大兴企业网站建设,汕头网站关键词推广,电玩网站开发,最近七天的新闻重点一、简介 GNN#xff08;Graph Neural Network#xff09;和GCN#xff08;Graph Convolutional Network#xff09;都是基于图结构的神经网络模型。本文目标就是打代码基础#xff0c;未用PyG#xff0c;来扒一扒Graph Net两个基础算法的原理。直接上代码。
二、代码
…一、简介 GNNGraph Neural Network和GCNGraph Convolutional Network都是基于图结构的神经网络模型。本文目标就是打代码基础未用PyG来扒一扒Graph Net两个基础算法的原理。直接上代码。
二、代码
import time
import random
import os
import numpy as np
import math
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Moduleimport torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optimimport scipy.sparse as sp#配置项
class configs():def __init__(self):# Dataself.data_path rE:\code\Graph\dataself.save_model_dir r\code\Graphself.model_name rGCN #GNN/GCNself.seed 2023self.device torch.device(cuda if torch.cuda.is_available() else cpu)self.batch_size 64self.epoch 200self.in_features 1433 #core ~ feature:1433self.hidden_features 16 # 隐层数量self.output_features 8 # core~paper-point~ 8类self.learning_rate 0.01self.dropout 0.5self.istrain Trueself.istest Truecfg configs()def seed_everything(seed2023):random.seed(seed)os.environ[PYTHONHASHSEED]str(seed)np.random.seed(seed)torch.manual_seed(seed)seed_everything(seed cfg.seed)#数据
class Graph_Data_Loader():def __init__(self):self.adj, self.features, self.labels, self.idx_train, self.idx_val, self.idx_test self.load_data()self.adj self.adj.to(cfg.device)self.features self.features.to(cfg.device)self.labels self.labels.to(cfg.device)self.idx_train self.idx_train.to(cfg.device)self.idx_val self.idx_val.to(cfg.device)self.idx_test self.idx_test.to(cfg.device)def load_data(self,pathcfg.data_path, datasetcora):Load citation network dataset (cora only for now)print(Loading {} dataset....format(dataset))idx_features_labels np.genfromtxt(os.path.join(path,dataset,dataset.content),dtypenp.dtype(str))features sp.csr_matrix(idx_features_labels[:, 1:-1], dtypenp.float32)labels self.encode_onehot(idx_features_labels[:, -1])# build graphidx np.array(idx_features_labels[:, 0], dtypenp.int32)idx_map {j: i for i, j in enumerate(idx)}edges_unordered np.genfromtxt(os.path.join(path,dataset,dataset.cites),dtypenp.int32)edges np.array(list(map(idx_map.get, edges_unordered.flatten())),dtypenp.int32).reshape(edges_unordered.shape)adj sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape(labels.shape[0], labels.shape[0]),dtypenp.float32)# build symmetric adjacency matrixadj adj adj.T.multiply(adj.T adj) - adj.multiply(adj.T adj)features self.normalize(features)adj self.normalize(adj sp.eye(adj.shape[0]))idx_train range(140)idx_val range(200, 500)idx_test range(500, 1500)features torch.FloatTensor(np.array(features.todense()))labels torch.LongTensor(np.where(labels)[1])adj self.sparse_mx_to_torch_sparse_tensor(adj)idx_train torch.LongTensor(idx_train)idx_val torch.LongTensor(idx_val)idx_test torch.LongTensor(idx_test)return adj, features, labels, idx_train, idx_val, idx_testdef encode_onehot(self,labels):classes set(labels)classes_dict {c: np.identity(len(classes))[i, :] for i, c inenumerate(classes)}labels_onehot np.array(list(map(classes_dict.get, labels)),dtypenp.int32)return labels_onehotdef normalize(self,mx):Row-normalize sparse matrixrowsum np.array(mx.sum(1))r_inv np.power(rowsum, -1).flatten()r_inv[np.isinf(r_inv)] 0.r_mat_inv sp.diags(r_inv)mx r_mat_inv.dot(mx)return mxdef sparse_mx_to_torch_sparse_tensor(self,sparse_mx):Convert a scipy sparse matrix to a torch sparse tensor.sparse_mx sparse_mx.tocoo().astype(np.float32)indices torch.from_numpy(np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))values torch.from_numpy(sparse_mx.data)shape torch.Size(sparse_mx.shape)return torch.sparse.FloatTensor(indices, values, shape)#精度评价指标
def accuracy(output, labels):preds output.max(1)[1].type_as(labels)correct preds.eq(labels).double()correct correct.sum()return correct / len(labels)#模型
#01-GNN
class GNNLayer(nn.Module):def __init__(self, in_features, output_features):super(GNNLayer, self).__init__()self.linear nn.Linear(in_features, output_features)def forward(self, adj_matrix, features):hidden_features torch.matmul(adj_matrix, features) # GNN公式H A * Hhidden_features self.linear(hidden_features) # 使用线性变换hidden_features F.relu(hidden_features) # 使用ReLU作为激活函数return hidden_features
