def forward(self, inputs, rel_rec, rel_send):
if torch.sum(self.adj_A != self.adj_A):
print('nan error \n')
adj_A1 = torch.sinh(3.*self.adj_A)
# adj_A = I-A^T, adj_A_inv = (I-A^T)^(-1)
adj_A = preprocess_adj_new((adj_A1))
adj_A_inv = preprocess_adj_new1((adj_A1))
meanF = torch.matmul(adj_A_inv, torch.mean(torch.matmul(adj_A, inputs), 0))
logits = torch.matmul(adj_A, inputs-meanF)
return inputs-meanF, logits, adj_A1, adj_A, self.z, self.z_positive, self.adj_A
def forward(self, inputs, rel_rec, rel_send):
if torch.sum(self.adj_A != self.adj_A):
print('nan error \n')
# to amplify the value of A and accelerate convergence.
adj_A1 = torch.sinh(3. * self.adj_A)
# adj_Aforz = I-A^T
adj_Aforz = preprocess_adj_new(adj_A1)
adj_A = torch.eye(adj_A1.size()[0]).double()
H1 = F.relu((self.fc1(inputs)))
x = (self.fc2(H1))
logits = torch.matmul(adj_Aforz, x + self.Wa) - self.Wa
return x, logits, adj_A1, adj_A, self.z, self.z_positive, self.adj_A, self.Wa
def forward(self, inputs, rel_rec, rel_send):
if torch.sum(self.adj_A != self.adj_A):
print('nan error \n')
adj_A1 = torch.sinh(3. * self.adj_A)
adj_Aforz = preprocess_adj_new(adj_A1)
adj_A = torch.eye(adj_A1.size()[0]).double()
bninput = self.embed(inputs.long().view(-1, inputs.size(2)))
bninput = bninput.view(*inputs.size(), -1).squeeze()
H1 = F.relu((self.fc1(bninput)))
x = (self.fc2(H1))
logits = torch.matmul(adj_Aforz, x + self.Wa) - self.Wa
prob = my_softmax(logits, -1)
alpha = my_softmax(self.alpha, -1)
return x, prob, adj_A1, adj_A, self.z, self.z_positive, self.adj_A, self.Wa, alpha