scorer.py 文件源码

def score_batch(self, e1, e2, r_index):
        """
        :param e1: (batch, entity_dim, )
        :param e2: (batch, entity_dim, )
        :param r_index: (batch, )
        :return: 
        """
        # (batch, entity_dim) dot (batch, entity_dim, entity_dim, hidden) dot (batch, entity_dim) -> hidden * (batch, )
        hidden1_sep, _ = theano.scan(fn=self.step_batch,
                                     sequences=[self.slice_seq],
                                     non_sequences=[e1, e2, self.W[r_index]],
                                     name='batch_scan')
        # hidden * (batch, ) -> (batch, hidden)
        hidden1 = T.concatenate([hidden1_sep], axis=1).transpose()
        if self.keep_normal:
            # (batch, 2 * entity_dim) dot (batch, 2 * entity_dim, hidden) -> (batch, hidden, )
            hidden2 = T.batched_dot(T.concatenate([e1, e2], axis=1), self.V[r_index])
            # (batch, hidden) + (batch, hidden) + (batch, hidden) -> (batch, hidden)
            hidden = hidden1 + hidden2 + self.b[r_index]
        else:
            hidden = hidden1
        # (batch, hidden) -> (batch, hidden)
        act_hidden = self.act.activate(hidden)
        # (batch, hidden) dot (batch, hidden) -> (batch, )
        return T.sum(act_hidden * self.U[r_index], axis=1)