当前位置: 首页 > news >正文

graph neural architecture search

graph neural architecture search图神经网络架构搜索,是一种自动化技术,其目标是让机器自动为特定的图数据和学习任务,找到最优的图神经网络模型结构,从而取代传统的人工设计模型的过程。

可以理解为用AI来设计AI在图神经网络领域的具体应用。

GraphNAS的核心组成部分

包含三个核心组成部分:

  • 搜索空间
    • 定义机器可以选择的所有可能的模型组件和连接方式
    • 组件级搜索:搜索每个GNN层的最佳操作
    • 架构级搜索:搜索模型的宏观连接结构
  • 搜索策略
    • 决定如何在搜索空间中高效的寻找性能优异的架构
    • 常见策略
      • 强化学习
      • 进化算法
      • 基于梯度的方法
      • 贝叶斯优化
  • 性能评估策略
    • 评估搜索出的每个候选架构的性能

示例

常见的是用于节点分类任务,这里采用强化学习作为搜索策略。

例如基于强化学习的图神经网络架构搜索:

1. 搜索空间定义

search_space = {'layer_type': ['gcn', 'gat', 'sage', 'gin', 'graph'],'hidden_dim': [64, 128, 256],'attention_heads': [1, 2, 4, 8],'aggregation': ['mean', 'max', 'sum', 'lstm'],'activation': ['relu', 'prelu', 'elu', 'tanh'],'skip_connection': [True, False],'dropout_rate': [0.0, 0.1, 0.3, 0.5],'num_layers': [2, 3, 4]  # 网络深度}

2. 控制器设计 RNN

import torch
import torch.nn as nnclass Controller(nn.Module):def __init__(self, vocab_sizes, hidden_dim=100):super(Controller, self).__init__()self.hidden_dim = hidden_dimself.lstm = nn.LSTMCell(hidden_dim, hidden_dim)# 为每个架构决策创建embedding和线性层self.embeddings = nn.ModuleList([nn.Embedding(vocab_size, hidden_dim) for vocab_size in vocab_sizes])self.decoders = nn.ModuleList([nn.Linear(hidden_dim, vocab_size) for vocab_size in vocab_sizes])def forward(self, inputs):hx, cx = torch.zeros(inputs.size(0), self.hidden_dim), \torch.zeros(inputs.size(0), self.hidden_dim)actions = []log_probs = []for i, (emb, dec) in enumerate(zip(self.embeddings, self.decoders)):# 通过LSTM处理hx, cx = self.lstm(inputs, (hx, cx))# 生成动作概率logits = dec(hx)prob = F.softmax(logits, dim=-1)# 采样动作action = torch.multinomial(prob, 1).squeeze()log_prob = F.log_softmax(logits, dim=-1)actions.append(action)log_probs.append(log_prob.gather(1, action.unsqueeze(1)).squeeze())# 准备下一时间步的输入inputs = emb(action)return actions, log_probs

3. 可微分GNN架构实现

import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, SAGEConv, GINConvclass SearchableGNN(nn.Module):def __init__(self, num_features, num_classes, architecture_decision):super(SearchableGNN, self).__init__()self.layers = nn.ModuleList()self.architecture = architecture_decision# 解析架构决策num_layers = architecture_decision['num_layers']layer_types = architecture_decision['layer_type']hidden_dims = architecture_decision['hidden_dim']# 构建GNN层in_dim = num_featuresfor i in range(num_layers):layer_type = layer_types[i]out_dim = hidden_dims[i]if layer_type == 'gcn':layer = GCNConv(in_dim, out_dim)elif layer_type == 'gat':heads = architecture_decision['attention_heads'][i]layer = GATConv(in_dim, out_dim, heads=heads, concat=True)out_dim = out_dim * headselif layer_type == 'sage':layer = SAGEConv(in_dim, out_dim)elif layer_type == 'gin':layer = GINConv(nn.Sequential(nn.Linear(in_dim, out_dim),nn.ReLU(),nn.Linear(out_dim, out_dim)))self.layers.append(layer)in_dim = out_dim# 分类层self.classifier = nn.Linear(in_dim, num_classes)self.dropout = architecture_decision['dropout_rate']def forward(self, x, edge_index):for i, layer in enumerate(self.layers):x = layer(x, edge_index)x = F.relu(x)  # 使用ReLU激活x = F.dropout(x, p=self.dropout, training=self.training)return self.classifier(x)

