TuckER模型|TuckER模型 pytorch损失函数 TuckER模型pytorch损失函数

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

【TuckER模型|TuckER模型 pytorch损失函数】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

文章图片
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()