Found 384 packages
Tensor (n-dimensional array) library for F# Core features: - n-dimensional arrays (tensors) in host memory or on CUDA GPUs - element-wise operations (addition, multiplication, absolute value, etc.) - basic linear algebra operations (dot product, SVD decomposition, matrix inverse, etc.) - reduction operations (sum, product, average, maximum, arg max, etc.) - logic operations (comparision, and, or, etc.) - views, slicing, reshaping, broadcasting (similar to NumPy) - scatter and gather by indices - standard functional operations (map, fold, etc.) Data exchange: - read/write support for HDF5 (.h5) - interop with standard F# types (Seq, List, Array, Array2D, Array3D, etc.) Performance: - host: SIMD and BLAS accelerated operations - by default Intel MKL is used (shipped with NuGet package) - other BLASes (OpenBLAS, vendor-specific) can be selected by configuration option - CUDA GPU: all operations performed locally on GPU and cuBLAS used for matrix operations Requirements: - Linux, MacOS or Windows on x64 - Linux requires libgomp.so.1 installed. Additional algorithms are provided in the Tensor.Algorithm package.
Google's TensorFlow full binding in .NET Standard. Building, training and infering deep learning models. https://tensorflownet.readthedocs.io
Microsoft.ML.TensorFlow contains ML.NET integration of TensorFlow.
SciSharp.TensorFlow.Redist is a package contains Google TensorFlow C library CPU version 2.16.0 redistributed as a NuGet package.
.NET Bindings for TensorFlow
The only .NET tensor & matrix library for generic types. It is also faster than other generic-typed matrix libraries.
SciSharp.TensorFlow.Redist-Windows-GPU contains the Google TensorFlow C library GPU version 2.10.3 redistributed as a NuGet package.
Microsoft.ML.TensorFlow.Redist contains the TensorFlow C library version 1.13.1 redistributed as a NuGet package.
Keras for .NET Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.
SciSharp.TensorFlow.Redist-Linux-GPU contains the Google TensorFlow C library GPU version 2.11.1 redistributed as a NuGet package.
FULL TensorFlow 1.15 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 and tf.contrib APIs, estimators and many more. 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 1.15.x. See the official installation instructions in https://www.tensorflow.org/install/pip#older-versions-of-tensorflow (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 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.
.NET for Android and MAUI bindings for the Android Java library 'org.tensorflow:tensorflow-lite'. Library description: A library helps deploy machine learning models on mobile devices
Provides support for operating over tensors.
.NET Core 2.0 port of the TensorFlow C# Library
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
Data types: - arbitrary precision rational numbers Matrix algebra (integer, rational): - Row echelon form - Smith normal form - Kernel, cokernel and (pseudo-)inverse Matrix decomposition (floating point): - Principal component analysis (PCA) - ZCA whitening Misc: - Bezout's identity - Loading of NumPy's .npy and .npz files.
.NET for Android and MAUI bindings for the Android Java library 'org.tensorflow:tensorflow-lite-api'. Library description: A library helps deploy machine learning models on mobile devices
.NET for Android and MAUI bindings for the Android Java library 'org.tensorflow:tensorflow-lite-gpu'. Library description: A library helps deploy machine learning models on mobile devices
A lightweight and high-performance tensor library which provides numpy-like operations but .NET style interfaces. It supports generic tensor, Linq, C# native slices and so on. It is young so that it may may lack some features or have some BUGs. Please tell us on github or via email, thank you! Github repository: https://github.com/AsakusaRinne/Tensor.NET Email: AsakusaRinne@gmail.com Update information: 1. Add static method for load and save. 2. Add mod, and, or, xor operators for Tensor. 3. Add ForEach method for Tensor. 4. Add docs for public APIs. Corresponding commit ID for this version: 03c0ceda1692dbcf40f6bded70330bd5bba04c54