TensorFlowSharp 1.5.0

There is a newer version of this package available.
See the version list below for details.
dotnet add package TensorFlowSharp --version 1.5.0                
NuGet\Install-Package TensorFlowSharp -Version 1.5.0                
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="TensorFlowSharp" Version="1.5.0" />                
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add TensorFlowSharp --version 1.5.0                
#r "nuget: TensorFlowSharp, 1.5.0"                
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install TensorFlowSharp as a Cake Addin
#addin nuget:?package=TensorFlowSharp&version=1.5.0

// Install TensorFlowSharp as a Cake Tool
#tool nuget:?package=TensorFlowSharp&version=1.5.0                

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

You can also access the API documentation.

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.1.5.0\lib\net461\TensorFlowSharp.dll"

open System
open System.IO
open TensorFlow

// set the path to find the native DLL
Environment.SetEnvironmentVariable("Path", 
    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)
Product Compatible and additional computed target framework versions.
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.NET Core netcoreapp2.0 was computed.  netcoreapp2.1 was computed.  netcoreapp2.2 was computed.  netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
.NET Standard netstandard2.0 is compatible.  netstandard2.1 was computed. 
.NET Framework net45 is compatible.  net451 was computed.  net452 was computed.  net46 was computed.  net461 is compatible.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
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NuGet packages (4)

Showing the top 4 NuGet packages that depend on TensorFlowSharp:

Package Downloads
DeepMorphy

Morphological analyzer for Russian language

SiaNet.Backend.TensorFlowLib

TensorFlow backend for SiaNet library. Please install SiaNet along with this backend.

Neuromatic

Package Description

Crosser.EdgeNode.Modules.TensorFlow

Package Description

GitHub repositories (2)

Showing the top 2 popular GitHub repositories that depend on TensorFlowSharp:

Repository Stars
cesarsouza/keras-sharp
Keras# initiated as an effort to port the Keras deep learning library to C#, supporting both TensorFlow and CNTK
Azure/sg-aks-workshop
Security + Governance Workshop
Version Downloads Last updated
1.15.1 220,534 12/4/2019
1.15.0 5,463 11/25/2019
1.15.0-pre2 1,193 11/7/2019
1.15.0-pre1 1,160 11/5/2019
1.13.1 93,049 11/4/2019
1.13.0 140,688 5/1/2019
1.12.0 52,035 12/6/2018
1.11.0 15,421 10/2/2018
1.10.0 6,576 9/7/2018
1.9.0 7,113 8/7/2018
1.9.0-pre1 1,770 8/2/2018
1.8.0-pre1 7,688 5/25/2018
1.7.0 38,283 4/15/2018
1.7.0-pre1 1,530 4/3/2018
1.6.0-pre1 1,800 3/11/2018
1.5.1-pre1 1,290 3/1/2018
1.5.0 13,138 1/27/2018
1.5.0-pre2 1,221 1/24/2018
1.5.0-pre1 1,288 1/14/2018
1.4.0 12,489 11/22/2017
1.4.0-pre1 1,739 11/5/2017
1.3.1-pre1 1,258 9/15/2017
1.3.0 3,791 9/15/2017
1.3.0-pre1 2,022 8/26/2017
1.2.2 12,877 6/28/2017
1.2.1 1,543 6/28/2017
0.96.0 9,300 5/21/2017
0.95.0 1,437 5/21/2017
0.94.0 1,461 5/21/2017
0.13.1 1,030 11/4/2019
0.13.0 2,198 5/1/2019

Adds support for TensorFlow 1.5

* No longer a -pre release
* Ships the latest official 1.5 packages (January 26th, Build #80 Mac, Linux, #59 Windows)
* This brings support for the TensorFlow 1.5 API
* New transpose overload without explicit perm parameter (Cesar Souza)
* New ReduceProd method (Cesar Souza)
* Supports for TensorFlow.Cond (Cesar Souza)
* Ships the latest official 1.5 packages.