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https://github.com/fluxml/geometricflux.jl
Geometric Deep Learning for Flux
https://github.com/fluxml/geometricflux.jl
deep-learning flux geometric-deep-learning graph-neural-networks juliagraphs machine-learning
Last synced: about 2 months ago
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Geometric Deep Learning for Flux
- Host: GitHub
- URL: https://github.com/fluxml/geometricflux.jl
- Owner: FluxML
- License: mit
- Created: 2019-04-04T12:18:22.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-29T01:03:20.000Z (about 1 year ago)
- Last Synced: 2024-08-02T20:04:29.414Z (5 months ago)
- Topics: deep-learning, flux, geometric-deep-learning, graph-neural-networks, juliagraphs, machine-learning
- Language: Julia
- Homepage: https://fluxml.ai/GeometricFlux.jl/stable/
- Size: 17.6 MB
- Stars: 349
- Watchers: 8
- Forks: 29
- Open Issues: 55
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# GeometricFlux.jl
[](https://fluxml.ai/GeometricFlux.jl/stable)
[](https://fluxml.ai/GeometricFlux.jl/dev)
[](https://codecov.io/gh/FluxML/GeometricFlux.jl)
GeometricFlux is a geometric deep learning library for [Flux](https://github.com/FluxML/Flux.jl). This library aims to be compatible with packages from [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem and have support of CUDA GPU acceleration with [CUDA](https://github.com/JuliaGPU/CUDA.jl). Message passing scheme is implemented as a flexbile framework and fused with Graph Network block scheme. GeometricFlux is compatible with other packages that are composable with Flux.
Suggestions, issues and pull requsts are welcome.
## Installation
```julia
]add GeometricFlux
```
## Features
* Extending Flux deep learning framework in Julia and seamlessly integration with regular Flux layers.
* Support of CUDA GPU with CUDA.jl and mini-batched training leveraging advantages of GPU
* Integration with existing JuliaGraphs ecosystem
* Support Message-passing and graph network architectures
* Support of static graph and variable graph strategy. Variable graph strategy is useful when training the model over diverse graph structures.
* Integration of GNN benchmark datasets
* Support dynamic graph update towards manifold learning 2.0
### Featured Graphs
GeometricFlux handles graph data (the topology plus node/vertex/graph features)
thanks to `FeaturedGraph` type.
A `FeaturedGraph` can be constructed from various graph structures, including
adjacency matrices, adjacency lists, Graphs' types...
```julia
fg = FeaturedGraph(adj_list)
```
### Graph convolutional layers
Construct a GCN layer:
```julia
GCNConv(input_dim => output_dim, relu)
```
## Use it as you use Flux
```julia
model = Chain(
WithGraph(fg, GCNConv(fg, 1024 => 512, relu)),
Dropout(0.5),
WithGraph(fg, GCNConv(fg, 512 => 128)),
Dense(128, 10)
)
## Loss
loss(x, y) = logitcrossentropy(model(x), y)
accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))
## Training
ps = Flux.params(model)
train_data = [(train_X, train_y)]
opt = ADAM(0.01)
evalcb() = @show(accuracy(train_X, train_y))
Flux.train!(loss, ps, train_data, opt, cb=throttle(evalcb, 10))
```
## Roadmap
To achieve geometric deep learning raised by Bronstein *et al*, 5G fields of deep learning models will be supported in GeometricFlux.jl. For details, you could check the [geometric deep learning official website](https://geometricdeeplearning.com/).
5(+1)G including the following fields:
* **Graphs** and Sets
* including classical GNN models and networks over sets.
* Transformer models are regard as a kind of GNN with complete graph, and you can check [chengchingwen/Transformers.jl](https://github.com/chengchingwen/Transformers.jl) for more details.
* **Grids** and Euclidean spaces
* including classical convolutional neural networks, multi-layer perceptrons etc.
* for operators over functional spaces of regular grid, you can check [SciML/NeuralOperators.jl](https://github.com/SciML/NeuralOperators.jl) for more details.
* **Groups** and Homogeneous spaces
* including a series of equivariant/invariant models.
* **Geodesics** and Manifolds
* **Gauges** and Bundles
* **Geometric algebra**
## Discussions
It's welcome to have direct discussions in #graphnet channel or in #flux-bridged channel on slack. For usage issues, it's welcome to post your minimal working examples (MWE) on [Julia discourse](https://discourse.julialang.org/) and then tag maintainer `@yuehhua`.