Comments (1)
您好,十分抱歉一直没注意到issue。
因为在beam search的过程中能够,一些beam候选可能会提前生成结束(生成了句尾符),然后被移除beam,此时剩余的beam数据改变,因此对应的states, 如hidden, memory等的batch size也需要相应变化,所以重新做了索引,选取了剩余的部分。此时只需要各种状态中统一索引下标属于统一beam candidate即可。不同beam candidate之间的相互位置有变化不影响结果,因为各个beam candidate是独立的。
之前我们升级了代码的python版本和tensorflow版本,一些细节也进行了优化,如有问题欢迎提出。
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