Folders and files

Name		Name	Last commit message	Last commit date
Latest commit History 332 Commits
.github		.github
Backends		Backends
Docs		Docs
Examples		Examples
Library		Library
SiaNet.Engine		SiaNet.Engine
SiaNet		SiaNet
Tools/docfx		Tools/docfx
docs-backup		docs-backup
docs		docs
.gitattributes		.gitattributes
.gitignore		.gitignore
.travis.yml		.travis.yml
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SiaNet.sln		SiaNet.sln
desktop.ini		desktop.ini
icon_32.PNG		icon_32.PNG
icon_64.PNG		icon_64.PNG

Repository files navigation

Trello is used to track SiaNet devlopment activities. You are welcome to watch any task and track progress. Suggestion will be put on the wishlist and then will be planned out for development

https://trello.com/b/bLbgQLgy/sianet-development

A C# deep learning library

Developing a C# wrapper to help developer easily create and train deep neural network models.

Easy to use library, just focus on research
Multiple backend - CNTK, TensorFlow, MxNet, PyTorch, ArrayFire
CUDA/ OpenCL support for some of the backends
Light weight libray, built with .NET standard 2.0
Code well structured, easy to extend if you would like to extend with new layer, loss, metrics, optimizers, constraints, regularizer

A Basic example

The below is a classification example with Titanic dataset. Able to reach 75% accuracy within 10 epoch.

//Setup Engine. If using TensorSharp then pass SiaNet.Backend.TensorSharp.SiaNetBackend.Instance.//Once other backend is ready you will be able to use CNTK, TensorFlow and MxNet as well.Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance,DeviceType.CPU);vardataset=LoadTrain();//Load train datavartest=LoadTest();//Load test datavar(train,val)=dataset.Split(0.25);//Build modelvarmodel=newSequential();model.EpochEnd+=Model_EpochEnd;model.Add(newDense(128,ActivationType.ReLU));model.Add(newDense(64,ActivationType.ReLU));model.Add(newDense(1,ActivationType.Sigmoid));//Compile with Optimizer, Loss and Metricmodel.Compile(OptimizerType.Adam,LossType.BinaryCrossEntropy,MetricType.BinaryAccurary);// Train for 100 epoch with batch size of 32model.Train(train,100,32,val);varpredictions=model.Predict(test);predictions.Print();