GAN（图卷积网络）的Pytorch实现

﹏ヽ暗。殇╰゛Y 2023-03-03 12:28 10阅读 0赞

import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    import networkx as nx
    
    
    def normalize(A , symmetric=True):
    	# A = A+I
    	A = A + torch.eye(A.size(0))
    	# 所有节点的度
    	d = A.sum(1)
    	if symmetric:
    		#D = D^-1/2
    		D = torch.diag(torch.pow(d , -0.5))
    		return D.mm(A).mm(D)
    	else :
    		# D=D^-1
    		D =torch.diag(torch.pow(d,-1))
    		return D.mm(A)
    
    
    class GCN(nn.Module):
    	'''
    	Z = AXW
    	'''
    	def __init__(self , A, dim_in , dim_out):
    		super(GCN,self).__init__()
    		self.A = A
    		self.fc1 = nn.Linear(dim_in ,dim_in,bias=False)
    		self.fc2 = nn.Linear(dim_in,dim_in//2,bias=False)
    		self.fc3 = nn.Linear(dim_in//2,dim_out,bias=False)
    
    	def forward(self,X):
    		'''
    		计算三层gcn
    		'''
    		X = F.relu(self.fc1(self.A.mm(X)))
    		X = F.relu(self.fc2(self.A.mm(X)))
    		return self.fc3(self.A.mm(X))
    
    #获得空手道俱乐部数据
    G = nx.karate_club_graph()
    A = nx.adjacency_matrix(G).todense()
    #A需要正规化
    A_normed = normalize(torch.FloatTensor(A),True)
    
    N = len(A)
    X_dim = N
    
    # 没有节点的特征，简单用一个单位矩阵表示所有节点
    X = torch.eye(N,X_dim)
    # 正确结果
    Y = torch.zeros(N,1).long()
    # 计算loss的时候要去掉没有标记的样本
    Y_mask = torch.zeros(N,1,dtype=torch.uint8)
    # 一个分类给一个样本
    Y[0][0]=0
    Y[N-1][0]=1
    #有样本的地方设置为1
    Y_mask[0][0]=1
    Y_mask[N-1][0]=1
    
    #真实的空手道俱乐部的分类数据
    Real = torch.zeros(34 , dtype=torch.long)
    for i in [1,2,3,4,5,6,7,8,11,12,13,14,17,18,20,22] :
    	Real[i-1] = 0
    for i in [9,10,15,16,19,21,23,24,25,26,27,28,29,30,31,32,33,34] :
    	Real[i-1] = 1
    
    # 我们的GCN模型
    gcn = GCN(A_normed ,X_dim,2)
    #选择adam优化器
    gd = torch.optim.Adam(gcn.parameters())
    
    for i in range(300):
    	#转换到概率空间
    	y_pred =F.softmax(gcn(X),dim=1)
    	#下面两行计算cross entropy
    	loss = (-y_pred.log().gather(1,Y.view(-1,1)))
    	#仅保留有标记的样本
    	loss = loss.masked_select(Y_mask).mean()
    
    	#梯度下降
    	#清空前面的导数缓存
    	gd.zero_grad()
    	#求导
    	loss.backward()
    	#一步更新
    	gd.step()
    
    	if i%20==0 :
    		_,mi = y_pred.max(1)
    		print(mi)
    		#计算精确度
    		print((mi == Real).float().mean())

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]: /images/20230208/4f71705ef0ba4520b91195eb30d68fd1.png