43 packages tagged with “tensor”
The only .NET tensor & matrix library for generic types. It is also faster than other generic-typed matrix libraries.
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.
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/
.NET Core 2.0 port of the TensorFlow C# Library
A tensor libaray based on CPU. It includes many operations and can be used for neural networks
Torch7-style tensors and operations on them for C#. All operations are implemented in native code, even the CPU-only [no GPU support] version is extremely fast.
A tensor libaray based on CUDA. It includes many operations and can be used for neural networks
RTLite for Microcontrollers for use with Wilderness Labs Meadow
DotTorch Core is the foundational .NET 8/9 library for high-performance tensor computations with full support for automatic differentiation (autograd), broadcasting, and computational graph tracing. Ideal for deep learning and scientific computing applications.
An extended version of the Array to accelerate operation, easy to use, multi dimensional. With SuperchargedArray.Accelerated namespace you will unlock SIMD potential to run the Array operation on any hardware like Intel CPU/GPU, NVIDIA, AMD etc.
The core code of the Owl framework
This library, based on .NET generic math, provides methods for performing mathematical operations over spans of value types. These operations can be accelerated using SIMD operations supported by the CPU where available.
The GH types wrappers fro the Owl framework
Class library implementing advanced mathematical algorithms, transforms, and time series manipulations. Implementations favour simplicity and correctness.
DotTorch Losses is the dedicated .NET 8/9 library providing a comprehensive set of loss functions for deep learning and machine learning tasks. This package integrates seamlessly with DotTorch.Core, enabling robust automatic differentiation and efficient tensor operations. The initial 9.0.0 release introduces key loss primitives such as MSE, Cross-Entropy, Binary Cross-Entropy, Huber, KL Divergence, NLL, and Hinge Loss with full support for broadcasting and reduction options.
Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It can be used for sequence-to-sequence task, sequence-labeling task and sequence-classification task and other NLP tasks. Seq2SeqSharp supports both CPUs (x86, x64 and ARM64) and GPUs. It's powered by .NET core, so Seq2SeqSharp can run on both Windows and Linux without any modification and recompilation.
Torch.NET brings the awesome Python package PyTorch to the .NET world. PyTorch offers Tensor computations and more with efficient GPU or multi-core CPU processing support and is to be considered one of the fundamental libraries for scientific computing, machine learning and AI in Python. Torch.NET empowers .NET developers to leverage PyTorch's extensive functionality including computational graphs with with multi-dimensional arrays, back-propagation, neural network implementations and many more via a compatible strong-typed API.
A Bonsai package for TorchSharp tensor manipulations.
ddddocr 的 C# 版本, 使用common_old.onnx制作, 受模型限制, 建议OCR图片的宽度为64整数倍
A Bonsai package for TorchSharp tensor manipulations. This package combines the Bonsai.ML.Torch package with LibTorch 2.5.1 CPU support.
Minimal reader for modern PyTorch zipfile checkpoints (.pt)
Multi-dimensional Chebyshev tensor interpolation with analytical derivatives for .NET