LostTech.TensorFlow 2.5.0-preview1

.NET Standard 2.0 This package targets .NET Standard 2.0. The package is compatible with this framework or higher.
This is a prerelease version of LostTech.TensorFlow.
There is a newer prerelease version of this package available.
See the version list below for details. 
dotnet add package LostTech.TensorFlow --version 2.5.0-preview1                

                    
                

            
NuGet\Install-Package LostTech.TensorFlow -Version 2.5.0-preview1
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="LostTech.TensorFlow" Version="2.5.0-preview1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
paket add LostTech.TensorFlow --version 2.5.0-preview1                

                    
                

            
#r "nuget: LostTech.TensorFlow, 2.5.0-preview1"
#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 LostTech.TensorFlow as a Cake Addin
#addin nuget:?package=LostTech.TensorFlow&version=2.5.0-preview1&prerelease

// Install LostTech.TensorFlow as a Cake Tool
#tool nuget:?package=LostTech.TensorFlow&version=2.5.0-preview1&prerelease

FULL TensorFlow 2.5+ for .NET with Keras. Build, train, checkpoint, execute models.

Samples: https://github.com/losttech/Gradient-Samples, https://github.com/losttech/YOLOv4, https://github.com/losttech/Siren
Deep learning with .NET blog: https://ml.blogs.losttech.software/

Comparison with TensorFlowSharp: https://github.com/losttech/Gradient/#why-not-tensorflowsharp
Comparison with TensorFlow.NET: https://github.com/losttech/Gradient/#why-not-tensorflow-net

Allows building arbitrary machine learning models, training them, and loading and executing pre-trained models using the most popular machine learning framework out there: TensorFlow. All from your favorite comfy .NET language. Supports both CPU and GPU training (the later requires CUDA or a special build of TensorFlow).

Provides access to full tf.keras, estimators and many more APIs.

Free for non-commercial use. For licensing options see https://losttech.software/buy_gradient.html

!!NOTE!!

This version requires Python 3.x x64 to be installed with tensorflow 2.5.x. See the official installation instructions in https://www.tensorflow.org/install/ (ensure you are installing version 2.5 to avoid hard-to-debug issues).

Please, report any issues to https://github.com/losttech/Gradient/issues
For community support use https://stackoverflow.com/ with tags (must be all 3 together) tensorflow, gradient, and .net.
For support email contact@losttech.software .

More information in NuGet package release notes and on the project web page: https://github.com/losttech/Gradient .

TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
.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 net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
MonoAndroid monoandroid was computed. 
MonoMac monomac was computed. 
MonoTouch monotouch was computed. 
Tizen tizen40 was computed.  tizen60 was computed. 
Xamarin.iOS xamarinios was computed. 
Xamarin.Mac xamarinmac was computed. 
Xamarin.TVOS xamarintvos was computed. 
Xamarin.WatchOS xamarinwatchos was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (4)

Showing the top 4 NuGet packages that depend on LostTech.TensorFlow:

Package Downloads
LostTech.Gradient.License.Azure

Enables LostTech.TensorFlow licensing via Azure Subscription
LostTech.TensorFlow.Siren

SIREN neural network (https://vsitzmann.github.io/siren/). Network with sine activations, perfect for neural representations of various signals: images, video, audio. SOTA 2020. See project page for usage example.
LostTech.TensorFlow.YOLOv4

Real-Time Object Detection network. TensorFlow-based implementation with support for fine-tuning and training from scratch.
LostTech.TensorFlow.GPT

GPT-2 neural network (https://openai.com/blog/better-language-models/). GPT-2 translates text, answers questions, summarizes passages, and generates text output on a level is sometimes indistinguishable from that of humans. A .NET port of open-source artificial intelligence created by OpenAI.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
2.5.0-rc1 6,745 9/27/2022
2.5.0-preview1 3,465 5/17/2021
2.4.1-preview1 2,457 5/4/2021
1.15.0 8,884 12/29/2020
1.15.0-RC1 4,890 8/26/2020

This version requires Python 3.x x64 to be installed with tensorflow or tensorflow-cpu. See the official installation instructions in https://www.tensorflow.org/install/ (ensure you are installing version 2.5.x to avoid hard-to-debug issues).

If your app, that uses LostTech.TensorFlow, targets net4xx (like net472), you need to specify a proper runtime identifier to run and publish. e.g. "dotnet publish -r win" and "dotnet run -r win7-x64". Note, "run" requires specific identifier.

What's new

TensorFlow 2:
- please see https://www.tensorflow.org/guide/effective_tf2#a_brief_summary_of_major_changes

1.15.0 (Release):
- enhanced Tensor<T>
- added licensing via Azure Subscription (must be admin of subscription): https://portal.azure.com/#create/losttechllc.tensorflow
- fixed off-by-one errors in passing `Index` and `Range` counting from the end
- support more NumPy functions
- added `PythonSlice`
- added .NET types for `ArithmeticError`, `FloatingPointError`, `OverflowError`, and `ZeroDivisionError`
- fixed a few GIL-related crashes

RC1:
- replaced expiration with licenses
- improved typing on many APIs
- fixed inability to access static settings
- strongly-typed wrappers for Tensor
- enhanced ndarray<T>
- improved exception handling and debugging
- core runtime components include source and debug symbols
- LINQ enumerables and 1D .NET arrays are now automatically converted to Python lists for compatibility with bad TensorFlow APIs (can be disabled)

Preview 7:
- TensorFlow 1.15
- strongly-typed accessors for ndarray<T>
- arithmetic, bitwise and comparison operators on Tensors (note, now to check for null `is null` must be used instead of `== null`)
- StartUsing extension on classes like Session, variable_scope, etc to allow simpler `using` blocks in .NET
- improved support for enums
- prepackaged TensorFlow runtime in a NuGet package for easy installation (separate): LostTech.TensorFlow.Python
- minimal wrapper for NumPy is released in a separate package (see dependencies)
- runtime initialization moved to Gradient.Runtime
- bugfixes: https://github.com/losttech/Gradient/milestone/3 + internally reported bugs
- new sample: reinforcement learning with Unity ML agents (see https://github.com/losttech/Gradient-Samples/ after 2020/02/10)

Preview 6:
- feature: enabled inheriting from TensorFlow classes. Now it is possible to build custom Keras layers, callbacks, etc
- feature: automatic marshalling of Gradient types for use with TensorFlow
- fixed an ability to modify collections belonging to TensorFlow objects
- fixed crash when enumerating TensorFlow collections without an explicit lock

Preview 5.1:
- improved passing dictionaries
- setup: optionally specify Conda environment via an environment variable
- setup: fixed Conda environment autodectection on Linux
- improved argument types in many places
- Gradient warnings are now printed to Console.Error by default, instead of Console.Out
- fixed crashes on dynamic interop and multithreaded enumeration
- fixed some properties not being exposed https://github.com/losttech/Gradient/issues/4

Preview 5:
- support for indexing Tensor objects via `dynamic`
- allow using specific Python environment via GradientSetup.UsePythonEnvironment

Preview 4:
- support for Ubuntu 18.04 x64 and MacOS with .NET Core; other *nix OS might work too


Preview 2:
- dynamically typed overloads, that enable fallback for tricky signatures
- a common interface for tf.Variable and tf.Tensor
- enabled enumeration over TensorFlow collection types