github地址
主要不要直接pip install安装,会由于依赖无法安装而失败
点击here手动安装依赖
选择对应的pytorch版本,我的是Win10 Python3.8.3+Pytorch1.8.1+CUDA10.2
手动下载四个依赖包本地安装:
主要不要直接:pip install torch_geometric
这样会安装最新的torch_geometric,后面在使用时候会出现Pytorch AttributeError: module 'torch' has no attribute 'sparse_scs'
这里手动指定低版本安装:
pip install torch_geometric==2.0.4
接下来就可以跑一个图卷积神经网络试试:
import torch
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.datasets import KarateClub
from torch_geometric.utils import to_networkx
dataset = KarateClub()
def visualize_graph(G, color):
plt.figure(figsize=(7,7))
plt.xticks([])
plt.yticks([])
nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,
node_color=color, cmap="Set2")
plt.show()
def visualize_embedding(h, color, epoch=None, loss=None):
plt.figure(figsize=(7,7))
plt.xticks([])
plt.yticks([])
h = h.detach().cpu().numpy()
plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap="Set2")
if epoch is not None and loss is not None:
plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)
plt.show()
G = to_networkx(data, to_undirected=True)
visualize_graph(G, color=data.y)
import torch
from torch.nn import Linear
from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):
def __init__(self):
super().__init__()
torch.manual_seed(1234)
self.conv1 = GCNConv(dataset.num_features, 4) # 只需定义好输入特征和输出特征即可
self.conv2 = GCNConv(4, 4)
self.conv3 = GCNConv(4, 2)
self.classifier = Linear(2, dataset.num_classes)
def forward(self, x, edge_index):
h = self.conv1(x, edge_index) # 输入特征与邻接矩阵(注意格式,上面那种)
h = h.tanh()
h = self.conv2(h, edge_index)
h = h.tanh()
h = self.conv3(h, edge_index)
h = h.tanh()
# 分类层
out = self.classifier(h)
return out, h
model = GCN()
_, h = model(data.x, data.edge_index)
print(f'Embedding shape: {list(h.shape)}')
visualize_embedding(h, color=data.y)
Embedding shape: [34, 2]
训练模型
import time
model = GCN()
criterion = torch.nn.CrossEntropyLoss() # Define loss criterion.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # Define optimizer.
def train(data):
optimizer.zero_grad()
out, h = model(data.x, data.edge_index) #h是两维向量,主要是为了咱们画个图
loss = criterion(out[data.train_mask], data.y[data.train_mask]) # semi-supervised
loss.backward()
optimizer.step()
return loss, h
for epoch in range(401):
loss, h = train(data)
if epoch % 10 == 0:
visualize_embedding(h, color=data.y, epoch=epoch, loss=loss)
time.sleep(0.3)