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It contains libtensorflow files (Windows cpu, Windows gpu(from anaconda python package), Android native binaries).
TensorFlowSharp are .NET bindings to the TensorFlow library published here:
https://github.com/tensorflow/tensorflow
This surfaces the C API as a strongly-typed C# API.
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.
My work-in-progress API documentation current API documentation.
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 TensorFlowSharpOr select it from the NuGet packages UI on Visual Studio.
Alternatively, you can download it directly.
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[0].GetValue();
Console.WriteLine("a+b={0}", addingResultValue);
// Multiply two constants
var multiplyResults = session.GetRunner().Run(graph.Mul(a, b));
var multiplyResultValue = multiplyResults[0].GetValue();
Console.WriteLine("a*b={0}", multiplyResultValue);
}TensorFlowSharp are bindings to the native TensorFlow library.
You can either use the TensorFlow C-library release binaries, or build your own from source. Here are some pre-built TensorFlow binaries you can use for each platform:
- Linux
- Mac: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-1.1.0.tar.gz
- Windows: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-1.2.0-rc0.zip
Unpack the above .tar.gz suitable for your system on a prefix that your
system's dynamic linker can use, for example, go to /usr/local and unpack there.
Mac note: the package contains a .so file, you will need to rename this to .dylib for
it to work.
Once you do that, you need to open the solution file on the top level directory and build. This will produce both the TensorFlowSharp library as well as compile the tests and samples.
To build the TensorFlow C library from source, follow these instructions.
This includes checking out the Tensorflow sources, installing Bazel, and building the core.
Once you do that, you will need to build the shared library. First, in the tensorflow directory, run:
./configure and answer the various prompts about your build. Important: building with CUDA support provides better runtime performance but has additional dependencies as discussed in the Tensorflow installation Web page.
Once configured, run:
bazel build -c opt //tensorflow:libtensorflow.soIf you want debug symbols for Tensorflow, while debugging the binding:
bazel build -c dbg --strip=never //tensorflow:libtensorflow.soYou will need the generated library (libtensorflow.so) to be installed in a
system accessible location like /usr/local/lib
On Linux:
sudo cp bazel-bin/tensorflow/libtensorflow.so /usr/local/lib/On MacOS:
sudo cp bazel-bin/tensorflow/libtensorflow.so /usr/local/lib/libtensorflow.dylibI am currently using Visual Studio for Mac to do the development, but this should work on Windows with VS and Linux with MonoDevelop.
Before the solution will run you will need the shared library generated to be on a location accessibly by the Mono runtime (for example /usr/local/lib).
While Tensorflow builds a library with the extension .so, you will need to make sure that it has the proper name for your platform (tensorflow.dll on Windows, tensorflow.dylib on Mac) and copy that there.
Tensorflow is a 64-bit library, so you will need to use a 64-bit Mono to run, at home (where I am doing this work), I have a copy of 64-bit Mono on /mono, so you will want to set that in your project configuration, to do this:
Ensure that your Build/Compiler settings set "Platform Target" to "x64".
Open the solution file in the top directory, and when you hit run, this will run the API test.
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:
https://github.com/tensorflow/models
Mobile: we need to package the library for consumption on Android and iOS.
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.
Look at:
./tensorflow/core/ops/ops.pbtxt AvgPool3D and: ./tensorflow/core/ops/nn_ops.cc for the C++ implementation with type definitions
Docs on types: https://www.tensorflow.org/extend/adding_an_op
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