PyTorch搭建图卷积神经网络（GCN）完成对论文分类及预测实战（附源码和数据集）

需要数据集和源码请点赞关注收藏后评论区留言~~~

一、数据集简介

我们将使用Cora数据集。

该数据集共2708个样本点，每个样本点都是一篇科学论文，所有样本点被分为7个类别，类别分别是1）基于案例；2）遗传算法；3）神经网络；4）概率方法；5）强化学习；6）规则学习；7）理论

每篇论文都由一个1433维的词向量表示，所以，每个样本点具有1433个特征。词向量的每个元素都对应一个词，且该元素只有0或1两种取值。取0表示该元素对应的词不在论文中，取1表示在论文中。所有的词来源于一个具有1433个词的字典。

每篇论文都至少引用了一篇其他论文，或者被其他论文引用，也就是样本点之间存在联系，没有任何一个样本点与其他样本点完全没联系。如果将样本点看作图中的点，则这是一个连通的图，不存在孤立点。

数据集主要文件有两个：cora.cites, cora.content。其中，cora.content包含了2708个样本的具体信息，每行代表一个论文样本，格式为

<论文id>  <由01组成的1433维特征>  <论文类别（label）>

总的来说，如果将论文当作“图”的节点，则引用关系则为“图”的边，论文节点信息和引用关系共同构成了图数据。本次实验，我们将利用这些信息，对论文所属的类别进行预测，完成关于论文类别的分类任务。

二、图神经网络与图卷积神经网络简介

 图神经网络（Graph Neural Networks, GNN）作为新的人工智能学习模型，可以将实际问题看作图数据中节点之间的连接和消息传播问题，对节点之间的依赖关系进行建模，挖掘传统神经网络无法分析的非欧几里得空间数据的潜在信息。在自然语言处理、计算机视觉、生物化学等领域中，图神经网络得到广泛的应用，并发挥着重要作用。

图卷积神经网络(Graph Convolutional Networks, GCN)是目前主流的图神经网络分支，分类任务则是机器学习中的常见任务。我们将利用GCN算法完成分类任务，进一步体会理解图神经网络工作的原理、GCN的构建实现过程，以及如何将GCN应用于分类任务。

三、运行效果

如下图 可见随着训练次数的增加，损失率在下降，精确度在上升，大概在200次左右收敛。

 

 

 

 四、部分源码

主测试类代码如下

from __future__ import division
from __future__ import print_function
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import time
import argparse
import numpy as np
from torch.utils.data import  DataLoader
import torch
import torch.nn.functional as F
import torch.optim as optim

from utils import load_data, accuracy
from models import GCN
import matplotlib.pyplot as plt

# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='Disables CUDA training.')
parser.add_argument('--fastmode', action='store_true', default=False,
                    help='Validate during training pass.')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--epochs', type=int, default=300,
                    help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
                    help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=5e-4,
                    help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=16,
                    help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.5,
                    help='Dropout rate (1 - keep probability).')

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

.manual_seed(args.seed)

# Load data
adj, features, labels, idx_train, idx_val, idx_test = load_data()

# Model and optimizer
model = GCN(nfeat=features.shape[1],
            nhid=args.hidden,
            nclass=labels.max().item() + 1,
            dropout=args.dropout)
optimizer = optim.Adam(model.parameters(),
                       lr=args.lr, weight_decay=args.weight_decay)

if args.cuda:
    model.cuda()
    features = features.cuda()
    adj = adj.cuda()
    labels = labels.cuda()
    idx_train = idx_train.cuda()
    idx_val = idx_val.cuda()
    idx_test = idx_test.cuda()
Loss_list = []


accval=[]

def train(epoch):
       t=time.time()
       model.train()
       optimizer.zero_grad()
       output=model(features,adj)
       loss_train=F.nll_loss(output[idx_train],labels[idx_train])
       acc_train=accuracy(output[idx_train],labels[idx_train])
       loss_train.backward()
       optimizer.step()

       if not args.fastmode:
           model.eval()
           output=model(features,adj)
       loss_val=F.nll_loss(output[idx_val],labels[idx_val])
       acc_val=accuracy(output[idx_val],labels[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))
       Loss_list.append(loss_train.item())
       Accuracy_list.append(acc_train.item())
       lossval.append(loss_val.item())
       accval.append(acc_val.item())











def test():
    model.eval()
    output = model(features, adj)
    loss_test = F.nll_loss(output[idx_test], labels[idx_test])
    acc_test = accuracy(output[idx_test], labels[idx_test])
    print("Test set results:",
          "loss= {:.4f}".format(loss_test.item()),
          "accuracy= {:.4f}".format(acc_test.item()))
    acc=acc_test.detach().numpy()
    loss=loss_test.detach().numpy()

    print(type(loss_test))
    print(type(acc_test))


    # 定义两个数组


# Train model
t_total = time.time()

for epoch in range(args.epochs):
    train(epoch)





print("Optimization Finished!")
printal time elapsed: {:.4f}s".format(time.time() - t_total))
'''
plt.plot([i for i in range(len(Loss_list))],Loss_list)
pplot([i for i in range(len(Accuracy_list))],Accuracy_list)
'''
plt.plot([i for i in range(len(lossval))],lossval)
plot([i for i in range(len(accval))],accval)
print(type(Loss_list))
print(type(Accuracy_list))
#plt.plot([i for i in range(len(Accuracy_list),Accuracy_list)])
plt.show()
# Testing

test()

模型类如下

import torch.nn as nn
import torch.nn.functional as F
from layers import GraphConvolution


class GCN(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN, self).__init__()

        self.gc1 = GraphConvolution(nfeat, nhid)
    on(nhid, nclass)
        self.dropout = dropout

    def forward(self, x, adj):
        x=F.relu(self.gc1(x,adj))
        x=F.dropout(x,self.dropout,training=self.training)
        x=self.gc2(x,adj)
        return F.log_softmax(x,dim=1)

layer类如下

import math

import torch

from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features=in_features
        self.out_features=out_features
        self.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.bias
         else:
             return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + str(self.in_features) + ' -> ' \
               + str(self.out_features) + ')'

util类如下

import numpy as np
import scipy.sparse as sp
import torch


def encode_onehot(labels):
    classes = set(labels)
    classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
                    enumerate(classes)}
    labels_onehot = np.array(list(map(classes_dict.get, labels)),
                             dtype=np.int32)
    return labels_onehot


def load_data(path="data/cora/", dataset="cora"):
    """Load citation network dataset (cora only for now)"""
    print('Loading {} dataset...'.format(dataset))

    idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
                                        dtype=np.dtype(str))
    features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
    labels = encode_onehot(idx_features_labels[:, -1])

    # build graph
    idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
    idx_map = {j: i for i, j in enumerate(idx)}
    edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset),
                                    dtype=np.int32)
    edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
                     dtype=np.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]),
                        dtype=np.float32)

    # build symmetric adjacency matrix
    adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)

    features = normalize(features)
    adj = 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 = 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_test


def normalize(mx):
    """Row-normalize sparse matrix"""
    rowsum = 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 mx




de_to_torch_sparse_tensor(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)

创作不易 觉得有帮助请点赞关注收藏~~~