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migueldeicazamigueldeicaza
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Shuffle samples
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ExampleInceptionInference/ExampleInceptionInference.csproj renamed to Examples/ExampleInceptionInference/ExampleInceptionInference.csproj

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<Compile Include="Properties\AssemblyInfo.cs" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\TensorFlowSharp\TensorFlowSharp.csproj">
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<ProjectReference Include="..\..\TensorFlowSharp\TensorFlowSharp.csproj">
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<Project>{0264C321-34F4-46AF-819E-168D1E597232}</Project>
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<Name>TensorFlowSharp</Name>
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</ProjectReference>
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<None Include="packages.config" />
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</ItemGroup>
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<Import Project="$(MSBuildBinPath)\Microsoft.CSharp.targets" />
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</Project>
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</Project>

ExampleInceptionInference/Program.cs renamed to Examples/ExampleInceptionInference/Program.cs

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ExampleInceptionInference/Properties/AssemblyInfo.cs renamed to Examples/ExampleInceptionInference/Properties/AssemblyInfo.cs

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‎Examples/ExampleInceptionInference/README.md‎

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An example for using the TensorFlow C# API for image recognition
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using a pre-trained inception model (http://arxiv.org/abs/1512.00567).
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Sample usage:
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```
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mono ExampleInceptionInference.exe [--dir=/tmp/modeldir] imagefile...
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The pre-trained model takes input in the form of a 4-dimensional
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tensor with shape [ BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, 3 ],
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where:
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- BATCH_SIZE allows for inference of multiple images in one pass through the graph
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- IMAGE_HEIGHT is the height of the images on which the model was trained
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- IMAGE_WIDTH is the width of the images on which the model was trained
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- 3 is the (R, G, B) values of the pixel colors represented as a float.
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And produces as output a vector with shape [ NUM_LABELS ].
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output[i] is the probability that the input image was recognized as
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having the i-th label.
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A separate file contains a list of string labels corresponding to the
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integer indices of the output.
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This example:
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- Loads the serialized representation of the pre-trained model into a Graph
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- Creates a Session to execute operations on the Graph
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- Converts an image file to a Tensor to provide as input to a Session run
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- Executes the Session and prints out the label with the highest probability
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To convert an image file to a Tensor suitable for input to the Inception model,
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this example:
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- Constructs another TensorFlow graph to normalize the image into a
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form suitable for the model (for example, resizing the image)
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- Creates an executes a Session to obtain a Tensor in this normalized form.

ExampleInceptionInference/packages.config renamed to Examples/ExampleInceptionInference/packages.config

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FExampleInceptionInference/AssemblyInfo.fs renamed to Examples/FExampleInceptionInference/AssemblyInfo.fs

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FExampleInceptionInference/FExampleInceptionInference.fsproj renamed to Examples/FExampleInceptionInference/FSharpExampleInceptionInference.fsproj

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<Compile Include="Program.fs" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\TensorFlowSharp\TensorFlowSharp.csproj">
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<ProjectReference Include="..\..\TensorFlowSharp\TensorFlowSharp.csproj">
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<Project>{0264C321-34F4-46AF-819E-168D1E597232}</Project>
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<Name>TensorFlowSharp</Name>
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</ProjectReference>
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</Properties>
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</MonoDevelop>
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</ProjectExtensions>
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</Project>
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</Project>

FExampleInceptionInference/Program.fs renamed to Examples/FExampleInceptionInference/Program.fs

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‎Examples/FExampleInceptionInference/README.md‎

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An example for using the TensorFlow F# API for image recognition
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using a pre-trained inception model (http://arxiv.org/abs/1512.00567).
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Sample usage:
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```
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mono ExampleInceptionInference.exe [--dir=/tmp/modeldir] imagefile...
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The pre-trained model takes input in the form of a 4-dimensional
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tensor with shape [ BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, 3 ],
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where:
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- BATCH_SIZE allows for inference of multiple images in one pass through the graph
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- IMAGE_HEIGHT is the height of the images on which the model was trained
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- IMAGE_WIDTH is the width of the images on which the model was trained
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- 3 is the (R, G, B) values of the pixel colors represented as a float.
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And produces as output a vector with shape [ NUM_LABELS ].
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output[i] is the probability that the input image was recognized as
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having the i-th label.
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A separate file contains a list of string labels corresponding to the
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integer indices of the output.
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This example:
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- Loads the serialized representation of the pre-trained model into a Graph
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- Creates a Session to execute operations on the Graph
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- Converts an image file to a Tensor to provide as input to a Session run
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- Executes the Session and prints out the label with the highest probability
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To convert an image file to a Tensor suitable for input to the Inception model,
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this example:
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- Constructs another TensorFlow graph to normalize the image into a
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form suitable for the model (for example, resizing the image)
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- Creates an executes a Session to obtain a Tensor in this normalized form.

‎TensorFlowSharp.sln‎

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@@ -9,9 +9,11 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "OpGenerator", "OpGenerator\
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Learn", "Learn\Learn.csproj", "{20567EDE-7C20-4734-B489-C6D303843105}"
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ExampleInceptionInference", "ExampleInceptionInference\ExampleInceptionInference.csproj", "{069A6736-7711-4805-8660-A267E713BC54}"
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ExampleInceptionInference", "Examples\ExampleInceptionInference\ExampleInceptionInference.csproj", "{069A6736-7711-4805-8660-A267E713BC54}"
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EndProject
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Project("{f2a71f9b-5d33-465a-a702-920d77279786}") = "FExampleInceptionInference", "FExampleInceptionInference\FExampleInceptionInference.fsproj", "{03FB7F3A-6D24-4033-9B04-69AD8A198CCF}"
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Project("{f2a71f9b-5d33-465a-a702-920d77279786}") = "FSharpExampleInceptionInference", "Examples\FExampleInceptionInference\FSharpExampleInceptionInference.fsproj", "{03FB7F3A-6D24-4033-9B04-69AD8A198CCF}"
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Examples", "Examples", "{674EC1D7-9649-462E-A7A8-93D0DE84FE64}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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{03FB7F3A-6D24-4033-9B04-69AD8A198CCF}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{03FB7F3A-6D24-4033-9B04-69AD8A198CCF}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(NestedProjects) = preSolution
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{069A6736-7711-4805-8660-A267E713BC54} = {674EC1D7-9649-462E-A7A8-93D0DE84FE64}
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{03FB7F3A-6D24-4033-9B04-69AD8A198CCF} = {674EC1D7-9649-462E-A7A8-93D0DE84FE64}
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EndGlobalSection
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EndGlobal

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