FULL TensorFlow 1.15 for .NET with Keras. Build, train, checkpoint, execute models.
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 and tf.contrib APIs, including estimators.
This preview will expire.
This version requires Python 3.x x64 to be installed with tensorflow or tensorflow-gpu 1.15. See the official installation instructions in https://www.tensorflow.org/install/ (ensure you are installing version 1.15 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 on-site/remote support for this preview email firstname.lastname@example.org .
More information in NuGet package release notes and at the project web page: https://github.com/losttech/Gradient .
Install-Package Gradient -Version 0.15.7.2
dotnet add package Gradient --version 0.15.7.2
<PackageReference Include="Gradient" Version="0.15.7.2" />
paket add Gradient --version 0.15.7.2
This version requires Python 3.x x64 to be installed with tensorflow or tensorflow-gpu. See the official installation instructions in https://www.tensorflow.org/install/ (ensure you are installing version 1.15 to avoid hard-to-debug issues).
If your app, that uses Gradient, 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.
- 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)
- 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 6 will expire in March 2020
- 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 5.1 will expire in Oct 2019
- support for indexing Tensor objects via `dynamic`
- allow using specific Python environment via GradientSetup.UsePythonEnvironment
- support for Ubuntu 18.04 x64 and MacOS with .NET Core; other *nix OS might work too
- dynamically typed overloads, that enable fallback for tricky signatures
- a common interface for tf.Variable and tf.Tensor
- enabled enumeration over TensorFlow collection types
This package is not used by any popular GitHub repositories.