migrate examples

Author: yuehhua

examples/gae.jl

@@ -7,7 +7,6 @@ using JLD2
 using Statistics: mean
 using SparseArrays
 using Graphs.SimpleGraphs
-using Graphs: adjacency_matrix
 using CUDA
 
 @load "data/cora_features.jld2" features
@@ -21,13 +20,13 @@ target_catg = 7
 epochs = 200
 
 ## Preprocessing data
-adj_mat = Matrix{Float32}(adjacency_matrix(g)) |> gpu
+fg = FeaturedGraph(g) |> gpu
 train_X = Matrix{Float32}(features) |> gpu  # dim: num_features * num_nodes
-train_y = adj_mat  # dim: num_nodes * num_nodes
+train_y = fg  # dim: num_nodes * num_nodes
 
 ## Model
-encoder = Chain(GCNConv(adj_mat, num_features=>hidden1, relu),
-                GCNConv(adj_mat, hidden1=>hidden2))
+encoder = Chain(GCNConv(fg, num_features=>hidden1, relu),
+                GCNConv(fg, hidden1=>hidden2))
 model = Chain(GAE(encoder, σ)) |> gpu
 
 ## Loss

examples/gcn.jl

@@ -6,7 +6,6 @@ using JLD2
 using Statistics
 using SparseArrays
 using Graphs.SimpleGraphs
-using Graphs: adjacency_matrix
 using CUDA
 using Random
 
@@ -25,12 +24,12 @@ epochs = 100
 ## Preprocessing data
 train_X = Matrix{Float32}(features) |> gpu  # dim: num_features * num_nodes
 train_y = Matrix{Float32}(labels) |> gpu  # dim: target_catg * num_nodes
-adj_mat = Matrix{Float32}(adjacency_matrix(g)) |> gpu
+fg = FeaturedGraph(g) |> gpu
 
 ## Model
-model = Chain(GCNConv(adj_mat, num_features=>hidden, relu),
+model = Chain(GCNConv(fg, num_features=>hidden, relu),
               Dropout(0.5),
-              GCNConv(adj_mat, hidden=>target_catg),
+              GCNConv(fg, hidden=>target_catg),
               ) |> gpu
 
 ## Loss

examples/gde.jl

@@ -2,7 +2,6 @@ using GeometricFlux, Flux, JLD2, SparseArrays, DiffEqFlux, DifferentialEquations
 using Flux: onehotbatch, onecold, logitcrossentropy, throttle
 using Flux: @epochs
 using Statistics: mean
-using Graphs: adjacency_matrix
 
 # Load the dataset
 @load "data/cora_features.jld2" features
@@ -19,22 +18,22 @@ epochs = 40
 # Preprocess the data and compute adjacency matrix
 train_X = Matrix{Float32}(features)  # dim: num_features * num_nodes
 train_y = Float32.(labels)  # dim: target_catg * num_nodes
-adj_mat = Matrix{Float32}(adjacency_matrix(g))
+fg = FeaturedGraph(g)
 
 # Define the Neural GDE
 diffeqarray_to_array(x) = reshape(cpu(x), size(x)[1:2])
 
 node = NeuralODE(
-    GCNConv(adj_mat, hidden=>hidden),
+    GCNConv(fg, hidden=>hidden),
     (0.f0, 1.f0), Tsit5(), save_everystep = false,
     reltol = 1e-3, abstol = 1e-3, save_start = false
 )
 
-model = Chain(GCNConv(adj_mat, num_features=>hidden, relu),
+model = Chain(GCNConv(fg, num_features=>hidden, relu),
               Dropout(0.5),
               node,
               diffeqarray_to_array,
-              GCNConv(adj_mat, hidden=>target_catg),
+              GCNConv(fg, hidden=>target_catg),
               softmax)
 
 # Loss

examples/gde_gpu.jl

@@ -2,7 +2,6 @@ using GeometricFlux, Flux, JLD2, SparseArrays, DiffEqFlux, DifferentialEquations
 using Flux: onehotbatch, onecold, logitcrossentropy, throttle
 using Flux: @epochs
 using Statistics: mean
-using Graphs: adjacency_matrix
 using CUDA
 
 # Load the dataset
@@ -20,22 +19,22 @@ epochs = 40
 # Preprocess the data and compute adjacency matrix
 train_X = Matrix{Float32}(features) |> gpu  # dim: num_features * num_nodes
 train_y = Float32.(labels) |> gpu  # dim: target_catg * num_nodes
-adj_mat = Matrix{Float32}(adjacency_matrix(g)) |> gpu
+fg = FeaturedGraph(g) |> gpu
 
 # Define the Neural GDE
 diffeqarray_to_array(x) = reshape(gpu(x), size(x)[1:2])
 
 node = NeuralODE(
-    GCNConv(adj_mat, hidden=>hidden) |> gpu,
+    GCNConv(fg, hidden=>hidden) |> gpu,
     (0.f0, 1.f0), Tsit5(), save_everystep = false,
     reltol = 1e-3, abstol = 1e-3, save_start = false
 )
 
-model = Chain(GCNConv(adj_mat, num_features=>hidden, relu),
+model = Chain(GCNConv(fg, num_features=>hidden, relu),
               Dropout(0.5),
               node,
               diffeqarray_to_array,
-              GCNConv(adj_mat, hidden=>target_catg),
+              GCNConv(fg, hidden=>target_catg),
               softmax) |> gpu
 
 # Loss