model.py 文件源码

def _algo_1_horiz_comp(self, sent1_block_a, sent2_block_a):
        comparison_feats = []
        for pool in ('max', 'min', 'mean'):
            for ws in self.filter_widths:
                x1 = sent1_block_a[ws][pool]
                x2 = sent2_block_a[ws][pool]
                batch_size = x1.size()[0]
                comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
                comparison_feats.append(F.pairwise_distance(x1, x2))
        return torch.cat(comparison_feats, dim=1)