4. 搜索算法

class GraphNAS:def __init__(self, data, num_features, num_classes):self.data = dataself.num_features = num_featuresself.num_classes = num_classes# 控制器参数:7个决策点vocab_sizes = [len(search_space['layer_type']),  # 层类型选择len(search_space['hidden_dim']),  # 隐藏维度len(search_space['attention_heads']),  # 注意力头数len(search_space['aggregation']),  # 聚合方式len(search_space['activation']),  # 激活函数len(search_space['skip_connection']),  # 跳跃连接len(search_space['dropout_rate'])  # dropout率]self.controller = Controller(vocab_sizes)self.optimizer = torch.optim.Adam(self.controller.parameters(), lr=0.001)def decode_architecture(self, actions):"""将控制器的动作解码为具体的架构参数"""architecture = {}architecture['layer_type'] = [search_space['layer_type'][actions[0]]]architecture['hidden_dim'] = [search_space['hidden_dim'][actions[1]]]architecture['attention_heads'] = [search_space['attention_heads'][actions[2]]]architecture['aggregation'] = search_space['aggregation'][actions[3]]architecture['activation'] = search_space['activation'][actions[4]]architecture['skip_connection'] = search_space['skip_connection'][actions[5]]architecture['dropout_rate'] = search_space['dropout_rate'][actions[6]]architecture['num_layers'] = 2  # 固定为2层简化示例return architecturedef evaluate_architecture(self, architecture):"""评估特定架构的性能"""model = SearchableGNN(self.num_features, self.num_classes, architecture)optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)criterion = nn.CrossEntropyLoss()# 快速训练和验证(实际应用中会训练更多轮次)model.train()for epoch in range(50):  # 简化训练过程optimizer.zero_grad()out = model(self.data.x, self.data.edge_index)loss = criterion(out[self.data.train_mask], self.data.y[self.data.train_mask])loss.backward()optimizer.step()# 在验证集上评估model.eval()with torch.no_grad():logits = model(self.data.x, self.data.edge_index)pred = logits.argmax(dim=1)val_acc = (pred[self.data.val_mask] == self.data.y[self.data.val_mask]).float().mean()return val_acc.item()def search(self, num_episodes=100):"""执行架构搜索"""best_accuracy = 0best_architecture = Nonefor episode in range(num_episodes):# 控制器生成架构inputs = torch.zeros(1, self.controller.hidden_dim)actions, log_probs = self.controller(inputs)# 解码架构architecture = self.decode_architecture([a.item() for a in actions])# 评估架构accuracy = self.evaluate_architecture(architecture)# 强化学习更新:准确性作为奖励reward = accuracybaseline = 0.8  # 简单的基线advantage = reward - baseline# 计算策略梯度policy_loss = []for log_prob in log_probs:policy_loss.append(-log_prob * advantage)policy_loss = torch.stack(policy_loss).sum()# 更新控制器self.optimizer.zero_grad()policy_loss.backward()self.optimizer.step()# 记录最佳架构if accuracy > best_accuracy:best_accuracy = accuracybest_architecture = architectureprint(f'Episode {episode+1}: Accuracy = {accuracy:.4f}, 'f'Best = {best_accuracy:.4f}')return best_architecture, best_accuracy

5. 运行搜索

# 初始化搜索
graph_nas = GraphNAS(data, num_features=1433, num_classes=7)# 开始搜索
best_arch, best_acc = graph_nas.search(num_episodes=50)print("搜索完成!")
print(f"最佳架构: {best_arch}")
print(f"最佳准确率: {best_acc:.4f}")

6. 搜索结构示例

经过搜索后,会发现类似这样的最有架构:

best_architecture = {'layer_type': ['gat', 'gcn'],  # 第一层用GAT,第二层用GCN'hidden_dim': [256, 128],      # 隐藏维度递减'attention_heads': [8, 1],     # 第一层8头注意力'aggregation': 'mean','activation': 'elu','skip_connection': True,'dropout_rate': 0.3,'num_layers': 2
}

效率上搜索过程计算大。

http://www.dtcms.com/a/495123.html

相关文章:

  • HTTP方法GET,HEAD,POST,PUT,PATCH,DELETE,OPTIONS,TRACE,RESTful API设计的核心详解
  • 用CMake 实现U8g2 的 SDL2 模拟环境
  • 企业网站排名提升软件智能优化wordpress 创业
  • 企业网站建设调查问卷网站开发周记30篇
  • 网站模板网站免费建商城网站
  • 安徽感智教育科技有限公司成功加入安徽省物流协会
  • Chart.js 雷达图
  • 百分点科技发布中国首个AI原生GEO产品Generforce,助力品牌决胜AI搜索新时代
  • 微算法科技(MLGO)突破性AI推理控制:一种基于集成学习优化算法的无线传感设备边缘协同推理控制技术
  • 智存跃迁,阿里云存储面向 AI 升级全栈数据存储能力
  • 临淄专业网站优化哪家好g3云推广官网
  • python离线包安装方法总结
  • Docker网络和存储卷
  • REFRAG技术详解:如何通过压缩让RAG处理速度提升30倍
  • C++ stack、queue栈和队列的使用——附加算法题
  • 论文解读--RCBEVDet++:Toward High-accuracy Radar-Camera Fusion 3D Perception Network
  • 网站建设公司 温州百度优化大师
  • Kubernetes:Ingress - Traefik
  • 自然的铁律与理想的迷梦:论阿伦特政治哲学的局限与谬误​​
  • 电商网站创办过程建站员工网站
  • Oracle数据库安全参数优化
  • 亚马逊云代理:利用亚马逊云进行大规模数据分析与处理的最佳实践
  • 生成链接的网站网站超链接用什么
  • 网站英文域名是什么django类似wordpress
  • 本地搭建EXAM-MASTER考试系统
  • 高级运维工程师面试题汇总-【DEVOPS】
  • 东莞浩智网站建设开发wordpress 中国地图
  • 【Go】C++ 转 Go 第(一)天:环境搭建 Windows + VSCode 远程连接 Linux
  • MYSQL学习笔记(个人)(第十五天)
  • 网站登录验证码不正确云端互联网站建设