Keras – stateful vs stateless LSTMs


I’m having a hard time conceptualizing the difference between stateful and stateless LSTMs in Keras. My understanding is that at the end of each batch, the “state of the network is reset” in the stateless case, whereas for the stateful case, the state of the network is preserved for each batch, and must then be manually reset at the end of each epoch.

My questions are as follows:
1. In the stateless case, how is the network learning if the state isn’t preserved in-between batches?
2. When would one use the stateless vs stateful modes of an LSTM?


I recommend you to firstly learn the concepts of BPTT (Back Propagation Through Time) and mini-batch SGD(Stochastic Gradient Descent), then you’ll have further understandings of LSTM’s training procedure.

For your questions,

Q1. In stateless cases, LSTM updates parameters on batch1 and then, initiate hidden states and cell states (usually all zeros) for batch2, while in stateful cases, it uses batch1’s last output hidden states and cell sates as initial states for batch2.

Q2. As you can see above, when two sequences in two batches have connections (e.g. prices of one stock), you’d better use stateful mode, else (e.g. one sequence represents a complete sentence) you should use stateless mode.

BTW, @vu.pham said if we use stateful RNN, then in production, the network is forced to deal with infinite long sequences. This seems not correct, actually, as you can see in Q1, LSTM WON’T learn on the whole sequence, it first learns sequence in batch1, updates parameters, and then learn sequence on batch2.

Answered By – zodiac

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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