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feature_extracter.py
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feature_extracter.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class NewGenderDiscriminator(nn.Module):
def __init__(self, embed_dim):
super(NewGenderDiscriminator, self).__init__()
self.embed_dim = int(embed_dim)
self.out_dim = 2
self.sigmoid = nn.Sigmoid()
self.criterion = nn.BCELoss()
self.net = nn.Sequential(
nn.BatchNorm1d(num_features=self.embed_dim),
nn.Linear(self.embed_dim, int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 4),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.3),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 4), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.3),
# nn.Linear(int(self.embed_dim * 4), int(self.embed_dim * 4), bias=True),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=0.3),
nn.Linear(int(self.embed_dim * 4), int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.3),
# nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 2), bias=True),
# # nn.BatchNorm1d(num_features=self.embed_dim),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=0.3),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.3),
# nn.Linear(int(self.embed_dim), int(self.embed_dim), bias=True),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=0.3),
nn.Linear(int(self.embed_dim), int(self.embed_dim / 2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=0.3),
nn.Linear(int(self.embed_dim / 2), self.out_dim, bias=True)
)
def forward(self, ents_emb):
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
# loss = self.criterion(output.squeeze(), A_labels)
return output
def predict(self, ents_emb):
with torch.no_grad():
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
preds = output.max(1, keepdim=True)[1] # get the index of the max
return output.squeeze(), preds
def save(self, fn):
torch.save(self.state_dict(), fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))
class AgeDiscriminator(nn.Module):
def __init__(self, embed_dim):
super(AgeDiscriminator, self).__init__()
self.embed_dim = int(embed_dim)
self.criterion = nn.NLLLoss()
self.out_dim = 7
self.drop = 0.5
self.net = nn.Sequential(
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.Linear(self.embed_dim, int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 4), bias=True),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 4), int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim), int(self.embed_dim / 2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim / 2), self.out_dim, bias=True)
)
def forward(self, ents_emb):
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
# loss = self.criterion(output.squeeze(), A_labels)
return output
def predict(self, ents_emb):
with torch.no_grad():
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
preds = output.max(1, keepdim=True)[1] # get the index of the max
return output.squeeze(), preds
def save(self, fn):
torch.save(self.state_dict(), fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))
def create_kl_divergence(self, u_g_embeddings):
predict = self.forward(u_g_embeddings)
target = (torch.ones(predict.shape) / predict.shape[1]).to(u_g_embeddings.device)
loss = F.kl_div(predict, target, reduction='batchmean')
return loss
class OccupationDiscriminator(nn.Module):
def __init__(self, embed_dim):
super(OccupationDiscriminator, self).__init__()
self.embed_dim = int(embed_dim)
self.criterion = nn.NLLLoss()
self.out_dim = 21
self.drop = 0.3
self.net = nn.Sequential(
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.Linear(self.embed_dim, int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim*4),bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 4), int(self.embed_dim*2),bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim*2), int(self.embed_dim*2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim*2), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim), int(self.embed_dim/2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim / 2), self.out_dim,bias=True)
)
def forward(self, ents_emb):
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
# loss = self.criterion(output.squeeze(), A_labels)
return output
def predict(self, ents_emb):
with torch.no_grad():
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
preds = output.max(1, keepdim=True)[1] # get the index of the max
return output.squeeze(), preds
def save(self, fn):
torch.save(self.state_dict(), fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))
def create_kl_divergence(self, u_g_embeddings):
predict = self.forward(u_g_embeddings)
target = (torch.ones(predict.shape) / predict.shape[1]).to(u_g_embeddings.device)
loss = F.kl_div(predict, target, reduction='batchmean')
return loss
class GenderDiscriminator(nn.Module):
def __init__(self, embed_dim):
super(GenderDiscriminator, self).__init__()
self.embed_dim = int(embed_dim)
self.out_dim = 1
self.sigmoid = nn.Sigmoid()
self.criterion = nn.BCELoss()
self.drop = 0.5
self.net = nn.Sequential(
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.Linear(self.embed_dim, int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 4), bias=True),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
# nn.Linear(int(self.embed_dim * 4), int(self.embed_dim * 4), bias=True),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 4), int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
# nn.Linear(int(self.embed_dim * 2), int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
# nn.Linear(int(self.embed_dim), int(self.embed_dim), bias=True),
# nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim), int(self.embed_dim / 2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim / 2), self.out_dim, bias=True)
)
def forward(self, ents_emb):
scores = self.net(ents_emb)
output = torch.sigmoid(scores)
output = output.clamp(1e-5, 1 - 1e-5)
# loss = self.criterion(output.squeeze(), A_labels)
return output
def predict(self, ents_emb):
with torch.no_grad():
scores = self.net(ents_emb)
output = torch.sigmoid(scores)
preds = (scores > torch.Tensor([0.5]).cuda()).float() * 1 # get the index of the max
return output.squeeze(), preds
def save(self, fn):
torch.save(self.state_dict(), fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))
def create_kl_divergence(self, u_g_embeddings):
predict = self.forward(u_g_embeddings)
predict = torch.hstack((predict, 1 - predict))
target = (torch.ones(predict.shape) / predict.shape[1]).to(u_g_embeddings.device)
predict = torch.log(predict)
loss = F.kl_div(predict, target, reduction='batchmean')
return loss
class LocationDiscriminator(nn.Module):
def __init__(self, embed_dim):
super(LocationDiscriminator, self).__init__()
self.embed_dim = int(embed_dim)
self.out_dim = 453
self.criterion = nn.NLLLoss()
self.drop = 0.5
self.net = nn.Sequential(
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.Linear(self.embed_dim, int(self.embed_dim * 2), bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 2), int(self.embed_dim*4),bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim * 2),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim * 4), int(self.embed_dim*2),bias=True),
# nn.BatchNorm1d(num_features=self.embed_dim),
nn.LeakyReLU(0.2),
# nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim*2), int(self.embed_dim*2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim*2), int(self.embed_dim), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim), int(self.embed_dim/2), bias=True),
nn.LeakyReLU(0.2),
nn.Dropout(p=self.drop),
nn.Linear(int(self.embed_dim / 2), self.out_dim,bias=True)
)
def forward(self, ents_emb):
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
# loss = self.criterion(output.squeeze(), A_labels)
return output
def predict(self, ents_emb):
with torch.no_grad():
scores = self.net(ents_emb)
output = F.log_softmax(scores, dim=1)
preds = output.max(1, keepdim=True)[1] # get the index of the max
return output.squeeze(), preds
def save(self, fn):
torch.save(self.state_dict(), fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))
def create_kl_divergence(self, u_g_embeddings):
predict = self.forward(u_g_embeddings)
target = (torch.ones(predict.shape) / predict.shape[1]).to(u_g_embeddings.device)
loss = F.kl_div(predict, target, reduction='batchmean')
return loss
if __name__ == '__main__':
pass