dotnethero/bittensor

Tensor computation library for .NET

BitTensor - Tensor computation library for .NET

BitTensor is a high-performance, easy-to-use tensor library designed for machine learning applications. It provides a comprehensive set of operations, including arithmetic operations, matrix manipulations, and automatic differentiation, making it ideal for building and training neural networks.

Features

• High-Performance Tensor Operations: Utilize unsafe code for critical sections to enhance performance.
• Automatic Differentiation: Support for gradients computation for backpropagation.
• Model Building Framework: Easily define and stack neural network layers with Model and SequentialModel.
• Support for Broadcasting and Aggregation: Perform operations on tensors of different shapes efficiently.
• Customizable: Define complex operations and custom gradients with support for custom forward and backward functions.

Installation

Currently, BitTensor is available as a source code repository. Clone the repository to get started:

git clone https://github.com/yourusername/BitTensor.git

Quick Start

Here's a quick example to get you started with BitTensor:

using BitTensor.Core;
using BitTensor.Units;

var linearLayer = new LinearLayer(inputs: 10, outputs: 5, activation: Tensor.Sigmoid);
var input = Tensor.Random.Uniform([1, 10]);
var output = linearLayer.Compute(input);

Console.WriteLine($"Output: {output}");

Examples

Building a Simple Model

var model = Model.Sequential(
[
    new LinearLayer(inputs: 784, outputs: 128, activation: Tensor.ReLU),
    new LinearLayer(inputs: 128, outputs: 10, activation: Tensor.Softmax)
]);

var input = Tensor.Random.Uniform([1, 784]);
var output = model.Compute(input);

Console.WriteLine($"Model output: {output}");

Training the Model

Refer to the Fit method in the Model class for examples on how to train the model using the provided dataset.

License

BitTensor is licensed under the MIT License - see the LICENSE file for details.