TuckER模型
class TuckER(torch.nn.Module):
def __init__(self, d, d1, d2, **kwargs):
super(TuckER, self).__init__()
self.E = torch.nn.Embedding(len(d.entities), d1, padding_idx=0)
self.R = torch.nn.Embedding(len(d.relations), d2, padding_idx=0)
self.W = torch.nn.Parameter(torch.tensor(np.random.uniform(-1, 1, (d2, d1, d1)),
dtype=torch.float, device="cuda", requires_grad=True))
self.input_dropout = torch.nn.Dropout(kwargs["input_dropout"])
self.hidden_dropout1 = torch.nn.Dropout(kwargs["hidden_dropout1"])
self.hidden_dropout2 = torch.nn.Dropout(kwargs["hidden_dropout2"])
self.loss = torch.nn.BCELoss() ###损失函数
self.bn0 = torch.nn.BatchNorm1d(d1)
self.bn1 = torch.nn.BatchNorm1d(d1)
def init(self):
xavier_normal_(self.E.weight.data)
xavier_normal_(self.R.weight.data)
def forward(self, e1_idx, r_idx):
e1 = self.E(e1_idx)
x = self.bn0(e1)
x = self.input_dropout(x)
x = x.view(-1, 1, e1.size(1))
r = self.R(r_idx)
W_mat = torch.mm(r, self.W.view(r.size(1), -1))
W_mat = W_mat.view(-1, e1.size(1), e1.size(1))
W_mat = self.hidden_dropout1(W_mat)
x = torch.bmm(x, W_mat)
x = x.view(-1, e1.size(1))
x = self.bn1(x)
x = self.hidden_dropout2(x)
x = torch.mm(x, self.E.weight.transpose(1,0))
pred = F.sigmoid(x)
return pred
self.loss = torch.nn.BCELoss()
loss = model.loss(predictions, targets) ##predictions是Sigmoid二分类
Examples::
>>> m = nn.Sigmoid()
>>> loss = nn.BCELoss()
>>> input = torch.randn(3, requires_grad=True)
>>> target = torch.empty(3).random_(2)
>>> output = loss(m(input), target)
>>> output.backward()
目标：可视化loss和标量值
pytorch可视化，安装tensorboardX和tensorflow
pip install tensorflow (服务器上已经安装了1.4.0版本)
pip install tensorboardX
tensorflow已经安装了1.4.0版本
使用tensorboardX，画出pytorch框架下的数值函数变化图
参考文章：Pytorch使用tensorboardX可视化。超详细！！！
from tensorboardX import SummaryWriter ##引用该模块
model.init()
opt = torch.optim.Adam(model.parameters(), lr=self.learning_rate)
writer = SummaryWriter('runs') ##放在优化之后
###在每个epcoh中添加这个标量
writer.add_scalar('train_loss', np.mean(losses), epoch)
###关闭
writer.close()
tensorboard --logdir runs
tensorboard --logdir runs
图片.png
