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https://github.com/denizyuret/knet.jl

Koç University deep learning framework.
https://github.com/denizyuret/knet.jl

data-science deep-learning julia knet machine-learning neural-networks

Last synced: about 1 month ago
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Koç University deep learning framework.

Host: GitHub
URL: https://github.com/denizyuret/knet.jl
Owner: denizyuret
License: other
Created: 2015-09-29T23:42:37.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2022-06-08T18:14:22.000Z (over 2 years ago)
Last Synced: 2024-10-15T04:01:38.378Z (about 1 month ago)
Topics: data-science, deep-learning, julia, knet, machine-learning, neural-networks
Language: Jupyter Notebook
Homepage: https://denizyuret.github.io/Knet.jl/latest
Size: 23 MB
Stars: 1,428
Watchers: 81
Forks: 230
Open Issues: 152
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
- License: LICENSE.md

Awesome Lists containing this project

README

        # Knet

[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://denizyuret.github.io/Knet.jl/latest) 

[![](https://travis-ci.org/denizyuret/Knet.jl.svg?branch=master)](https://travis-ci.org/denizyuret/Knet.jl) 

[![](https://coveralls.io/repos/github/denizyuret/Knet.jl/badge.svg?branch=master)](https://coveralls.io/github/denizyuret/Knet.jl?branch=master)

[![](https://codecov.io/gh/denizyuret/Knet.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/denizyuret/Knet.jl)

[Knet](https://denizyuret.github.io/Knet.jl/latest) (pronounced "kay-net") is the [Koç

University](http://www.ku.edu.tr/en) deep learning framework implemented in

[Julia](http://docs.julialang.org) by [Deniz Yuret](http://www.denizyuret.com) and

collaborators.  It supports GPU operation and automatic differentiation using dynamic

computational graphs for models defined in plain Julia. You can install Knet with the 

following at the julia prompt: `using Pkg; Pkg.add("Knet")`. Some starting points:

* [Tutorial:](tutorial) 

  introduces Julia and Knet via examples.

* [Documentation:](https://denizyuret.github.io/Knet.jl/latest)

  installation, introduction, design, implementation, full reference and deep learning chapters.

* [Examples:](examples)

  more tutorials and example models.

* [Benchmarks:](http://denizyuret.github.io/Knet.jl/latest/tutorial/#Benchmarks-1)

  comparison of Knet's speed with TensorFlow, PyTorch, DyNet etc.

* [Paper:](https://goo.gl/zeUBFr)

  Yuret, D. "Knet: beginning deep learning with 100 lines of julia." In *Machine Learning Systems Workshop* at NIPS 2016.

* [KnetML:](https://github.com/KnetML)

  github organization with Knet repos of models, tutorials, layer collections and other resources.

* [Images:](http://denizyuret.github.io/Knet.jl/latest/install/#Using-Amazon-AWS-1)

  Knet machine images are available for [AWS](http://denizyuret.github.io/Knet.jl/latest/install/#Using-Amazon-AWS-1), [Singularity](https://github.com/KnetML/singularity-images) and [Docker](https://github.com/JuliaGPU/docker).

* [Issues:](https://github.com/denizyuret/Knet.jl/issues)

  if you find a bug, please open a github issue.

* [knet-users:](https://groups.google.com/forum/#!forum/knet-users)

  if you need help or would like to request a feature, please join this mailing list.

* [knet-dev:](https://groups.google.com/forum/#!forum/knet-dev)

  if you would like to contribute to Knet development, please join this mailing list and check out these [tips](https://denizyuret.github.io/Knet.jl/latest/install/#Tips-for-developers-1).

* [knet-slack:](https://julialang.slack.com/messages/CDLKQ92P3/details) Slack channel for Knet.

* Related work: Please check out [Flux](https://github.com/FLuxML), [Mocha](https://github.com/pluskid/Mocha.jl), [JuliaML](https://github.com/JuliaML), [JuliaDiff](https://github.com/JuliaDiff), [JuliaGPU](https://github.com/JuliaGPU), [JuliaOpt](https://github.com/JuliaOpt) for related packages.

## Example

Here is a simple example where we define, train and test the

[LeNet](http://yann.lecun.com/exdb/lenet) model for the

[MNIST](http://yann.lecun.com/exdb/mnist) handwritten digit recognition dataset from scratch

using 15 lines of code and 10 seconds of GPU computation.

```julia

# Install packages before first run: using Pkg; pkg"add Knet IterTools MLDatasets"

using Knet, IterTools, MLDatasets

# Define convolutional layer:

struct Conv; w; b; end

Conv(w1,w2,nx,ny) = Conv(param(w1,w2,nx,ny), param0(1,1,ny,1))

(c::Conv)(x) = relu.(pool(conv4(c.w, x) .+ c.b))

# Define dense layer:

struct Dense; w; b; f; end

Dense(i,o; f=identity) = Dense(param(o,i), param0(o), f)

(d::Dense)(x) = d.f.(d.w * mat(x) .+ d.b)

# Define a chain of layers and a loss function:

struct Chain; layers; end

(c::Chain)(x) = (for l in c.layers; x = l(x); end; x)

(c::Chain)(x,y) = nll(c(x),y)

# Load MNIST data:

xtrn,ytrn = MNIST.traindata(Float32); ytrn[ytrn.==0] .= 10

xtst,ytst = MNIST.testdata(Float32);  ytst[ytst.==0] .= 10

dtrn = minibatch(xtrn, ytrn, 100; xsize = (28,28,1,:))

dtst = minibatch(xtst, ytst, 100; xsize = (28,28,1,:))

# Define and train LeNet (~10 secs on a GPU or ~3 mins on a CPU to reach ~99% accuracy)

LeNet = Chain((Conv(5,5,1,20), Conv(5,5,20,50), Dense(800,500,f=relu), Dense(500,10)))

progress!(adam(LeNet, ncycle(dtrn,3)))

accuracy(LeNet,data=dtst)

```

## Contributing

Knet is an open-source project and we are always open to new contributions: bug reports and

fixes, feature requests and contributions, new machine learning models and operators,

inspiring examples, benchmarking results are all welcome. See [Tips for Developers](https://denizyuret.github.io/Knet.jl/latest/install/#Tips-for-developers) for instructions.

Contributors: Can Gümeli, Carlo Lucibello, Ege Onat, Ekin Akyürek, Ekrem Emre Yurdakul, Emre Ünal, Emre Yolcu, Enis Berk, Erenay Dayanık, İlker Kesen, Kai Xu, Meriç Melike Softa, Mike Innes, Onur Kuru, Ozan Arkan Can, Ömer Kırnap, Phuoc Nguyen, Rene Donner, Tim Besard, Zhang Shiwei.