Comments (3)
关于第一个词和最后一个词的选取,这是和双向的RNN有关的,选取双向RNN的两个方向的最后一个隐向量来作为边的表示。
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在our_models.py的107行中,您定义# rnn_output shape: batch * MAX_EDGES_PER_GRAPH, max_sent_len, hidden 我设置的是batch_size = 10 所以size = (720, 36, 512) 108行代码 rnn_result = torch.cat([rnn_output[:, -1, :self.p['units1']], rnn_output[:, 0, self.p['units1']:]], dim=1).view(sentence_input.size()[0], MAX_EDGES_PER_GRAPH, -1) 我的理解是: 在108行中, 相当于截取lstm中第35个word的前0到255的embedding,和第0个word的第256到512的embedding。经过tocrh,cat变成维度(720,2,256)最后再view一下变成(10, 72,512 ),view相当于把同一个sentence中的第一个word和最后1个word的各自256维度拼接。这个第一个词后半embedding后最后一个词的前半embedding的拼接。作为graph edge的value 我的问题:如果我理解的没有错,请问这样做有什么依据吗?它对应了论文中哪一条公式?
您好,请问您有实现P@K这个指标吗?我并没有找到这个指标的代码
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Related Issues (2)
- P@K HOT 3
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