当前位置:网站首页>The first simple case of GNN: Cora classification
The first simple case of GNN: Cora classification
2022-07-06 11:59:00 【Want to be a kite】
GNN–Cora classification
Cora The dataset is GNN A classic dataset in , take 2708 The papers are divided into seven categories :1) Based on the case 、2) Genetic algorithm (ga) 、3) neural network 、4) Probability method 、5)、 Reinforcement learning 、6) Rule learning 、7) theory . Each paper is regarded as a node , Each node has 1433 Features .
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:\Python\python_dataset\GNN_Dataset\Cora',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])
The code gives GCN、GAT、SGConv、ChebConv、SAGEConv Simple implementation of
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:\Python\python_dataset\GNN_Dataset\Cora',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
边栏推荐
- 分布式节点免密登录
- MP3mini播放模块arduino<DFRobotDFPlayerMini.h>函数详解
- MySQL realizes read-write separation
- Analysis of charging architecture of glory magic 3pro
- nodejs连接Mysql
- Using LinkedHashMap to realize the caching of an LRU algorithm
- Togglebutton realizes the effect of switching lights
- SQL time injection
- Keyword inline (inline function) usage analysis [C language]
- JS object and event learning notes
猜你喜欢
MongoDB
sklearn之feature_extraction.text.CountVectorizer / TfidVectorizer
STM32型号与Contex m对应关系
Pytoch Foundation
Kaggle竞赛-Two Sigma Connect: Rental Listing Inquiries(XGBoost)
Comparaison des solutions pour la plate - forme mobile Qualcomm & MTK & Kirin USB 3.0
Linux Yum install MySQL
Connexion sans mot de passe du noeud distribué
[template] KMP string matching
Mysql database interview questions
随机推荐
[Bluebridge cup 2021 preliminary] weight weighing
Password free login of distributed nodes
4. Install and deploy spark (spark on Yan mode)
nodejs连接Mysql
Reno7 60W super flash charging architecture
arduino获取数组的长度
Yarn installation and use
C语言回调函数【C语言】
Word排版(小計)
XML文件详解:XML是什么、XML配置文件、XML数据文件、XML文件解析教程
Contiki源码+原理+功能+编程+移植+驱动+网络(转)
共用体(union)详解【C语言】
[Bluebridge cup 2020 preliminary] horizontal segmentation
MongoDB
MySQL主从复制的原理以及实现
[yarn] yarn container log cleaning
Detailed explanation of Union [C language]
Wangeditor rich text component - copy available
机器学习--决策树(sklearn)
ToggleButton实现一个开关灯的效果