Update to v0.6.1

Author: yuehhua

CHANGELOG.md

@@ -2,6 +2,12 @@
 
 All notable changes to this project will be documented in this file.
 
+## [0.6.1]
+
+- Update to CUDA 1.2 and Flux 0.11
+- Refactor graph-related API
+- Improve learning rate in example
+
 ## [0.6.0]
 
 - Rewrite graph network `GraphNet` and message passing `MessagePassing` framework

Project.toml

@@ -1,7 +1,7 @@
 name = "GeometricFlux"
 uuid = "7e08b658-56d3-11e9-2997-919d5b31e4ea"
 authors = ["Yueh-Hua Tu <a504082002@gmail.com>"]
-version = "0.6.0"
+version = "0.6.1"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

README.md

@@ -23,11 +23,42 @@ Suggestions, issues and pull requsts are welcome.
 
 ## Features
 
-Construct layers from adjacency matrix or graph (maybe extend to other structures).
-Input features (including vertex, edge or graph features) of neural network may not need a structure or type.
-Labels or features for output of classification or regression are part of training data, they may not need a specific structure or type, too.
+* Extend Flux deep learning framework in Julia and compatible with Flux layers.
+* Support of CUDA GPU with CUDA.jl
+* Integrate with existing JuliaGraphs ecosystem
+* Support generic graph neural network architectures
+* Variable graph inputs are supported. You use it when diverse graph structures are prepared as inputs to the same model.
+* Integrate GNN benchmark datasets (WIP)
 
-* **NOTICE**: Scatter operations on CUDA are only supported in v1.3 (due to new feature in CUDAnative v2.8 which only supports julia v1.3). CPU version scatter operations are always available.
+## Graph convolutional layers
+
+Construct GCN layer:
+
+```
+graph = # can be adj_mat, adj_list, simple_graphs...
+GCNConv([graph, ]input_dim=>output_dim, relu)
+```
+
+## Use it as you use Flux
+
+```
+model = Chain(GCNConv(g, 1024=>512, relu),
+              Dropout(0.5),
+              GCNConv(g, 512=>128),
+              Dense(128, 10),
+              softmax)
+## 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))
+```
 
 ## Benchmark