the extension of https://github.com/philipperemy/keras-attention-mechanism , create a new scipt to add attetion to input dimensions rather than timesteps in the origin project。
Home Page: https://blog.csdn.net/qq_35649669/article/details/103807915
License: Apache License 2.0
老师好，我是一名研究生三年级的在校生，最近有幸阅读到您在github上面关于LSTM和注意力机制的文章（https://github.com/PatientEz/keras-attention-mechanism#dense-layer）。有个疑问想向您请教。主要是关于LSTM后面使用注意力机制后，要观察如何抓到维度的注意力机制的问题，我举个例子：
有个数据维度是（1000,20,30） 20个时间步，30个维度的。
经过LSTM层后，假设设置的隐藏层数是32，那么经过LSTM，维度就会变成（20,32）了，这个时候再输入您的注意力机制层，提取出来的维度注意力概率的就会变成32维，而不再是输入时候的30维了，请问遇到这种现象怎么处理呢？
盼望得到您的指导和解答，不胜感激！
张书韵（[email protected]）
2020年12月14日
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