cleanup

Author: CarloLucibello

Project.toml

@@ -15,7 +15,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
-PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -26,6 +25,7 @@ ChainRulesCore = "1"
 DataStructures = "0.18"
 Flux = "0.12"
 KrylovKit = "0.5"
+LearnBase = "0.5"
 LightGraphs = "1.3"
 MacroTools = "0.5"
 NNlib = "0.7"

src/gnngraph.jl

@@ -79,11 +79,11 @@ g = GNNGraph(s, t)
 # From a LightGraphs' graph
 g = GNNGraph(erdos_renyi(100, 20))
 
-# Add node 2 node feature arrays
+# Add 2 node feature arrays
 g = GNNGraph(g, ndata = (X = rand(100, g.num_nodes), y = rand(g.num_nodes)))
 
 # Add node features and edge features with default names `X` and `E` 
-g = GNNGraph(g, ndata = rand(100, g.num_nodes), edata = rand(16, g.num_nodes))
+g = GNNGraph(g, ndata = rand(100, g.num_nodes), edata = rand(16, g.num_edges))
 
 g.ndata.X
 g.ndata.E