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TensorFlowSharp are .NET bindings to the TensorFlow library published here:

This surfaces the C API as a strongly-typed .NET API for use from C# and F#.

The API binding is pretty much done, and at this point, I am polishing the API to make it more pleasant to use from C# and F# and resolving some of the kinks and TODO-items that I left while I was doing the work.

The current API documentation is here.

Using TensorFlowSharp


The easiest way to get started is to use the NuGet package for TensorFlowSharp which contains both the .NET API as well as the native libraries for 64-bit Linux, Mac and Windows using the CPU backend.

You can install using NuGet like this:

nuget install TensorFlowSharp

Or select it from the NuGet packages UI on Visual Studio.

On Visual Studio, make sure that you are targeting .NET 4.6.1 or later, as this package uses some features of newer .NETs. Otherwise, the package will not be added. Once you do this, you can just use the TensorFlowSharp nuget

Alternatively, you can download it directly.

Using TensorFlowSharp

Your best source of information right now are the SampleTest that exercises various APIs of TensorFlowSharp, or the stand-alone samples located in "Examples".

This API binding is closer design-wise to the Java and Go bindings which use explicit TensorFlow graphs and sessions. Your application will typically create a graph (TFGraph) and setup the operations there, then create a session from it (TFSession), then use the session runner to setup inputs and outputs and execute the pipeline.

Something like this:

using(var graph = new TFGraph ())
    graph.Import (File.ReadAllBytes ("MySavedModel"));
    var session = new TFSession (graph);
    var runner = session.GetRunner ();
    runner.AddInput (graph ["input"] [0], tensor);
    runner.Fetch (graph ["output"] [0]);

    var output = runner.Run ();

    // Fetch the results from output:
    TFTensor result = output [0];

In scenarios where you do not need to setup the graph independently, the session will create one for you. The following example shows how to abuse TensorFlow to compute the addition of two numbers:

using (var session = new TFSession())
    var graph = session.Graph;

    var a = graph.Const(2);
    var b = graph.Const(3);
    Console.WriteLine("a=2 b=3");

    // Add two constants
    var addingResults = session.GetRunner().Run(graph.Add(a, b));
    var addingResultValue = addingResults.GetValue();
    Console.WriteLine("a+b={0}", addingResultValue);

    // Multiply two constants
    var multiplyResults = session.GetRunner().Run(graph.Mul(a, b));
    var multiplyResultValue = multiplyResults.GetValue();
    Console.WriteLine("a*b={0}", multiplyResultValue);

Here is an F# scripting version of the same example, you can use this in F# Interactive:

#r @"packages\TensorFlowSharp.1.4.0\lib\net461\TensorFlowSharp.dll"

open System
open System.IO
open TensorFlow

// set the path to find the native DLL
    Environment.GetEnvironmentVariable("Path") + ";" + __SOURCE_DIRECTORY__ + @"/packages/TensorFlowSharp.1.2.2/native")

module AddTwoNumbers = 
    let session = new TFSession()
    let graph = session.Graph

    let a = graph.Const(new TFTensor(2))
    let b = graph.Const(new TFTensor(3))
    Console.WriteLine("a=2 b=3")

    // Add two constants
    let addingResults = session.GetRunner().Run(graph.Add(a, b))
    let addingResultValue = addingResults.GetValue()
    Console.WriteLine("a+b={0}", addingResultValue)

    // Multiply two constants
    let multiplyResults = session.GetRunner().Run(graph.Mul(a, b))
    let multiplyResultValue = multiplyResults.GetValue()
    Console.WriteLine("a*b={0}", multiplyResultValue)

Working on TensorFlowSharp

If you want to work on extending TensorFlowSharp or contribute to its development read the file.

Possible Contributions

Build More Tests

Would love to have more tests to ensure the proper operation of the framework.


The binding is pretty much complete, and at this point, I want to improve the API to be easier and more pleasant to use from both C# and F#. Creating samples that use Tensorflow is a good way of finding easy wins on the usability of the API, there are some here:


Mobile: we need to package the library for consumption on Android and iOS.

Documentation Styling

The API documentation has not been styled, I am using the barebones template for documentation, and it can use some work.


I have logged some usability problems and bugs in Issues, feel free to take on one of those tasks.

Notes on OpDefs

Look at:

./tensorflow/core/ops/ops.pbtxt AvgPool3D and: ./tensorflow/core/ops/ for the C++ implementation with type definitions

Docs on types:


Much of the online documentation comes from TensorFlow and is licensed under the terms of Apache 2 License, in particular all the generated documentation for the various operations that is generated by using the tensorflow reflection APIs.

Last API update: a4b352bfddd518b540c30e456f3bc0027ba9351f


TensorFlow API for .NET languages







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