Cora数据集是GNN中一个经典的数据集，将2708篇论文分为七类：1）基于案例、2）遗传算法、3）神经网络、4）概率方法、5）、强化学习、6）规则学习、7）理论。每一篇论文看作是一个节点，每个节点有1433个特征。
import os
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.datasets import Planetoid
import torch_geometric.nn as pyg_nn
#load Cora dataset
def get_data(root_dir='D:Pythonpython_datasetGNN_DatasetCora',data_name='Cora'):
Cora_dataset = Planetoid(root=root_dir,name=data_name)
print(Cora_dataset)
return Cora_dataset
Cora_dataset = get_data()
print(Cora_dataset.num_classes,Cora_dataset.num_node_features,Cora_dataset.num_edge_features)
print(Cora_dataset.data)
Cora() 7 1433 0 Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])代码中给出GCN、GAT、SGConv、ChebConv、SAGEConv的简单实现
import os
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.datasets import Planetoid
import torch_geometric.nn as pyg_nn
#load Cora dataset
def get_data(root_dir='D:Pythonpython_datasetGNN_DatasetCora',data_name='Cora'):
Cora_dataset = Planetoid(root=root_dir,name=data_name)
print(Cora_dataset)
return Cora_dataset
#create the Graph cnn model
"""
2-GATConv
"""
# class GATConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(GATConv,self).__init__()
# self.GATConv1 = pyg_nn.GATConv(in_channels=in_c,out_channels=hid_c)
# self.GATConv2 = pyg_nn.GATConv(in_channels=hid_c, out_channels=hid_c)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.GATConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.GATConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
"""
2-SAGE 0.788
"""
# class SAGEConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(SAGEConv,self).__init__()
# self.SAGEConv1 = pyg_nn.SAGEConv(in_channels=in_c,out_channels=hid_c)
# self.SAGEConv2 = pyg_nn.SAGEConv(in_channels=hid_c, out_channels=hid_c)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.SAGEConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.SAGEConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
"""
2-SGConv 0.79
"""
class SGConv(nn.Module):
def __init__(self,in_c,hid_c,out_c):
super(SGConv,self).__init__()
self.SGConv1 = pyg_nn.SGConv(in_channels=in_c,out_channels=hid_c)
self.SGConv2 = pyg_nn.SGConv(in_channels=hid_c, out_channels=hid_c)
def forward(self,data):
x = data.x
edge_index = data.edge_index
hid = self.SGConv1(x=x,edge_index=edge_index)
hid = F.relu(hid)
out = self.SGConv2(hid,edge_index=edge_index)
out = F.log_softmax(out,dim=1)
return out
"""
2-ChebConv
"""
# class ChebConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(ChebConv,self).__init__()
#
# self.ChebConv1 = pyg_nn.ChebConv(in_channels=in_c,out_channels=hid_c,K=1)
# self.ChebConv2 = pyg_nn.ChebConv(in_channels=hid_c,out_channels=out_c,K=1)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.ChebConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.ChebConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
"""
2-GCN
"""
# class GraphCNN(nn.Module):
# def __init__(self, in_c,hid_c,out_c):
# super(GraphCNN,self).__init__()
#
# self.conv1 = pyg_nn.GCNConv(in_channels=in_c,out_channels=hid_c)
# self.conv2 = pyg_nn.GCNConv(in_channels=hid_c,out_channels=out_c)
#
# def forward(self,data):
# #data.x,data.edge_index
# x = data.x # [N,C]
# edge_index = data.edge_index # [2,E]
# hid = self.conv1(x=x,edge_index=edge_index) #[N,D]
# hid = F.relu(hid)
#
# out = self.conv2(hid,edge_index=edge_index) # [N,out_c]
#
# out = F.log_softmax(out,dim=1)
#
# return out
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
Cora_dataset = get_data()
#my_net = GATConv(in_c=Cora_dataset.num_node_features, hid_c=100, out_c=Cora_dataset.num_classes)
#my_net = SAGEConv(in_c=Cora_dataset.num_node_features, hid_c=40, out_c=Cora_dataset.num_classes)
my_net = SGConv(in_c=Cora_dataset.num_node_features,hid_c=100,out_c=Cora_dataset.num_classes)
#my_net = ChebConv(in_c=Cora_dataset.num_node_features,hid_c=20,out_c=Cora_dataset.num_classes)
# my_net = GraphCNN(in_c=Cora_dataset.num_node_features,hid_c=12,out_c=Cora_dataset.num_classes)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
my_net = my_net.to(device)
data = Cora_dataset[0].to(device)
optimizer = torch.optim.Adam(my_net.parameters(),lr=1e-3)
#model train
my_net.train()
for epoch in range(500):
optimizer.zero_grad()
output = my_net(data)
loss = F.nll_loss(output[data.train_mask],data.y[data.train_mask])
loss.backward()
optimizer.step()
print("Epoch",epoch+1,"Loss",loss.item())
#model test
my_net.eval()
_,prediction = my_net(data).max(dim=1)
target = data.y
test_correct = prediction[data.test_mask].eq(target[data.test_mask]).sum().item()
test_number = data.test_mask.sum().item()
print("Accuracy of Test Sample:",test_correct/test_number)
if __name__ == '__main__':
main()
Cora() Epoch 1 Loss 4.600048542022705 Epoch 2 Loss 4.569146156311035 Epoch 3 Loss 4.535804271697998 Epoch 4 Loss 4.498434543609619 Epoch 5 Loss 4.456351280212402 Epoch 6 Loss 4.409425258636475 Epoch 7 Loss 4.357522964477539 Epoch 8 Loss 4.3007612228393555 Epoch 9 Loss 4.2392096519470215 Epoch 10 Loss 4.172731876373291 Epoch 11 Loss 4.101400375366211 Epoch 12 Loss 4.025243282318115 ............... Epoch 494 Loss 0.004426263272762299 Epoch 495 Loss 0.004407935775816441 Epoch 496 Loss 0.004389731213450432 Epoch 497 Loss 0.004371633753180504 Epoch 498 Loss 0.004353662021458149 Epoch 499 Loss 0.0043357922695577145 Epoch 500 Loss 0.004318032879382372 Accuracy of Test Sample: 0.794
