图模型训练

news2024/12/27 10:56:04

一、依赖安装

网址：pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch (github.com)

找到此处，点击here进入依赖安装界面

找到自己安装的torch版本并点击，，进入安装依赖

二、用库自带的数据集

代码：

import torch
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.utils import to_networkx


def visualiize_graph(G,color):
    plt.figure(figsize=(7, 7))
    plt.xticks([])
    plt.yticks([])
    nx.draw_networkx(G, pos=nx.spring_layout(G,seed = 42), node_color=color, cmap = "Set2", with_labels=False)

    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], c=color, cmap = "Set2", s = 140)
    if epoch is not None and loss is not None:
        plt.xlabel("Epoch: {}, Loss: {:.4f}".format(epoch, loss))
    plt.show()


from torch_geometric.datasets import KarateClub

dataset = KarateClub()

data = dataset[0]
print(data)
#展示点之间的关系
edge_index = data.edge_index
print(edge_index.t())

#可视化
G = to_networkx(data, to_undirected=True)
visualiize_graph(G,data.y)

查看数据集格式

Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])

数据可视化

三、模型网络搭建

import torch
from torch.nn import Linear
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import KarateClub

from test import visualize_embedding

dataset = KarateClub()
data = dataset[0]

class GCN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        torch.manual_seed(1234)
        self.conv1 = GCNConv(dataset.num_features, 4)
        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)
        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()
# print(model)

_,h = model(data.x, data.edge_index)
print(f'Hidden state size: {h.size()}')

visualize_embedding(h,color = data.y)

四、模型训练

代码：

import time
import torch.nn
from model import GCN
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.datasets import KarateClub

model = GCN()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

def visualiize_graph(G,color):
    plt.figure(figsize=(7, 7))
    plt.xticks([])
    plt.yticks([])
    nx.draw_networkx(G, pos=nx.spring_layout(G,seed = 42), node_color=color, cmap = "Set2", with_labels=False)

    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], c=color, cmap = "Set2", s = 140)
    if epoch is not None and loss is not None:
        plt.xlabel("Epoch: {}, Loss: {:.4f}".format(epoch, loss))
    plt.show()

def train(data):
    optimizer.zero_grad()
    out,h = model(data.x,data.edge_index)
    loss = criterion(out[data.train_mask],data.y[data.train_mask])
    loss.backward()
    optimizer.step()
    return loss,h

dataset = KarateClub()
data = dataset[0]

for epoch in range(101):
    loss,h = train(data)
    if epoch % 10 == 0:
        visualize_embedding(h,color = data.y, epoch = epoch, loss = loss)
        time.sleep(0.3)

结果：