使用双向包装器时，如何在LSTM层中获得最终的隐藏状态和序列

问题描述

我已按照 https:/中的步骤进行操作/machinelearningmastery.com/return-sequences-and-return-states-for-lstms-in-keras/但是当涉及到双向lstm时，我尝试过

i have followed the steps in https://machinelearningmastery.com/return-sequences-and-return-states-for-lstms-in-keras/But when it comes to the Bidirectional lstm, i tried this

lstm, state_h, state_c = Bidirectional(LSTM(128, return_sequences=True, return_state= True))(input)

但是它不起作用.

有什么方法可以在使用双向包装器时在LSTM层中同时获取最终的隐藏状态和序列

is there some approach to get both the final hidden state and sequence in a LSTM layer when using a bidirectional wrapper

推荐答案

调用Bidirectional(LSTM(128, return_sequences=True, return_state=True))(input)返回5个张量:

1. 整个隐藏状态序列，默认情况下将是向前和向后状态的串联.
2. 前向LSTM的最后一个隐藏状态h
3. 正向LSTM的最后一个单元状态c
4. 向后LSTM的最后一个隐藏状态h
5. 向后LSTM的最后一个单元状态c

1. The entire sequence of hidden states, by default it'll be the concatenation of forward and backward states.
2. The last hidden state h for the forward LSTM
3. The last cell state c for the forward LSTM
4. The last hidden state h for the backward LSTM
5. The last cell state c for the backward LSTM

您要发布的行会引发错误，因为您希望将返回的值仅解压缩为三个变量(lstm, state_h, state_c).

The line you've posted would raise an error since you want to unpack the returned value into just three variables (lstm, state_h, state_c).

要更正它，只需将返回值解压缩为5个变量.如果要合并状态，可以将前向和后向状态与Concatenate层连接起来.

To correct it, simply unpack the returned value into 5 variables. If you want to merge the states, you can concatenate the forward and backward states with Concatenate layers.

lstm, forward_h, forward_c, backward_h, backward_c = Bidirectional(LSTM(128, return_sequences=True, return_state=True))(input)
state_h = Concatenate()([forward_h, backward_h])
state_c = Concatenate()([forward_c, backward_c])

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