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。