class GNN(nn.Module):def __init__(self, in_features, hidden_features, output_features, num_layers2):super(GNN, self).__init__()#输入维度in_features、隐藏层维度hidden_features、输出维度output_features、GNN的层数num_layersself.layers nn.ModuleList([GNNLayer(in_features, hidden_features) if i 0 else GNNLayer(hidden_features, hidden_features) for i inrange(num_layers)])self.output_layer nn.Linear(hidden_features, output_features)def forward(self, adj_matrix, features):hidden_features featuresfor layer in self.layers:hidden_features layer(adj_matrix, hidden_features)output self.output_layer(hidden_features)return F.log_softmax(output,dim1)#02-GCN
class GraphConvolution(Module):Simple GCN layer, similar to https://arxiv.org/abs/1609.02907def __init__(self, in_features, out_features, biasTrue):super(GraphConvolution, self).__init__()self.in_features in_featuresself.out_features out_featuresself.weight Parameter(torch.FloatTensor(in_features, out_features))if bias:self.bias Parameter(torch.FloatTensor(out_features))else:self.register_parameter(bias, None)self.reset_parameters()def reset_parameters(self):stdv 1. / math.sqrt(self.weight.size(1))self.weight.data.uniform_(-stdv, stdv)if self.bias is not None:self.bias.data.uniform_(-stdv, stdv)def forward(self, input, adj):support torch.mm(input, self.weight)output torch.spmm(adj, support)if self.bias is not None:return output self.biaselse:return outputdef __repr__(self):return self.__class__.__name__ ( \ str(self.in_features) - \ str(self.out_features) )class GCN(nn.Module):def __init__(self, in_features, hidden_features, output_features, dropoutcfg.dropout):super(GCN, self).__init__()self.gc1 GraphConvolution(in_features, hidden_features)self.gc2 GraphConvolution(hidden_features, output_features)self.dropout dropoutdef forward(self, adj_matrix, features):x F.relu(self.gc1(features, adj_matrix))x F.dropout(x, self.dropout, trainingself.training)x self.gc2(x, adj_matrix)return F.log_softmax(x, dim1)class graph_run():def train(self):t time.time()#Create Train Processingall_data Graph_Data_Loader()#创建一个模型model eval(cfg.model_name)(in_featurescfg.in_features,hidden_featurescfg.hidden_features,output_featurescfg.output_features).to(cfg.device)optimizer optim.Adam(model.parameters(),lrcfg.learning_rate, weight_decay5e-4)#Trainmodel.train()for epoch in range(cfg.epoch):optimizer.zero_grad()output model(all_data.adj, all_data.features)loss_train F.nll_loss(output[all_data.idx_train], all_data.labels[all_data.idx_train])acc_train accuracy(output[all_data.idx_train], all_data.labels[all_data.idx_train])loss_train.backward()optimizer.step()loss_val F.nll_loss(output[all_data.idx_val], all_data.labels[all_data.idx_val])acc_val accuracy(output[all_data.idx_val], all_data.labels[all_data.idx_val])print(Epoch: {:04d}.format(epoch 1),loss_train: {:.4f}.format(loss_train.item()),acc_train: {:.4f}.format(acc_train.item()),loss_val: {:.4f}.format(loss_val.item()),acc_val: {:.4f}.format(acc_val.item()),time: {:.4f}s.format(time.time() - t))torch.save(model, os.path.join(cfg.save_model_dir, latest.pth)) # 模型保存def infer(self):#Create Test Processingall_data Graph_Data_Loader()model_path os.path.join(cfg.save_model_dir, latest.pth)model torch.load(model_path, map_locationtorch.device(cfg.device))model.eval()output model(all_data.adj,all_data.features)loss_test F.nll_loss(output[all_data.idx_test], all_data.labels[all_data.idx_test])acc_test accuracy(output[all_data.idx_test], all_data.labels[all_data.idx_test])print(Test set results:,loss {:.4f}.format(loss_test.item()),accuracy {:.4f}.format(acc_test.item()))if __name__ __main__:mygraph graph_run()if cfg.istrain True:mygraph.train()if cfg.istest True:mygraph.infer()三、结果与讨论 需要从网上下载cora数据集数据组织形式如下图。 测了下Params和GFLOPs,还是比较大的,发现若作为一个Net的Block还是需要优化的哈哈~
ModelParamsGFLOPsGNN23.352K126.258M
ModelCora(/train/val/test)GNN1.0000/0.7800/0.7620GCN0.9714/0.7767/0.8290
四、展望 未来可以考虑用PyG(PyTorch Geometric)毕竟PyG实现GAT等图网络、图的数据组织、加载会更加方便。Graph Net通常用可以用于属性数据的embedding模式将属性数据可以作为一种补充特征加入Net去训练看能不能发挥效能。
