex64", L"Complex128",
-        };
         switch (descriptor.Kind())
         {
             case LearningModelFeatureKind::Tensor:
             {
                 auto tensorDescriptor = descriptor.as<TensorFeatureDescriptor>();
-                return tensorKind[(int)tensorDescriptor.TensorKind()];
+                return TypeHelper::Stringify(tensorDescriptor.TensorKind());
             }
             case LearningModelFeatureKind::Image:
             {
@@ -475,7 +469,7 @@ class OutputHelper
             case LearningModelFeatureKind::Map:
             {
                 auto mapDescriptor = descriptor.as<MapFeatureDescriptor>();
-                std::wstring str = L"Map<" + tensorKind[(int)mapDescriptor.KeyKind()] + L",";
+                std::wstring str = L"Map<" + TypeHelper::Stringify(mapDescriptor.KeyKind()) + L",";
                 str += FeatureDescriptorToString(mapDescriptor.ValueDescriptor());
                 str += L">";
                 return str;
diff --git a/Tools/WinMLRunner/src/Run.cpp b/Tools/WinMLRunner/src/Run.cpp
index caa27bdd..ca434e44 100644
--- a/Tools/WinMLRunner/src/Run.cpp
+++ b/Tools/WinMLRunner/src/Run.cpp
@@ -19,14 +19,15 @@ std::vector<ILearningModelFeatureValue> GenerateInputFeatures(const LearningMode
     {
         std::wcout << L"Generating input feature(s) with image: " << imagePath << std::endl;
     }
-    for (uint32_t i = 0; i < model.InputFeatures().Size(); i++)
+    for (uint32_t inputNum = 0; inputNum < model.InputFeatures().Size(); inputNum++)
     {
-        auto&& description = model.InputFeatures().GetAt(i);
+        auto&& description = model.InputFeatures().GetAt(inputNum);
 
         if (inputDataType == InputDataType::Tensor)
         {
             // If CSV data is provided, then every input will contain the same CSV data
-            auto tensorFeature = BindingUtilities::CreateBindableTensor(description, args, inputBindingType);
+            auto tensorFeature = BindingUtilities::CreateBindableTensor(description, imagePath, inputBindingType, inputDataType,
+                                                                        args, iterationNum);
             inputFeatures.push_back(tensorFeature);
         }
         else
diff --git a/Tools/WinMLRunner/src/TypeHelper.h b/Tools/WinMLRunner/src/TypeHelper.h
index b17da804..db18a684 100644
--- a/Tools/WinMLRunner/src/TypeHelper.h
+++ b/Tools/WinMLRunner/src/TypeHelper.h
@@ -111,18 +111,61 @@ class TypeHelper
         throw "No name found for this DeviceCreationLocation.";
     }
 
+    static std::wstring Stringify(TensorKind tensorKind)
+    {
+        // IMPORTANT: This tensorKinds array needs to match the "enum class TensorKind" idl in
+        // Windows.AI.MachineLearning.0.h
+        switch (tensorKind)
+        {
+            case TensorKind::Undefined:
+                return L"Undefined";
+            case TensorKind::Float:
+                return L"Float";
+            case TensorKind::UInt8:
+                return L"UInt8";
+            case TensorKind::Int8:
+                return L"Int8";
+            case TensorKind::UInt16:
+                return L"UInt16";
+            case TensorKind::Int16:
+                return L"Int16";
+            case TensorKind::Int32:
+                return L"Int32";
+            case TensorKind::Int64:
+                return L"Int64";
+            case TensorKind::String:
+                return L"String";
+            case TensorKind::Boolean:
+                return L"Boolean";
+            case TensorKind::Float16:
+                return L"Float16";
+            case TensorKind::Double:
+                return L"Double";
+            case TensorKind::UInt32:
+                return L"UInt32";
+            case TensorKind::UInt64:
+                return L"UInt64";
+            case TensorKind::Complex64:
+                return L"Complex64";
+            case TensorKind::Complex128:
+                return L"Complex128";
+        };
+
+        throw "No name found for this TensorKind.";
+    }
+
     static LearningModelDeviceKind GetWinmlDeviceKind(DeviceType deviceType)
     {
         switch (deviceType)
         {
             case DeviceType::CPU:
-                return LearningModelDeviceKind::Cpu;
-            case DeviceType::DefaultGPU:
-                return LearningModelDeviceKind::DirectX;
-            case DeviceType::MinPowerGPU:
-                return LearningModelDeviceKind::DirectXMinPower;
-            case DeviceType::HighPerfGPU:
-                return LearningModelDeviceKind::DirectXHighPerformance;
+                    return LearningModelDeviceKind::Cpu;
+                case DeviceType::DefaultGPU:
+                    return LearningModelDeviceKind::DirectX;
+                case DeviceType::MinPowerGPU:
+                    return LearningModelDeviceKind::DirectXMinPower;
+                case DeviceType::HighPerfGPU:
+                    return LearningModelDeviceKind::DirectXHighPerformance;
         }
 
         throw "No LearningModelDeviceKind found for this DeviceType.";