LSTM: How to feed the output back to the input?

model = Sequential() model.add(LSTM(512, input_dim = 4, return_sequences = True)) model.add(TimeDistributed(Dense(4))) model.add(Activation('softmax'))

The input here is the one hot representation of a string and the dictionary size is set to be 4. In other word, there are four types of chars in this string. The output here is the probabilities that the next char ought to be.

If the length of input sequence is 1, the output dimension is 4 by 1. I just wonder could I feed the output back to the input and get an arbitrary length of output sequence (illustrated as follows). It may not be reasonable to plug back the probabilities but I just want to know the possibility to implement this one-to-many structure in keras. Thanks.

Example:

input1 -(LSTM)-> output1 output1 -(LSTM) -> output2 output2 - (LSTM) -> output3

We could get a 4 by 3 output in the end.

[wgmao]

I referred to https://github.com/LantaoYu/SeqGAN/blob/e2b52fb6309851b14765290e8a972ccac09f1bec/target_lstm.py to write customized recurrent layers.

[EderSantana]

actually, I think he will have to write his own custom layer to do that. See this DreamyRNN for example: https://github.com/commaai/research/blob/master/models/layers.py#L334-L397 It takes a n frames and input and outputs n+m where the last m frames are generated by feeding outputs back as input.